Energy Analysis 2005
Republic of Lebanon Ministry of Public Works and Transport General Directorate of Urban Planning
ENERGY ANALYSIS AND ECONOMIC FEASIBILITY STUDY Development of viable solutions for the Thermal Standard for Buildings in Lebanon
Copyright © UNDP/GEF and MPWT/DGU – 2005 Reproduction is authorized provided the source is acknowledged and provided the reproduction is not sold. UNDP is the UN’s global development network, advocating for change and connecting countries to knowledge, experience and resources to help build a better life. We are on the ground in 166 countries, working with them on their own solutions to global and national development challenges. As they develop local capacity, they draw on the people of UNDP and our wide range of partners. For further information: United Nations Development Programme, www.undp.org.lb General Directorate of Urban Planning, www.public-works.gov.lb Note: The information contained within this document has been developed within a specific scope, and may be updated in the future.
Energy Analysis and Economic Feasibility Study
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:: PREFACE This study has been developed in the context of Project “Capacity Building for the adoption and application of Thermal Standards for Buildings”. The project was funded by the Global Environment Facility, Managed by the United Nations Development Programme, and Executed under the Lebanese General Directorate of Urban Planning, Ministry of Public Works and Transport. The project falls under the Climate Change focal area and aims at the establishment of Thermal Standards for Buildings, and at enabling their adoption and application through the provision of capacity building and information dissemination.
:: ACKNOWLEDGEMENTS This study is the result of a collaborative effort between national inputs and international expertise. The project wishes to thank all individuals and institutions who supported and contributed to this study. Particular acknowledgements to: The General Directorate of Urban Planning The Directorate of Meteorological Services The Order of Engineers and Architects, Beirut
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:: TABLE OF CONTENTS Preface and Acknowledgements List of figures List of Tables Executive Summary
ii iv v vi
1
INTRODUCTION 1.1 Methodology of the Energy Analysis 1.2 Methodology of the Economic Analysis
2
ENERGY ANALYSIS 2.1 Parametric Variations of Roof U-value 2.2 Parametric Variations of Wall U-value 2.3 Parametric Variations of Window U-value and Shading Coefficient 2.4 Parametric Variations of Window to Wall Ratio 2.5 Parametric Variations of Window Orientation and Architectural Shading 2.6 Recommended Architectural Shading Factors 2.7 Selected Combined Simulations for Walls, Roofs and Windows
9 9 15 19 19 32 36 38
3
ECONOMIC ANALYSIS 3.1 Economic Results of Roof U-value 3.2 Economic Results of Wall U-value 3.3 Economic Results of Window U-value and Shading Coefficient 3.4 Economic Results of Architectural Shading (Fins and Overhangs) 3.5 Economic Results of Selected Combined Measures 3.6 Sensitivity Analysis
40 40 41 42 44 47 49
4
RECOMMENDATIONS FOR THE THERMAL STANDARD 4.1 Maximum U-values for Roofs and Walls 4.2 Maximum U-values for Windows 4.3 Maximum Effective Fenestration Ratios
50 50 51 51
5
IMPACT ASSESSMENT 5.1 Projected Economic Growth rate 5.2 Projected Population Growth rate 5.3 Projected Building Growth 5.4 Projected Reduction in Energy Consumption 5.5 Summary of the Energy Savings Results
58 58 58 60 62 60
ANNEX 1 Description of the Residential Building used as Model ANNEX 2 Description of the Office Building used as Model ANNEX 3 Hypothesis for Energy Price Forecast ANNEX 4 Methodology of Economic Analysis ANNEX 5 Methodology of energy and Discount Rate variations ANNEX 6 Incremental Construction Costs ANNEX 7 Avoided costs on HVAC systems
66 69 73 78 81 82 85
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:: LIST OF FIGURES Figure 1 Total Energy Usage vs. Roof U factor – Office Buildings
11
Figure 2 Cooling Energy Usage vs. Roof U factor – Office Buildings
11
Figure 3 Heating Energy Usage vs. Roof U factor – Office Buildings
12
Figure 4 Effect of the roof insulation – Cedars
12
Figure 5 Energy Usage vs. Roof U factor – Residential Buildings
13
Figure 6 Cooling Energy Usage vs. Roof U factor – Residential Buildings
14
Figure 7 Heating Energy Usage vs. Roof U factor – Residential Buildings
14
Figure 8 Total Energy Usage vs. Wall U factor - Office Buildings
16
Figure 9 Heating Energy Usage vs. Wall U factor - Office Buildings
16
Figure 10 Cooling Energy Usage vs. Wall U factor - Office Buildings
17
Figure 11 Energy Usage vs. Wall U factor - Residential Buildings
18
Figure 12 Cooling Energy Usage vs. Wall U factor - Residential Buildings
18
Figure 13 Heating Energy Usage vs. Wall U factor - Residential Buildings
19
Figure 14 Total energy usage with different Glazing - Office
20
Figure 15 Heating energy usage with different Glazings – Office
21
Figure 16 Cooling energy usage with different Glazings – Office
21
Figure 17 Single Glass Glazing Results - 3 Cities – Office
22
Figure 18 Double Glazing Analysis for 3 Cities – Office
23
Figure 19 Total Energy Usage With Different Glazings – Residential
23
Figure 20 Heating energy usage with different Glazings - Residential
24
Figure 21 Cooling energy usage with different Glazings - Residential
24
Figure 22 Single Glazing Analysis for 3 Cities - Residential
25
Figure 23 Double Glazing Analysis for 3 Cities - Residential
25
Figure 24 Variation of the Energy Usage with Increasing WWR – Office
27
Figure 25 Variation of Heating energy usage with increasing WWR - Office
28
Figure 26 Variation of Cooling energy usage with increasing WWR - Office
28
Figure 27 Recommended Threshold to trigger the Performance Path - Office
29
Figure 28 Variation of the Total Energy Usage with Increasing WWR - Residential
30
Figure 29 Variation of the Heating Energy Usage with Increasing WWR - Residential
30
Figure 30 Variation of the Cooling Energy Usage with Increasing WWR - Residential
31
Figure 31 Threshold Recommended to trigger the Performance Path - Residential
31
Figure 32 Overhang Projection Factor Parameters
33
Figure 33 Architectural Shading Factor for Various Overhang Projections
33
Figure 34 Fins Projection Factor Parameters
34
Figure 35 Fins Effect on the Architectural Shading Factor
34
Figure 36 Combined Effects of Fins and Overhang
35
Figure 37 Combined Measure Results in Office Buildings
38
Figure 38 Combined Measure Results Residential Buildings
38
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:: LIST OF TABLES Table 1 Outdoor Design Conditions for Lebanon
2
Table 2 Breakdown of Existing Building Units according to use
2
Table 3 Average number of Floors per Building Permit
3
Table 4 Breakdown of Building Permits according to use use
4
Table 5 Base Case Roof and Wall Compositions of Residential and Office Buildings
5
Table 6 Crude Oil 20 Year Forecast Assumptions
6
Table 7 Assumed Diesel Oil Prices
6
Table 8 Cost of Electricity in LP and US$ Per Kwh
7
Table 9 Assumed Discount Rates
7
Table 10 Presentation of the Nine Scenarios
7
Table 11 Parameters That will be Applied in the Nine Scenarios
8
Table 12 Parametric Roof U factor Alternatives
9
Table 13 Parametric Wall U factor Alternatives
15
Table 14 Parametric Window U factor and Shading Coefficient Alternatives
20
Table 15 Parametric Window to Wall Ratio Alternatives
27
Table 16 Parametric Alternatives for the Evaluation of Architectural Shading Factors
32
Table 17 Architectural Shading Factor (ASF) for Unprotected Windows
36
Table 18 Architectural Shading Factor (ASF) for Windows Protected by Overhang Only
36
Table 19 Architectural Shading Factor (ASF) for Windows Protected by Fins Only
36
Table 20 Architectural Shading Factor (ASF) for Windows Protected by Fins and Overhang
36
Table 21 Combined Simulation Parameters
37
Table 22 Optimum Insulation Level for Roof (with additional cooling) – Office Building
39
Table 23 Optimum Insulation Level for Roof (without additional cooling) – Office Building
39
Table 24 Optimum Insulation Level for Roof (with additional cooling) – Residential Building
40
Table 25 Optimum Insulation Level for Roof (without additional cooling) – Residential Building
40
Table 26 Optimum Insulation Level for Wall (with additional cooling) – Office Building
40
Table 27 Optimum Insulation Level for Wall (without additional cooling) – Office Building
40
Table 28 Optimum Insulation Level for Wall (with additional cooling) – Residential Building
41
Table 29 Optimum Insulation Level for Wall (without additional cooling) – Residential Building
41
Table 30 Optimum Performance of Windows (with additional cooling load) – Office Building
42
Table 31 Optimum Performance of Windows (without additional cooling) – Residential Building
42
Table 32 Optimum Projection and Net Present Value – Office Building, Climatic Zone 1
43
Table 33 Optimum Projection and Net Present Value – Office Building, Climatic Zone 2
43
Table 34 Optimum Projection and Net Present Value – Office Building, Climatic Zone 3
44
Table 35 Optimum Projection and Net Present Value – Office Building, Climatic Zone 4
44
Table 36 Optimum Projection and Net Present Value – Residential Building, Climatic Zone 1
45
Table 37 Optimum Projection and Net Present Value – Residential Building, Climatic Zone 2
45
Table 38 Optimum Projection and Net Present Value – Residential Building, Climatic Zone 3
46
Table 39 Optimum Projection and Net Present Value – Residential Building, Climatic Zone 4
46
Table 40 Combination Measures Economic Analysis – Office Buildings
47
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Table 41 Combined Simulations for Residential Buildings
47
Table 42 Range in Optimum Shell Properties to Maximize NPV (for all 9 scenarios) – Office
48
Table 43 Range in Optimum Shell Properties to Maximize NPV (for all 9 scenarios) – Residential
48
Table 44 Optimum Roof and Wall Insulation levels by Climate Zone
50
Table 45 Glazing thermal transmittance requirement
50
Table 46 Variations of Buildings for the EFRreq Analysis
53
Table 47 Selected and Limit Values of SC and projection factor for overhang
54
Table 48 Case Study Analysis to Determine the WWRreq – Office Buildings
54
Table 49 Thermal Standard Requirement for WWRreq – Office Buildings
55
Table 50 Variations of Buildings for the EFRreq Analysis
55
Table 51 Selected and Limit Values of SC and and projection factor for overhang
55
Table 52 Case Study Analysis to Determine the WWRreq – Residential Buildings
56
Table 53 Thermal Standard Requirement for EFRreq – Residential Buildings
56
Table 54 Economic Growth Rate
57
Table 55 Projected Population Growth
57
Table 56 Projected Number of Households
58
Table 57 Projected Number of Residences
58
Table 58 Forecast of the Residential Building Area that will Comply with the Thermal Standard
59
Table 59 Forecast of the Office Building Area that will Comply with the Thermal Standard
60
Table 60 Office Building Annual Base Case Energy per m2 and Savings for Heating & Cooling
61
Table 61 Projected Cumulative office built-up area (2010-2029)
62
Table 62 Projected Energy Savings from Office Buildings (2010-2029)
62
Table 63 Residential Building Annual Base Case Energy per m2 and Savings for Heating & Cooling 63 Table 64 Projected cumulative residential built up area (2010-2029)
63
Table 65 Projected Energy Savings from Residential Buildings (2010-2029)
63
Table 66 Summary of the Energy Savings at Building Input
64
Table 67 projected avoided CO2 emissions (2010-2029)
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:: EXECUTIVE SUMMARY This Study has sought to answer questions such as: • • • •
How much energy can be saved by building envelope conservation measures? How much increase in construction cost would occur? What would be the financial impacts on building developers and owners? What would be the economic and environmental impacts at the national level?
The methodology used in this study consisted of conducting parametric hourly energy simulations for selected representative building types and then of conducting an economic analysis featuring the calculation of the net present value (NPV) of each energy conservation measures in order to assess its economical interest. The projected heating and cooling energy savings resulting from the application of the Thermal Standard for buildings vary between 10% in climatic zone 1 (coastal) to over 50% in Climatic zone 4 (high mountain).
Residential Buildings
Office Buildings
1.000 0.900 0.800 0.700
GJ/m2
0.600 0.500 58%
0.400
57% 45%
0.300
47%
43% 13%
10%
23%
0.200 0.100 0.000 CZ 1
CZ 2
CZ 3
CZ 4
CZ 1
CZ 2
CZ 3
CZ 4
Climatic Zone Base Case
Thermal Standard
The incremental construction cost of the measures that have a positive net present value, vary from a no cost (savings on equipment are higher than the cost of envelop energy conservation measures) to a cost that is more significant. The average cost for office buildings was around 7.85 USD/m2 while for residential buildings it was around 5.92 USD/m2. The positive net present value obtained for all retained energy conservation measures translated into a payback period between zero (immediate payback) to a maximum of 6 years. The average payback period for office buildings was around 5.2 years while for residential buildings it was around 0.9 years if we consider that comfort conditions are to be met for higher hours of operation. Over a 20 year period, the projected national benefits resulting from the application of the Thermal Standard include the avoidance of around 1.4 million tons of oil-equivalent, and the avoidance of an associated 6 million tons of CO2. The economic savings resulting from the avoided energy will vary in magnitude depending on the price of fuel and diesel oil. Average estimations indicate savings in the range of 500 million USD. These savings come from cost effective measures and further highlight the positive impacts of the application of the thermal standard for Buildings in Lebanon. Energy Analysis and Economic Feasibility Study
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1
INTRODUCTION
The aim of the study is to determine cost effective thermal improvement levels for the Thermal Standard for Buildings in Lebanon. The heating and cooling energy used by various building types under various scenarios of envelope improvement was determined by computer simulations to establish a realistic economic analysis of improvement to building envelopes. Two types of mid-rise buildings were simulated, an office building and a residential building. The simulations were conducted using five different climatic weather files (four climatic zones were finally retained for the thermal standard) representing the full range of weather experienced in Lebanon. This provided a good representation of the energy usage in different regions. Then parametric simulations were realized from these ten base simulations (two building types and five weather files) to determine the impact of variation in building construction practices and materials. The Parameters investigated included: thermal transmittance levels (wall. roof and windows), window sizes and solar (shading) characteristics as well as architectural shading devices (fins and overhangs). A total of 1460 model runs were conducted for the parametric analysis. The economic analysis of each improvement was evaluated by calculating the direct payback period and the net present value of the investment and the generated savings for the various climatic zones of Lebanon.
1.1 1.1.1
Methodology of the Energy Analysis Selection of Energy Simulation Software
The Visual-DOE3 program simulates the envelope heat gains and losses as well as the operation of various types of cooling and heating systems in buildings. The weather conditions are specified as typical year hourly weather files and are used to calculate the energy use, (electrical, fuel, gas) associated with cooling and heating loads. The software also takes into account temperature set point schedules, building occupancy schedules, lighting and equipment schedules and other energy end-uses as infiltration and ventilation. The simulation program is a powerful and flexible energy analysis tool for buildings. Professional journals have highlighted the value of this modeling tool for the evaluation of various alternative energy efficiency options. Nowadays, the dynamic simulation program DOE-2.1E and its interface Visual-DOE3 are one of the reliable energy prediction tools on the market. The software Visual-DOE3 was used to model the operation and energy consumption of space conditioning systems in each building type assuming a building mass representative of current construction practice in Lebanon. For walls, the insulation was assumed to be integrated between two wall layers to various levels of thermal transmittance. It is to be noted however, that the Visual-DOE3 software does not model Thermal Bridges. As such, the latter could not be integrated within the scope of the present study, but should be the subject of future analysis.
1.1.2
Outdoor Design Conditions
Weather records for each of the climatic zones in Lebanon were analyzed statistically to determine the 99% and 97.5 % probability values of minimum winter dry-bulb temperatures and maximum summer dry-bulb and wet-bulb temperatures. The results are presented in Table 1. Energy Analysis and Economic Feasibility Study
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Table 1 Outdoor Design Conditions for Lebanon Climatic Zone Zone 1: Coastal Zone 2: Western Mid-Mountain Zone 3: Inland plateau Zone 4: High mountain
1.1.3
Winter ºC Design Dry-Bulb 99% 97.5% 6 7 -2 -1 -5 -4 -10 -7
Summer ºC Design Dry-Bulb Design Wet-Bulb 1% 2.5% 5% 1% 2.5% 5% 34 33 32 27 26 25 32 31 31 22 22 21 37 36 35 22 21 21 31 30 30 22 21 21
Indoor Temperature Assumptions
The decision of average indoor temperatures that should be used for the simulations involves some hypothesis as there are two aspects of uncertainty in determining the appropriate comfort level for Lebanon: • •
First, the indoor temperatures chosen by the building occupant. Indoor temperatures in Lebanon are quite varied because most people heat or cool living areas intermittently. Second, the way that the thermostat location and settings in the living areas affect temperatures in other zones of the building and corresponding heat losses.
The occupants of the residences control to a large extent both temperature setting of the control thermostat(s) and the distribution of heated or cooled air through the building by leaving doors between zones of a building open or closed. However, the purpose of a thermal standard is not only to avoid excessive heating and cooling loads according to current practices, but also to insure that in the future buildings will have an envelope that could provide a good performance if heating or cooling equipment are added or if comfort level is increased in buildings. The penetration rate of heating and cooling equipment indicates that inhabitants in Lebanon need to meet a certain comfort level. For these reasons, the complete analysis was performed with residential buildings fully heated and conditioned to comfort level. For office buildings, the normal occupancy schedule was used as the period of time that the space conditioning was operated. The heating and cooling schedule which was considered as operation practice in Lebanon consisted of: Living zones heated and/or cooled during the day and night. Heating and cooling are determined by the thermostat set points. The set-point temperatures (operative temperatures) are 21.1ºC for heating in winter and 23.3 ºC for cooling in summer.
1.1.4
Selection of Building Types for Simulation
In order to select representative buildings, statistics of the existing building stock by region were examined. These data are presented in Tables 2 and 3. Table 2 Breakdown of Existing Building Units (up to 1995) according to use
Beirut Mount Lebanon North Lebanon Beqaa South Lebanon Nabatieh Total
No. of Bldg. Units 159,438 611,346 257,514 178,879 152,367 96,835 1,456,379
Residential
Commercial
115,728 462,804 177,961 121,198 111,136 73,018 1,061,845
42,703 146,544 76,417 57,239 40,924 23,671 387,498
Mixed R/C 510 779 1,317 107 206 67 2,986
Not Determined* 497 1,219 1,819 335 101 79 4,050
Source: Administration Centrale de la Statistique – Etude Statistique No. 3, 4, 6, 8, 11 – Recensement des immeubles et des etablissements – 1996
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Table 3 Average number of Floors per Building Permit 1994
Total No. of Permits
Total Area in m2
Total No. of Floors
Beirut Suburbs of Beirut Rest Mount Lebanon North Lebanon Beqaa South Lebanon Nabatieh
222 1,273 2,853 1,292 961 951 770
733,000 1,497,000 1,806,000 807,000 520,000 712,000 343,000
1,689 3,884 6,230 2,821 1,669 2,161 1,393
Average Area (m2) per Permit 3,301.8 1,175.9 632.9 624.9 540.6 748.4 445.0
Total
8,322
6,418,000
19,847
771.0
2.3
1995
Total No. of Permits
Total Area in m2
Average No. of Floors per Permit 8.4 3.7 2.2 2.5 1.8 2.2 1.8
184 1,905 4,457 1,569 1,185 1,179 938
650,000 2,560,000 3,479,000 1,468,000 588,000 850,000 423,000
1,543 6,988 9,968 3,913 2,076 2,589 1,695
Average Area (m2) per Permit 3,531.9 1,344.0 780.5 935.8 495.9 721.1 450.7
Total
11,417
10,018,000
28,772
877.4
2.5
1996
Total No. of Permits
Total Area in m2
Average No. of Floors per Permit 7.3 3.1 2.3 3.5 1.8 2.2 1.8
Beirut Suburbs of Beirut Rest Mount Lebanon North Lebanon Beqaa South Lebanon Nabatieh
Total No. of Floors
Average No. of Floors per Permit 7.6 3.1 2.1 2.2 1.7 2.2 1.8
146 1,801 4,725 1,738 1,202 1,129 711
410,000 2,738,000 3,602,000 1,241,000 729,000 788,000 379,000
1,059 5,540 10,643 6,070 2,113 2,449 1,295
Average Area (m2) per Permit 2,806.5 1,520.3 762.4 714.3 606.5 698.2 533.2
Total
11,452
9,887,000
29,169
863.4
2.5
1997
Total No. of Permits
Total Area in m2
Average No. of Floors per Permit 7.6 3.6 2.4 2.6 2.2 2.4 1.9 2.6
Beirut Suburbs of Beirut Rest Mount Lebanon North Lebanon Beqaa South Lebanon Nabatieh
Beirut Suburbs of Beirut Rest Mount Lebanon North Lebanon Beqaa South Lebanon Nabatieh Total
Total No. of Floors
Total No. of Floors
164 1,452 3,297 2,210 1,288 1,533 1,055
520,000 2,025,000 2,963,000 1,468,000 1,102,000 2,032,000 613,000
1,244 5,214 7,941 5,725 2,773 3,688 2,024
Average Area (m2) per Permit 3,169.2 1,394.5 898.7 664.1 855.6 1,325.5 581.4
10,999
10,723,000
28,609
974.9
Source: Administration centrale de la statistique, Etude Statistique No. 15, Dec. 1999, Les permis de construire au Liban entre 1994 et 1997
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Table 4 Breakdown of building permits (1994-1997) according to Use
Year 1994 Year 1995 Year 1996 Year 1997
Total Area in m2 6,418,000 100% 10,018,000 100% 9,887,000 100% 10,723,000 100%
Residential
Mixed R/C
3,144,000 49% 5,964,000 59.5% 6,014,000 60.8% 6,541,000 61%
1,230,000 19.2% 1,888,000 18.9% 1,642,000 16.6% 2,177,000 20.3%
Commercial 244,000 3.8% 475,000 4.7% 779,000 7.9% 1,115,000 10.4%
Not determined* 1,800,000 28% 1,691,000 16.9% 1,452,000 14.7% 890,000 8.3%
Source: Administration centrale de la statistique, Etude Statistique No. 15, Dec. 1999, Les permis de construire au Liban entre 1994 et 1997.
For the purpose of selecting the most appropriate building types in order to conduct the analysis for the optimization of the envelope thermal characteristics within limited scope of this study, the following building types were selected to represent the residential and the other sectors:
Residential buildings: Multifamily housing It was decided that because of the limited scope of the project, only a five floor residential building would be modeled. It is expected that in future revisions of the thermal standard (for the mandatory introduction of the standard), this limitation will be addressed by modeling different types of residential buildings to see the sensitivity of the recommendations to the building major parameters. However, for the initial voluntary introduction of the thermal standard, the selected building can be considered representative of the building stock in Lebanon.
Other buildings: represented by an office building The majority of buildings in this category are commercial or institutional buildings and will both require space heating and cooling, which may or may not have been installed at the time the building was built. It was decided to use a five floor office building in the model study.
1.1.5
Current Construction Practices in Lebanon
The main elements that characterize the building envelopes in Lebanon include: • • •
•
Walls: Predominantly consist of hollow concrete blocks with a thickness of 15-20cm with 1 cm of plaster on each side. Exterior cladding when installed is typically stone cladding (limestone, sandstone, granite, etc.). Roofs: Are either flat or tilted. They are usually not insulated. Pitched roofs tend to be covered with tiles. Windows: Are typically made of single or double glazing (single is still the most popular) with aluminum frames. Glass panes are usually six millimeters thick and in some cases are tinted or with a reflective coating. Some high end buildings use double glazing but it is not the dominant trend. Low-e windows have been reported in some projects, but it is a relatively new trend. Other architectural features: Balconies are widely used in residential buildings. They provide some shading for windows.
Although this type of construction has the advantage of thermal mass, some shortcomings include: • •
Poor thermal performance and poor waterproofing of walls and roofs. Concrete is a good heat conductor and therefore allows heat to pass through either direction according to temperature differences between indoor and outdoor. Windows tend to have a high level of heat transmission due to air leakage across the frame and to heat transfer across the single glass pane and frame.
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1.1.6
Building Models
The two buildings selected for the analysis are described in this section. Residential Building Model The baseline residential building can be characterized as follows: The building has a rectangular floor plan, is five floors high and has a flat roof. It is built of hollow concrete walls, concrete floor slabs and is plastered inside and out. The details of construction, materials, fenestration, schedules and set points are described in detail in Annex 1. Commercial Building Model The base commercial building can be characterized as follows: The building has a rectangular floor plan, with the longer dimensions facing north and south. The building is five floors high and has a flat roof. The details of construction, materials, fenestration, operational schedules and set points are described in detail in Annex 2. Table 5 presents the composition and the thermal characteristics of Roofs and exterior walls including the thermal resistance (R-values) and thermal conductance (U values) excluding the effect of the air films. Table 5 Base Case Roof and Wall Compositions of the Residential and Office Buildings Component
Roof
Layer 1 2 3 4 5
Material Tile Sand Weatherization Concrete Plaster
Thickness (mm) 30 100 10 200 10
Total
Wall
U value 1 2 3
(W/m2 ºC) 10 150 10
Plaster Hollow block Plaster
Total (W/m2 ºC)
U value
RSI value (m2 ºC/W) 0.0882 0.1176 0.0588 0.1129 0.0139 0.3913 2.56 0.0139 0.2636 0.0139 0.2914 3.432
Both base models have 6 mm thick clear glass, single pane windows with a thermal conductance (U factor) of 6.16 W/m2.C and a shading coefficient of 0.95.
1.1.7
Building Operating Schedule
In order to avoid dealing with a multiplicity of building variations in the model, their operating schedule of operation were standardized to be the same for all weather zones. It was assumed that residential buildings are occupied full-time, whereas the commercial buildings are occupied parttime according to the day of the week and associated hours of occupancy.
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1.2
Methodology of the Economic Analysis
The methodology used in this study is to calculate the net present value (NPV) of each energy conservation measures in order to assess its economical interest. Annex 4 provides more information on the economic analysis approach and the discount rate selected. The main findings of these analyses are summarized in the following sections. The economic analysis was evaluated by determining the direct payback period and the net present value (NPV) of each improvement. The economic analysis took into consideration the energy cost forecast, discount rates, incremental costs and energy savings calculated for each option. The scenarios used for the economic analysis, This section discusses the hypothesis used for the economic analysis including the length of the study period, the price of energy, the discount rate and the combination of these hypotheses into nine individual scenarios for a sensitivity analysis. 1.2.1
General Parameters for the Net Present Value Calculation
The net present value analysis was conducted in constant value of money stream. The study period for the economic analysis was selected as 20 years. This period is appropriate as the measures considered have a very long life and in reality will extend for some of them over the 20 years period. For instance, wall and roof insulation and architectural shading can be considered permanent measures. A period of 20 years is reasonable for windows. No residual value for the energy conservation measure investments were considered at the end of the study period. 1.2.2
Energy Prices
The future evolution of energy prices cannot be predicted with certainty. For the purpose of this study, three scenarios were developed with different costs of energy in USD per barrel for crude oil and in USD per liter for diesel oil. Please see Annex 3 for the rationale and references. The assumed price of crude oil over the next 20 years (in constant dollars) is presented in Table 6.
Table 6 Crude Oil 20 Year Forecast Assumptions Alternative Low Base High
Price (USD 2002/Barrel) 20.452 25.565 30.678
Remarks - 20% from the base case + 20 % from the base case
Consequently, as explained in Annex 3 corresponding diesel oil prices are as presented in Table 7. Table 7 Assumed Diesel Oil Prices Scenario Low Base High
Price (USD 2002/liter) 0.24 0.30 0.36
Remarks 20% lower than base case 20% higher than base case
Based on usage of electricity by the base case buildings for space heating and cooling and the current rate structures for electricity for the various types of customers, the rates were estimated in Annex 3 for three scenarios. Table 8 represents the respective electricity prices for each customer class adjusted for the crude oil price forecasts presented in Table 6.
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Table 8 Cost of Electricity in LP and US$ Per Kwh Scenario Low case Base case High case
1.2.3
Residential houses (LP or USD/kWh) 234.300 – 0.1562 241.450 – 0.1609 248.600 – 0.1657
Offices and others (LP or USD/kWh) 213.000 – 0.142 219.500 – 0.14633 226.000 – 0.1506
Discount Rates
Several factors were taken into consideration when determining the discount rate (excluding the risk factor associated with the variability of energy consumption from year to year due to weather variations) The factors are described in Annex 5. Very briefly, they included: • • • •
Current national bond interest rates; A country risk interest premium; Consideration of a discount rate for investors without opportunities to invest outside of Lebanon (interest rates on Lebanese government treasury bonds were taken); Consideration of a discount rate for investors with opportunities outside of Lebanon; the country risk premium was taken from the debt rating given by rating agencies. The assumed discount rates are presented in Table 9.
Table 9 Assumed Discount Rates Name of Case Base Case High Case Low Case
1.2.4
Discount Rates 12% 16% 8%
Establishment of Simulation Scenarios
Using the above three scenarios about the price of crude oil (and the subsequent effects on the price of diesel fuel and electricity) and the three discount rate scenarios, the set of simulation scenario parameters in table 10 were established for the simulations. Table 10 Presentation of the Nine Scenarios Parameter 1 – Energy Price
Low alternative
Base High alternative alternative 0.24 0.30 0.36 0.1562 0.1609 0.1657 0.1420 0.14633 0.1506 Resulting combined scenarios
Domestic Diesel Oil (EN 590) Price ($/liter) Electricity Price - Residential ($/kWh) Electricity Price - Offices and Other ($/kWh) Parameter 2 – Discount Rate Low Alternative for Discount Rate at 8% Base Alternative for Discount Rate at 12% High Alternative for Discount Rate at 16%
Scenario 3 Scenario 6 Scenario 9
Scenario 1 Scenario 4 Scenario 7
Scenario 2 Scenario 5 Scenario 8
Finally, the nine scenarios that became the basis of the economic and sensitivity analysis assembled from the above price assumptions are presented in Table 11. Scenario number 4 is the preferred scenario which will be used to establish the recommendation for the thermal standard. The other eight scenarios will be used to perform a sensitivity analysis of the recommendations made.
Energy Analysis and Economic Feasibility Study
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Table 11 Parameters that will be applied in the Nine Scenarios Scenario
Residential Buildings
Office Buildings and other Non-residential buildings
1.2.5
1 2 3 4 (Preferred) 5 6 7 8 9 1 2 3 4 (Preferred) 5 6 7 8 9
Discount Rate (%) 8 8 8 12 12 12 16 16 16 8 8 8 12 12 12 16 16 16
Diesel Oil Price (USD/liter) 0.30 0.36 0.24 0.30 0.36 0.24 0.30 0.36 0.24 0.30 0.36 0.24 0.30 0.36 0.24 0.30 0.36 0.24
Electricity Price (USD/kWh) 0.1609 0.1657 0.1562 0.1609 0.1657 0.1562 0.1609 0.1657 0.1562 0.14633 0.1506 0.1420 0.14633 0.1506 0.1420 0.14633 0.1506 0.1420
Assumptions on Material and Labor Cost for Energy Saving Measure
Incremental costs of adding insulation, better windows and shading were estimated to determine the additional capital cost required to achieve energy savings in combined space heating and cooling of the model buildings. The cost assumptions are detailed in Annex 6. 1.2.6
Assumptions for the Avoided Cost of HVAC Systems
The economic analysis took into consideration the avoided cost of the heating and cooling equipment that could be reduced in size if the building is thermally improved. Annex 7 provides the details of the values used. The avoided costs used for the residential and office buildings are the following: Building Type
Heating (USD/kW)
Cooling (USD/kW)
Residential Buildings
69.56
577.55
Office Buildings
63.97
177.71
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2
ENERGY ANALYSIS
Parametric simulations were conducted on the two selected building types by varying thermal transmittance levels of the following building components: walls, roofs, and glazing. Also, parametric simulations were conducted for the glass shading coefficient, the window-to-wall ratio and the architectural shading factor (fins and overhang). The simulations were conducted for each weather station to assess the difference in economic return of various improvements in different climates. Each simulation involved changing one of the parameters, while other parameters were maintained at the base case value. Additionally, limited selected combined simulations were conducted.
2.1
Parametric Variations of Roof U-value
The roof thermal insulation level was varied between the simulation runs as indicated in Table 12. Table 12 Parametric Alternatives of Roof U factor Case
Base Alternative 1 Alternative 2 Alternative 3 Alternative 4
U value (W/m2C) Excluding Air Films Residence Office 2.556 2.556 1.116 1.116 0.713 0.713 0.524 0.524 0.415 0.415
Equivalent Insulation (R=0.25) Thickness (cm) 0 2 4 6 8
Results for office buildings Figure 2 presents the results of the roof insulation parametric runs for the office building. The U factor, plotted along the x-axis, varies from 2.556 to 0.415. The space conditioning energy required for each roof insulation variation is plotted along the y-axis. The related reduction in energy usage for the whole building varies from 6% in Beirut to 11% in Cedars. This result is expectedly small, as the roof is a relatively small surface area compared to 5 floors of external wall area, and the window surfaces where a large portion of the heat loss/gain takes place. In Beirut, most of the savings result from the reduction in heating energy which is reduced by 39%. The cooling is nearly unaffected by the insulation with a mere 2% reduction. In the overall, the 39% reduction in heating is weighted by the fact that heating energy is much smaller than the cooling energy in Beirut, this results in an overall 6% in reduction. The small cooling reduction is a result of the double impact of the insulation during the cooling season. On the one hand, the insulation reduces the heat gain by transmission through the roof which has a tendency to reduce the cooling load in the building. On the other hand, added insulation makes the spaces at the top floor more efficient “greenhouses” so they trap the solar heat gain and are less able to dissipate the heat through conduction during the night. The insulation also results in a longer cooling period even during the day as the point of equilibrium of the building between the solar heat gain and the natural cooling through envelope walls shifts earlier at the beginning of the cooling season and later at the end of the cooling season. This results in an addition of load for the mechanical cooling system for the solar load portion.
Energy Analysis and Economic Feasibility Study
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These effects (conduction and solar) cancel each other mostly and the resulting impact on the cooling energy consumption is very small. However, some results came from a limitation of the Visual-DOE3 system which cannot really model occupant behavior like the intentional opening of windows. In a case where the building was overheated during the week-end, it is likely that the occupants will open the windows (if the building has windows that can be opened) when arriving to their office Monday morning thus effectively creating a large natural ventilation that will help to rapidly dissipate the extra heat without much air conditioning. In buildings where the natural ventilation can be controlled by opening the window, we can even expect some savings on air conditioning because the insulation will help reduce the heat gain by transmission when the outside temperature is higher than the inside temperature. On the other hand, some buildings do not have opening windows and will require some air conditioning to remove the extra heat trapped inside during unoccupied hours (mainly week-ends) by the additional insulation of the building envelope. The tool limitation was somewhat mitigated by fixing the seasonal period where the air conditioning system is allowed to operate. With this approach, some of the period where there is a cooling load during the mid-season does not result in additional energy usage for the building. The assumption is that the occupants’ behavior to open windows will help reduce the negative effect of insulation by regulating the natural ventilation to evacuate any excess heat trapped inside the building and will effectively allow realizing some savings on air conditioning. These savings will be approximately the same as the extra cooling load added to building without opening windows so on a national basis it is expected that both trends will cancel each other. At the other end of the climate spectrum, Cedars climate has little effect on cooling load. In this case, the solar heat gain is more important in the building energy balance and the colder nights will allow a better evacuation of any excess heat. We can observe a slight increase in cooling consumption (1%). But as the cooling season is very short, this does not affect the results too much and the heating energy reduction of 12% provided an overall weighted energy reduction of 11% from worst to best case scenario. Here again, the added cooling energy was not taken into consideration in the economic analysis on the ground that occupants will open windows to dissipate any excess heat inside the buildings. When a building is insulated, this type of behavior for the cooling energy requirement is quite typical of Mediterranean climates where there is a delicate balance between the heat gain by conduction in summer and the solar and internal heat gain trapped in the insulated building. The insulated envelope is less able to dissipate excess heat when outside temperature falls at night or during the mid-season. During these periods we can simultaneously have a temperature outside that is below the desired internal temperature and important heat gain from the sun that will have a less important angle above the horizon than during the summer months. Both effects (conduction heat gain reduction and solar and internal heat trapping) are about the same order of magnitude and the resulting effect on the cooling energy if any will always be small. One of the conclusions we can draw from these simulations is that the thermal standard should focus on insulation in cold regions and on the rejection of solar heat gain in hot climate. Rejection of solar load can be achieved by reflective glazing, shading, and by reflective or light colored roof surfaces. For roofs, it is possible to use a reflective membrane to cover the roof and significantly reduce the cooling load but this faces several practical constraints that make this measure unacceptable as a requirement in a thermal standard. There is first the aesthetic and glaring effect the reflective roof could have on nearby buildings. The second issue is the access and cleaning of roof surfaces. As dirt accumulates on the reflective surface, there will be a degradation of the reflective effect and an increase in the interior cooling load. Thus as there is no way to insure a regular cleaning of the surface, this kind of measure is difficult to apply as a long term solution and as a requirement in a thermal standard but it can definitively be applied as a recommendation with the appropriate warning to designers and promoters.
Energy Analysis and Economic Feasibility Study
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Figure 1 - Total Energy Usage vs. Roof U factor – Office Buildings Total Energy Usage vs Roof U Factor Office Buildings 1,400
1,200
600
Energy Usage (GJ)
800
Beirut Bayssour Kartaba Zahle Cedars
Cooling Energy (GJ)
1,000
Beirut Bayssour Kartaba Zahle Cedars
400
200
3.000
2.500
2.000
1.500
1.000
0.500
0 0.000
U factor (W/m2.C)
Figure 2 - Cooling Energy Usage vs. Roof U factor – Office Buildings Cooling Energy Usage vs Roof U Factor Office Buildings 350
300
250
200
150
100
50
3.000
2.500
2.000
1.500
1.000
U factor (W/m2.C)
Energy Analysis and Economic Feasibility Study
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0.500
0 0.000
Figure 3 - Heating Energy Usage vs. Roof U factor – Office Buildings Heating Energy Usage vs Roof U factor Office Buildings 1,200
1,000
600
Heating Energy (GJ)
800 Beirut Bayssour Kartaba Zahle Cedars
400
200
3.000
2.500
2.000
1.500
1.000
0 0.000
0.500
U factor (W/m2.C)
It is interesting to compare the effect of the roof insulation on the last floor of the building. In the results shown for Cedars, the reduction in total energy is 11%. Figure 4 presents the effect of insulation on the last floor of the building. We can see that the reduction in total energy for this floor is higher with 34.1% of the total energy reduction. The reduction in heating is 37.0% while the cooling requirement increases by -4.4%. Figure 4 - Effect of the roof insulation – Cedars Energy usage for the 5th floor Cedars 500 450 400
300 250 200 150 100 50 0 3
2.5
2
1.5
1
U factor (W/m2.C)
Energy Analysis and Economic Feasibility Study
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0.5
0
Energy Usage (GJ)
350
Heating energy Cooling energy Total Energy
Results for residential buildings For residential buildings, we observe a pattern similar to office buildings with the exception that the percentage savings generated are higher due to longer hours where comfort conditions are maintained compared to office buildings. For instance, the savings on total energy for Beirut, if 4 cm of insulation (R=0.25) is installed, are 7% compared to 6% in office buildings. Figure 5 presents the results for the total energy variation for the five considered weather files. In the case of residential buildings, the insulation of the roof has a neutral effect on the cooling load for the coastal zone. This is due to the fact that most of the cooling load comes from the solar gains, the internal gains and the infiltration. The insulation has also the perverse effect to trap more the internal gain and the solar gain inside the building thus increasing the duration of the cooling period and the energy usage for cooling. This effect reduces the gain that could be obtained by the reduction in conduction heat gain. The control strategy applied to residential buildings considered that the occupants will open windows as soon as the exterior temperature will be sufficiently low to insure a comfort level by natural ventilation.
Figure 5 - Energy Usage vs. Roof U factor – Residential Buildings Total Energy Usage vs Roof U factor Residential Buildings 1400
1200
Energy Usage (GJ)
1000
Beirut Bayssour Kartaba Zahle Cedars
800
600
400
200
0 3.000
2.500
2.000
1.500
1.000
U factor (W/m2.C)
Energy Analysis and Economic Feasibility Study
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0.500
0.000
Figure 6 - Cooling Energy Usage vs. Roof U factor – Residential Buildings Cooling Energy Usage vs Roof U factor Residential Buildings 300
250
Cooling Energy (GJ)
200 Beirut Bayssour Kartaba Zahle Cedars
150
100
50
0 3.000
2.500
2.000
1.500
1.000
0.500
0.000
U factor (W/m2.C)
Figure 7 - Heating Energy Usage vs. Roof U factor – Residential Buildings Heating Energy Usage vs Roof U factor Residential Buildings 1400
1200
Heating Energy (GJ)
1000
Beirut Bayssour Kartaba Zahle Cedars
800
600
400
200
0 3.000
2.500
2.000
1.500
1.000
U factor (W/m2.C)
Energy Analysis and Economic Feasibility Study
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0.500
0.000
2.2
Parametric Variations of Wall U-value
The alternative wall thermal transmittance levels used in the parametric analysis are presented in Table 13. The base case is a single wall without insulation. All the other cases include a double wall made of a 15 cm row of hollow concrete blocks and a 10 cm row of hollow concrete blocks. The insulation layer is installed in between the two rows of blocks. The alternatives consist of varying the thickness of insulation (R=0.25) by an increment of 2 cm. Table 13 Parametric Alternatives of Wall U factor Case
Base Wall Alternative 1 Alternative 2 Alternative 3 Alternative 4
Equivalent Insulation (R=0.25) Thickness (cm) 0 2 4 6 8
U value including air films (W/m2.C) Office Residence 3.432 1.011 0.669 0.500 0.399
3.432 1.011 0.669 0.500 0.399
Results of Wall insulation analysis for office buildings Figure 8 presents the results of the wall insulation parametric runs. The U factor varies from 3.432 W/m2.C with a single row of hollow block walls and no insulation to a minimum of 0.399 with double block walls and 8 centimeters of insulation (R=0.25) between rows of blocks. The U value constitutes the “x” axis while the global energy consumption at cooling and heating equipment input is shown in Gigajoules on the “y” axis. The related reduction in energy usage for the whole building varies from 9% in Beirut to 33% in Cedars when we insulate the walls from no insulation to a maximum of 8 cm expanded polystyrene. In all regions, most of the savings come from the reduction in heating energy. In Beirut, 8 cm insulation with expanded polystyrene will result in 77% reduction (indicating that the building will be in equilibrium between the losses and the internal and solar heat gains. However, the heating season and energy consumption is very small) while in Cedars, the reduction will be 36%. The effect of insulation on cooling requirement is approximately neutral or slightly negative. As it was the case for the roof, the mixed results we get for the cooling season is a result from a double effect of the insulation during the cooling season. On the one hand, the insulation reduces the heat gain by transmission through the wall which has a tendency to reduce the cooling load in the building (but the reduction is small). On the other hand, the added insulation makes the building a more efficient “greenhouse” so it traps the solar and internal heat gains more effectively and is less able to dissipate the heat through conduction (when the outside temperature drops below the interior temperature, especially in mid-season). These result in an additional load for the mechanical cooling system to extract the internal and solar loads for more hours in the year. Both effects (conduction transmission reduction and solar and internal gain trapping) mostly cancel each other and the resulting impact on the cooling energy consumption is neutral or negative. Also, for the wall analysis, it will be considered that the occupants’ control of natural ventilation in buildings (where windows can be opened) will offset any small increase in cooling energy requirement.
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Figure 8 - Total Energy Usage vs. Wall U factor - Office Buildings Total Energy Usage vs Wall U factor Office Buildings 1,400
1,200
Total Energy (GJ)
1,000
Beirut Bayssour Kartaba Zahle Cedars
800
600
400
200
0 4.000
3.500
3.000
2.500
2.000
1.500
1.000
0.500
0.000
U factor (W/m2.C)
Figure 9 and figure 10 present respectively the results for heating energy and cooling energy.
Figure 9 - Heating Energy Usage vs. Wall U factor - Office Buildings Heating Energy vs Wall U factor Office Buildings 1,200
1,000
Heating Energy (GJ)
800 Beirut Bayssour Kartaba Zahle Cedars
600
400
200
0 4.000
3.500
3.000
2.500
2.000
1.500
U factor Walls (W/m2.C)
Energy Analysis and Economic Feasibility Study
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1.000
0.500
0.000
Figure 10 - Cooling Energy Usage vs. Wall U factor - Office Buildings Cooling Energy vs Wall U factor Office Buildings 350
300
Cooling Energy (GJ)
250
Beirut Bayssour Kartaba Zahle Cedars
200
150
100
50
0 4.000
3.500
3.000
2.500
2.000
1.500
1.000
0.500
0.000
U factor Walls (W/m2.C)
Results of Wall insulation analysis for residential buildings Figure 11 presents the results of the wall insulation parametric runs for residential buildings. The U factor varies from 3.432 W/m2.C with a single row of hollow block walls and no insulation to a minimum of 0.399 W/m2.C with double block walls and 8 cm of insulation (R=0.25) between the rows of blocks. The U factor is displayed on the x axis while the global energy consumption at cooling and heating equipment input is shown in Gigajoules on the y axis. The reduction in energy usage for the whole building varies from 11% in Beirut to 39-43% in the other regions when we insulate the walls from no insulation to a maximum of 8 cm expanded polystyrene. We observe the same trend as for the roof between office and residential buildings. Residential buildings produce more important savings than office buildings mainly because the hours of full time occupancy are considered higher. In all regions, most of the savings come from the reduction in heating energy as shown in figure 13. In Beirut, 8 cm of insulation will produce 94% reduction while in Cedars it will produce 47% reduction. The high result in Beirut just points to the fact that with this insulation level, the building does not need any additional heating and the already small heating load almost disappears. It is interesting to note that the impact in Beirut is the least important of the five weather files considered. In Beirut, we observe the same behavior as found for office buildings where the added insulation on walls has a positive and negative impact at the same time which results in little savings on the cooling energy. In Cedars, the effect is even negative but this will not be considered in the economic analysis as we will assume that the occupant will open the window to dissipate additional heat. In all the other climates, the heating impact is more important (Figure 13) since residential buildings are supposedly used for longer hours than office buildings.
Energy Analysis and Economic Feasibility Study
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Figure 11 - Energy Usage vs. Wall U factor - Residential Buildings Total Energy Usage vs Wall U factor Residential Buildings 1400
1200
Total Energy (GJ)
1000
Beirut Bayssour Kartaba Zahle Cedars
800
600
400
200
0 4.00
3.50
3.00
2.50
2.00
1.50
1.00
0.50
0.00
U factor (W/m2.C)
Figure 12 - Cooling Energy Usage vs. Wall U factor - Residential Buildings Cooling Energy vs Wall U factor Residential Buildings 300
250
Cooling Energy (GJ)
200 Beirut Bayssour Kartaba Zahle Cedars
150
100
50
0 4.00
3.50
3.00
2.50
2.00
1.50
U factor Walls (W/m2.C)
Energy Analysis and Economic Feasibility Study
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1.00
0.50
0.00
Figure 13 - Heating Energy Usage vs. Wall U factor - Residential Buildings Heating Energy vs Wall U factor Residential Buildings 1400
1200
Heating Energy (GJ)
1000
Beirut Bayssour Kartaba Zahle Cedars
800
600
400
200
0 4.00
3.50
3.00
2.50
2.00
1.50
1.00
0.50
0.00
U factor Walls (W/m2.C)
2.3
Parametric Variations of Window U-value and Shading Coefficient
Several alternative glazing options were tested in the simulations to determine the impact of various U factors and shading coefficients on the total energy usage of the residential and office building models during an entire year of operation. The following characteristics were changed in the alternatives: • The number of panes (1 or 2) • The shading coefficient of the glass • The U value (Width of air space, Filling gas, Low-e coating) These options not only represent the windows actually available in Lebanon but also the technologies that have not been widespread in Lebanon but are well proven in northern countries. The base case is a clear window with a single glazing and it represents the most frequently used window in Lebanon. The window types used for Options 1, 2 and 3 have a decreasing shading coefficient. These represent tinted or reflective windows that are popular in Lebanon. The window for Option 4 is a window with a low emissivity coating that is beneficial in winter to reduce heat losses through radiation by the windows. The low-e coating has little or no benefit during the cooling period. Options 5 to 9 are all double glazed windows. Option 5 is the most common clear window and it is becoming also popular in Lebanon partly because of its better thermal characteristics and partly because it provides a better sound attenuation. Windows for options 6 and 7 are tinted reflective glass that will reduce the solar heat gain. Options 8 and 9 are types of windows that achieve a very low thermal conductance through the combination of low-e films and/or argon filling between the panes. Alternative thermal conductance levels described above for use in both the residential building model and the commercial building model are summarized in Table 14.
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Table 14 Parametric Alternatives of Window U-factor and Shading Coefficient Composition
Option Base 1 2 3 4 5 6 7 8 9
Tint Clear Bronze Clear Clear Clear Clear Bronze Clear Clear Clear
Single (6 mm) Single (6 mm) Single (6 mm) Single (6 mm) Single (6 mm) Double (6mm, 12mm, 6mm) Double (6mm, 12mm, 6mm) Double (6mm, 12mm, 6mm) Double (6mm, 12mm, 6mm) Double (6mm, 12mm, 6mm)
Characteristics Reflective Low-e --------Tin-Oxide --Titanium ----e = 0.2 --------Stainless H ----e = 0.2 e = 0.2
Filling ----------Air Air Air Air Argon
U value W/m2 °C 6.16 6.16 6.12 5.50 4.27 2.74 2.74 2.50 2.0 1.7
SC (%) 0.95 0.71 0.58 0.45 0.84 0.81 0.57 0.26 0.78 0.79
Results of glazing analysis for office buildings The results achieved from these combinations of glazing properties are presented in figure 14. The base case window is identified with the letter “B” and the SC variations, low-e coating and gas filling are illustrated for Cedars (the shape is similar for the other regions except Beirut that is mirror like). The results are grouped into two main sections: single glazing on the left and double glazing on the right. If we first concentrate on the bottom left portion of the figure, we can observe an almost mirror shape curve between Beirut and Bayssour. For Beirut, we can see that as we decrease the shading coefficient of glass, the energy consumption has a tendency to decrease. This indicates that the solar load is important in this city and that the reduction of solar load by using tinted or reflective glass is beneficial. On the other hand, for the four other regions, we can see that an increase in shading coefficient alone without improvement of the U-value produces the inverse effect. The global energy consumption for the building increases as the shading coefficient is reduced. This means that for these locations, the solar heat gain in the heating season is more beneficial than the cooling load created during the cooling season. Thus reduction in solar heat gain in these zones may produce a global increase in the energy usage of the buildings. Figure 14 - Total energy usage with different glazing - Office Energy Consumed vs Glazing Types Office Buildings 1600
#2: SC= 0.58
1400
#3: SC= 0.45
#1: SC= 0.71
Cedars
SC= 0.95
1200
#7: SC= 0.26
B
#6: SC= 0.57
Beirut (Single) Bayssour (Single) Kartaba (Single) Zahle (Single) Cedars (Single) Beirut (Double) Bayssour (Double) Kartaba (Double) Zahle (Double) Cedars (Double)
#5: SC= 0.81
1000 Energy (GJ)
Cedars
#4: SC=0.84 & Low-e
#8: SC= 0.78 & low-e #9: SC= 0.79 & low-e & argon filling
Zahle 800 Zahle Kartaba 600
Bayssour
B
Kartaba
Bayssour
B
400 B
Beirut 200
Beirut
Single window
Double window
0 7
6
5
4
3
U Value (W/m.C)
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2
1
0
Figure 15 and figure 16 provide details for the heating and cooling energy for the various glazing types. Figure 15 - Heating energy usage with different Glazing – Office Heating Energy Consumed vs Glazing Types Office Buildings 1400 #2: SC= 0.58
Single window
#3: SC= 0.45
Double window
#1: SC= 0.71
1200
1000
#7: SC= 0.26
Cedars
SC= 0.95
#5: SC= 0.81
#4: SC=0.84 & Low-e
Energy (GJ)
Cedars
#6: SC= 0.57
B
Beirut (Single) Bayssour (Single) Kartaba (Single) Zahle (Single) Cedars (Single) Beirut (Double) Bayssour (Double) Kartaba (Double) Zahle (Double) Cedars (Double)
#8: SC= 0.78 & low-e
800
#9: SC= 0.79 & low-e & argon filling
Zahle 600 Kartaba 400
Zahle
B
Kartaba
B
Bayssour
200 Bayssour
Beirut
Beirut
B
0 7
6
5
4
3
2
1
0
U Value (W/m.C)
Figure 16 - Cooling energy usage with different Glazing – Office Cooling Energy Consumed vs Glazing Types Office Buildings 500 Single window
Double window
450 #8: SC= 0.78 & low-e
400
SC= 0.95
B
350
#1: SC= 0.71
Beirut
#4: SC=0.84 & Low-e
#5: SC= 0.81
Beirut
#9: SC= 0.79 & low-e & argon filling
#6: SC= 0.57
Beirut (Single) Bayssour (Single) Kartaba (Single) Zahle (Single) Cedars (Single) Beirut (Double) Bayssour (Double) Kartaba (Double) Zahle (Double) Cedars (Double)
#2: SC= 0.58
Energy (GJ)
#7: SC= 0.26
#3: SC= 0.45
300 B
250
Bayssour
Kartaba
B
Zahle
Bayssour
200 B
150
Zahle
Kartaba
B
100 Cedars
Cedars 50 0 7
6
5
4
3
U Value (W/m.C)
Energy Analysis and Economic Feasibility Study
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2
1
0
Figure 17 and Figure 18 provide a zoom–in view of the Beirut, Bayssour and Qartaba single glazing. The shading coefficient values are marked near the data points so it may be easy to follow the trend. As the shading coefficient decreases in Beirut, so does the energy usage meaning that controlling the solar gain is beneficial. The point for option 5 that is at the far right is a window with a lower conductance but with a higher shading coefficient. We can immediately see that the low-e characteristics have not been significant compared to the fact that the shading coefficient is not improved too much for this window type. For Bayssour and Qartaba, windows with a shading coefficient of 0.95, 0.71 and 0.58 show a steady increase in energy consumption as the shading coefficient is reduced (higher window reflectivity). For the window with a shading coefficient of 0.48, the energy usage is decreasing contrary to the trend in Beirut. In this case, the best characteristics of the U factor for this window produce a reduction of heating energy in the winter season. The window with a clear glass and a low-e coating is clearly interesting in Bayssour and Qartaba that have a longer heating season. We observe that the same pattern for Bayssour and Qartaba is also applicable to Zahle and Cedars but with a higher amplitude in energy variations.
Figure 17 - Single Glass Glazing Results - 3 Cities – Office Single Glazing - 3 Cities 600
SC=0.58 550
SC=0.95 SC=0.71
500 Total Energy (GJ)
SC=0.45
SC=0.71 SC=0.58
SC=0.45
SC=0.95 SC=0.84 & Low-e Beirut (Single) Bayssour (Single) Kartaba (Single)
450 SC=0.95
SC=0.84 & Low-e
SC=0.71
400
SC=0.84 & Lowe
SC=0.58 SC=0.45 350
300 6.5
6
5.5
5
4.5
4
U value (W/m.C)
If we now concentrate on the right side of Figure 14, we can observe that a similar pattern emerges for double windows. Figure 18 enlarges the bottom right portion of Figure 14 for Beirut, Bayssour and Qartaba. Here again, we can observe for Beirut that the glass shading coefficient is the key characteristics for performance of a window. The incorporation of a low-e layer and argon gas filling have little effect and will not be economically attractive as their effect is almost neutral compared to a standard double glazed window. In this case, the added thermal resistance of these high performance windows has a double effect. In winter, they reduce the heat losses of the building which is beneficial, but in the summer and mid-season, they trap a larger quantity of heat that has entered during the day by solar radiation and they reduce the ability of the building to cool down naturally at night. This results in a higher cooling load that nullifies the positive effect in winter. This behavior of buildings in the Mediterranean climate is quite common. An improvement of the thermal envelope in certain climates may in fact have a detrimental effect on the overall energy consumption of buildings. This initial analysis from the raw results will be confirmed later in this document by an economic analysis.
Energy Analysis and Economic Feasibility Study
:: 22 ::
Figure 18 - Double Glazing Analysis for 3 Cities – Office Double Glazing - 3 Cities 550
SC=0.26
500
Total Energy (GJ)
SC=0.57 450
SC=0.26
SC=0.81 SC=0.81
400
SC=0.78 & low-e
SC=0.57
SC=0.81
SC=0.78 & low-e
Beirut (Double) Bayssour (Double) Kartaba (Double)
SC=0.79 & low-e & Argon
SC=0.79 & low-e & Argon
SC=0.57 350 SC=0.26
300 2.9
2.7
2.5
2.3
2.1
1.9
1.7
1.5
U value (W/m.C)
Glazing analysis results for residential buildings Figure 19 presents the results of the energy simulations for the glazing types in residential buildings. The shape is similar to the office buildings in general but the savings generated are more important due to the higher occupancy rate of the residential buildings. Reflective and tinted glass windows can reduce the energy consumption in Beirut by 11% which may be an attractive measure as the incremental cost will be quite limited. In other regions, the low-e coating on single glazing provides savings ranging between 14% (Bayssour) to 21% Cedars of the total energy for cooling and heating. For double windows, the savings follow a similar trend and the percentage reduction achieved by the double glazing with low-e coating is more important with 21% achieved in Bayssour and up to 34% in Cedars. Figure 19 - Total Energy Usage with Different Glazing – Residential Energy Consumed vs Glazing Types Residential Buildings 1,200
#2: SC= 0.58
1,000
#3: SC= 0.45 #1: SC= 0.71
Cedars
B: SC= 0.95
#7: SC= 0.26
800 Energy (GJ)
#6: SC= 0.57 #4: SC= 0.84 & low-e
#5: SC= 0.81
600
Beirut (Single) Bayssour (Single) Kartaba (Single) Zahle (Single) Cedars (Single) Beirut (Double) Bayssour (Double) Kartaba (Double) Zahle (Double) Cedars (Double)
Cedars #8: SC= 0.78 & low-e
Zahle #9: SC= 0.79 Low-e & argon
Zahle
Kartaba 400
Beirut
Bayssou
Kartaba
Beirut
Bayssour
200 Single window
Double window
0 7
6
5
4
3
U Value (W/m.C)
Energy Analysis and Economic Feasibility Study
:: 23 ::
2
1
Figures 20 and 21 present the detailed results of the simulation for the heating and cooling energy for the building. Figure 20 - Heating energy usage with different Glazing - Residential Heating Energy consumed vs Glazing types Residential building 1000 #2: SC= 0,58
Single window
#3: SC= 0,45
Double window
900 #1: SC= 0,71
800
Cedars
B: SC= 0,95
#7: SC= 0,26
700
Beirut (Single) Bayssour (Single) Kartaba (Single) Zahle (Single) Cedars (Single) Beirut (Double) Bayssour (Double) Kartaba (Double) Zahle (Double) Cedars (Double)
#6: SC= 0,57
Energy (GJ)
600
Cedars #4: SC= 0,84 & low-e
500 400
#5: SC= 0,81
#8: SC= 0,78 & low-e
#9: SC= 0,79 Low-e & argon
Zahle Zahle
300 Kartaba 200
Bayssou
Bayssour
Kartaba
100 Beyrouth
Beyrouth
0 7
6
5
4
3
2
1
U Value (W/m.C)
Figure 21 - Cooling energy usage with different Glazing - Residential Cooling Energy consumed vs Glazing types Residential building 400 Single window
B: SC= 0,95
Double window
#9: SC= 0,79 Low-e & argon
#5: SC= 0,81
350 #1: SC= 0,71
#4: SC= 0,84 & low-e
Beyrouth
#8: SC= 0,78 & low-e #6: SC= 0,57
#2: SC= 0,58
300
#3: SC= 0,45
#7: SC= 0,26
Beirut (Single) Bayssour (Single) Kartaba (Single) Zahle (Single) Cedars (Single) Beirut (Double) Bayssour (Double) Kartaba (Double) Zahle (Double) Cedars (Double)
Beyrouth
Energy (GJ)
250 Bayssour 200
Bayssou
Kartaba
150 Kartaba
Zahle
Zahle
100 Cedars
50
Cedars
0 7
6
5
4
3
U Value (W/m.C)
Energy Analysis and Economic Feasibility Study
:: 24 ::
2
1
Figure 22 and Figure 23 provide a close-up view of the single and double window results for Beirut, Bayssour and Qartaba.
Figure 22 - Single Glazing Analysis for 3 Cities - Residential Single Glazing - 3 Cities Residential Buildings 430 SC=0.58 410
SC=0.45
SC=0.71
390
Total Energy (GJ)
SC=0.95 SC=0.95
370
SC=0.58
SC=0.71
SC=0.45
Beirut Bayssour Kartaba
SC=0.84 & Low-e
SC=0.95
350
SC=0.71 330
SC=0.58 SC=0.45
SC=0.84 & Low-e
310 SC=0.84 & Low-e 290 6.5
6
5.5
5
4.5
4
U value (W/m.C)
Figure 23 - Double Glazing Analysis for 3 Cities - Residential Double Glazing - 3 Cities Residential Buildings 360
SC=0.78 & low-e
SC=0.81
SC=0.798 & low-e & Argon
350
SC=0.26
340
Total Energy (GJ)
330
SC=0.57 SC=0.57
320 SC=0.26
300
SC=0.798 & low-e & Argon
Beirut Bayssour Kartaba
SC=0.81
310
SC=0.78 & low-e SC=0.57
290
SC=0.81
SC=0.26
280 270
SC=0.798 & low-e & Argon
SC=0.78 & low-e
260 1.5
1.7
1.9
2.1
2.3
U value (W/m.C)
Energy Analysis and Economic Feasibility Study
:: 25 ::
2.5
2.7
2.9
2.4
Parametric Variations of Window to Wall Ratio
The Window to wall ratio of a building is defined as the ratio of the vertical window areas to the gross area of the vertical walls (the gross area is inclusive of the window area). The Skylight to roof ratio (SRR) is similarly defined as the ratio of the skylight area over the gross roof area (the gross roof area includes the skylight area). They are calculated respectively using Equations 1 and 2. WWR = Σ (Awi x SCwi x ASFwi) / Σ Av (1) SRR = Σ (Asi x SCsi) / Σ Ah (2) Awi SCwi ASFwi Av Asi SCsi Ah
= = = = = = =
Area of the individual vertical window (m2) Shading coefficient of the individual vertical window Architectural shading factor of the individual vertical window Area of all vertical surfaces (opaque walls + windows) (m2) Area of the individual skylight (m2) Shading coefficient of the individual skylight Area of all horizontal surfaces (roofs + skylights) (m2)
The Impact of the WWR and the SRR on the heating and cooling energy usage of a building are quite important. The thermal standard will set recommended effective fenestration ratios. These will take into consideration WWR and SRR ,the glazing characteristics, orientation, and architectural shading devices (fins and overhang). A series of simulations were thus performed for various percentages of windows to the gross wall ratios. In the Thermal Standard it will be recommended to put a limit to the WWR and SRR that designers or developers are allowed to use under the prescriptive path for compliance. To illustrate the necessity for this requirement, we can take the case of a building to be constructed in a region where there is no limit for WWR and no limit for window U-value but where there is a limit for wall and roof U-value. This can be the case in mild climates (like Lebanon Coastal regions) where double windows cannot be required by a regulation because of their low economic return. In such situation, a designer would construct a building with nearly 100% windows that would benefit from the absence of a thermal requirement for windows. This situation should be avoided. To avoid this type of situation, a maximum limit for WWR and SRR will be set in the prescriptive approach. It is important to point out that this will not place a constraint on building design, as buildings which do not comply with the prescriptive path, can demonstrate compliance through an alternative performance path. As such, in the proposed thermal standard for Lebanon, when the proposed building exceeds a certain maximum WWR and SRR set in the standard, the designer will need to take the performance path to demonstrate compliance. This requirement would insure that the thermal envelope of the proposed building will be thermally equivalent to a building with a more conservative WWR and SRR. In the performance path, the designer will need to simulate two buildings, the proposed building and a similar reference building that has the maximum allowed WWR and SRR as prescribed in the thermal standard. The total energy requirements of the proposed building should be less than or equal to those of the reference building. In practice, this will force the designer to take certain measures such as improving the U-values of walls or windows, or protecting the windows from the summer solar loads. Whatever solution is proposed, the designer will need to be demonstrated that the modified building will not consume more energy than the reference building. It is important to note that this threshold is implemented mainly because of the thermal transmittance characteristics of windows (U value) and their effect on the overall envelope thermal performance. Other characteristics related to the solar radiation heat gain like the solar reflection and architectural protection would be covered by another requirement called effective fenestration ratio (EFR) that will be discussed in a separate section.
Energy Analysis and Economic Feasibility Study
:: 26 ::
In order to determine what the maximum threshold value should be, a series of simulations were conducted by varying the WWR from 10% to 80% for office buildings and from 10% to 35% for residential buildings. The skylight to roof ratio was not determined analytically because of the limited scope of this study. It was rather fixed by inspection at a value of 0.05. The Window-to-wall ratios used in the parametric analysis for residential and office buildings are presented in Table 15. Table 15 Parametric Alternatives of Window to Wall Ratio Case Alternative 1 Alternative 2 Alternative 3 Base Case Alternative 4 Alternative 5 Alternative 6 Alternative 7 Alternative 8 Alternative 9 Alternative10
Office Buildings 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.50 0.60 0.70 0.80
Residential Buildings 0.10 0.13 0.16 0.20 0.22 0.25 0.28 0.30 0.35 ---
Results of the WWR analysis for office buildings The results of the simulations for office buildings are presented in Figure 24. The energy usage represented here is a combination of the energy input to a building heated by a central boiler and cooled by electric air conditioner.
Figure 24 - Variation of the Energy Usage with Increasing WWR – Offices Total Energy Usage vs WWR Office Buildings 1,600
1,400
Base case 1,200
GJ
1,000 Beirut Bayssour Kartaba Zahle Cedars
800
600
400
200
0 0.10
0.15
0.20
0.25
0.30
0.35
0.40
WWR
Energy Analysis and Economic Feasibility Study
:: 27 ::
0.50
0.60
0.70
0.80
Figure 25 and Figure 26 present the variation of the heating and cooling component caused by a change in the WWR for the office building.
Figure 25 - Variation of Heating energy usage with increasing WWR - Office Heating Energy Usage vs WWR Office Buildings 1,400
1,200
Base case 1,000
Beirut Bayssour Kartaba Zahle Cedars
GJ
800
600
400
200
0 0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.50
0.60
0.70
0.80
WWR
Figure 26 - Variation of Cooling energy usage with increasing WWR - Office Cooling Energy Usage vs WWR Office Buildings 700
600
Base case 500
Beirut Bayssour Kartaba Zahle Cedars
GJ
400
300
200
100
0 0.10
0.15
0.20
0.25
0.30
0.35
0.40
WWR
Energy Analysis and Economic Feasibility Study
:: 28 ::
0.50
0.60
0.70
0.80
From these results we can observe that the energy consumption for Beirut, Bayssour and Qartaba are relatively close. However, their climates are quite different as the largest energy usage in Beirut is for cooling. In Bayssour and Qartaba, the cooling load is lower than in Beirut but the heating load is higher which makes the building energy requirement slightly higher than Beirut. For Zahle and Cedars, the heating load is much larger than the cooling load and their energy requirements are much higher. The 0.25 WWR was considered to represent base case level of WWR for non residential buildings constructed in Lebanon. The same results can be presented in a different format to help determine what should be the maximum WWR. Figure 27 presents the information in a different format. In this figure, the energy usage related to 25% WWR was put as the reference, and the percentage variation from this value was calculated.
Figure 27 - Recommended Threshold to trigger the Performance Path - Office Energy Usage Increase (%) 60%
50%
Percentage Energy Increase
40%
Recommended Threshold for Performance Approach 30% Beirut Bayssour Kartaba Zahle Cedars
20%
10%
0% 0.10
0.15
0.20
0.25
0.30
-10%
0.35
0.40
0.50
0.60
0.70
0.80
WWR
-20%
-30%
We can observe from this figure that the energy usage for a building with 0.80 WWR will be between 32% and 55% more than a building with 0.25 WWR. The determination of the maximum WWR is essentially a judgment call as there is no specific formula that can fix this value. The threshold should be high enough to allow design flexibility and to avoid too many buildings going to the performance path that is more complex and more costly to implement (and more complex to handle for the government body in charge of compliance verifications). Much of the attention was put on the curve for Beirut since the coastal zone has the largest population and buildings and the maximum cooling loads. By inspection we can find that fixing a limit of 5 % of additional consumption over a “normal” building will fix the threshold of WWR at 0.30. If we want to allow 10% more energy for the threshold, then the WWR limit will go close to 0.35. In practice, a limit of 0.30 WWR would be a reasonable recommendation for Lebanon. As mentioned previously, this is not an absolute limitation, since designers have the option of using the performance path. For skylights, the current practice in Lebanon is to have very few skylights in the buildings. No simulation were realized for this particular components but using the same logics as for windows where a 5% deviation from the current trend is made acceptable, a 0.05 skylight should be used to trigger the switch to the performance approach. Energy Analysis and Economic Feasibility Study
:: 29 ::
Results of the WWR analysis for residential buildings A similar analysis was conducted for residential buildings and the results are presented in Figures 28, 29, 30 and 31.
Figure 28 - Variation of the Total Energy Usage with Increasing WWR - Residential Total Energy Usage vs WWR Residential Buildings 900
800
Base case
700
Energy Usage (GJ)
600 Beirut Bayssour Kartaba Zahle Cedars
500
400
300
200
100
0 0.10
0.13
0.16
0.205
0.22
0.25
0.28
0.30
0.35
WWR
Figure 29 - Variation of the Heating Energy Usage with Increasing WWR - Residential Heating Energy Usage vs WWR Residential Buildings 800
700
Base case
Energy Usage (GJ)
600
500
Beirut Bayssour Kartaba Zahle Cedars
400
300
200
100
0 0.10
0.13
0.16
0.205
0.22
0.25
WWR
Energy Analysis and Economic Feasibility Study
:: 30 ::
0.28
0.30
0.35
Figure 30 - Variation of the Cooling Energy Usage with Increasing WWR - Residential Cooling Energy Usage vs WWR Residential Buildings 500 450
Base case
400
Energy Usage (GJ)
350 300
Beirut Bayssour Kartaba Zahle Cedars
250 200 150 100 50 0 0.10
0.13
0.16
0.205
0.22
0.25
0.28
0.30
0.35
WWR
Figure 31 - Recommended Threshold to trigger the Performance Path - Residential Energy Usage Increase (%) Residential Buildings 40%
Percentage energy increase
30%
Recommended Threshold for Performance Approach 20% Beirut Bayssour Kartaba Zahle Cedars
10%
0% 0.10
0.13
0.16
0.205
0.22
0.25
0.28
0.30
0.35 WWR
-10%
-20%
Energy Analysis and Economic Feasibility Study
:: 31 ::
2.5
Parametric Variations of Window Orientation and Architectural Shading
This section presents the results obtained from a set of parametric simulations conducted for different orientations to determine the effect of the overhangs, fins or a combination of these two architectural shading devices on the solar load of windows over a complete heating and cooling season. The objective of this parametric analysis was to determine suitable architectural shading factors (ASF) that can be applied in the thermal standard to determine the effective fenestration ratio (EFRReq). This architectural shading factor incorporates the effect of the orientation of the façade, where each window of the building is located, and the shading effect of fins and overhangs. The architectural shading factor will then be combined with the window to wall ratio and the glazing shading coefficient to calculate the equivalent solar heat gain effect of the window compared to a clear single glass of 6mm thickness. To perform this analysis, windows on one façade (initially oriented toward the south) have been shaded with fins and overhangs of different dimensions as presented in Table 16. Table 16 Parametric Alternatives for the Evaluation of Architectural Shading Factors Parametric run Base case – No shading Category 1 – Overhang only
Category 2 – Fins only
Category 3 – Combined Fins and overhang
Alternative 1 Alternative 2 Alternative 3 Alternative 4 Alternative 5 Alternative 6 Alternative 7 Alternative 1 Alternative 2 Alternative 3 Alternative 4 Alternative 1 Alternative 2 Alternative 3 Alternative 4
Comments No fins and no overhang on the window 0.10 projection of the overhang 0.20 projection of the overhang 0.40 projection of the overhang 0.60 projection of the overhang 0.80 projection of the overhang 1.00 projection of the overhang 1.50 projection of the overhang 0.10 projection of the overhang 0.20 projection of the overhang 0.30 projection of the overhang 0.40 projection of the overhang 0.10 overhang and 0.10 fins 0.40 overhang and 0.20 fins 0.80 overhang and 0.40 fins 1.50 overhang and 0.60 fins
The results of each simulation were then analyzed by extracting cooling and heating information for each individual space in the building that has windows oriented towards one orientation selected for analysis and by looking more specifically at the solar thermal load created by the window. These simulations were carried out with clear windows as the thermal standard will separately consider the shading characteristics of the glazing (shading coefficient) and the shading from architectural devices. The total thermal cooling load caused by windows in all these spaces was then summed up over the complete cooling season. Then the contribution of the window solar heat gain to the heating of the building in the winter season was established. Each thermal load was then converted into cooling and heating energy required at the input of the heating and cooling equipment by considering appropriate efficiency factors. The resulting architectural shading factor can then be directly related to the energy usage effect of the shading device and the orientation for a clear window of 6mm thick. This analysis provides a series of values representing the impact of the orientation of windows and the shading caused by the architectural shading devices on the building energy requirements. After the initial analysis of the south orientation of the building, the same analysis was performed for the other main orientations of the building to see the combined impact of orientation and external shading devices on the cooling and heating load. Thus eight orientations (N, NE, E, SE, S, SW, W, and NW) were analyzed to determine the architectural shading factor (ASF).
Energy Analysis and Economic Feasibility Study
:: 32 ::
The simulations were done only for Beirut on the rationale that the coastal region features the highest population and construction density. So the architectural shading factor should reflect the situation in this part of the country. It is obvious that the economics of fins and overhang will be higher in the coastal regions than in other parts of the country. It will be necessary to take that into consideration in the thermal standard. This will be done through a selection of an effective fenestration ratio (EFRreq) compatible with the economic return of architectural shading devices.
2.5.1
Overhang Analysis
Figure 33 presents the results of the parametric runs made on the overhang. The results are expressed as the architectural shading factor (ASF) which is the ratio of the annual energy usage created by the window solar gain on the orientation selected when the window is equipped with an overhang compared to the annual energy usage of an equivalent window without solar protection. The dimensions of the overhang are expressed as the projection factor (Figure 32). Figure 32 - Overhang Projection Factor Parameters
PFOverhang =
A B
PFOverhang = Projection Factor for overhang (dimensionless) A = Horizontal extension of the overhang from the vertical plane of the wall where the window is located (m) B = Distance between the bottom edge of the window and the bottom edge of the overhang (m)
We can see from Figure 33 that in the base case without overhangs, the solar cooling load and the useful solar heating gain in winter induce an energy requirement about four times lower on the north façade than on a south façade. Southeast, south and south-west façades have quite similar values and making a single recommendation for the architectural shading factor for these three orientations will make sense. Then the east and west orientations are close enough so that a single architectural shading factor could be developed for both of them. Finally, the northeast and northwest orientations can be grouped to determine a single architectural shading factor. Figure 33 - Architectural Shading Factor for Various Overhang Projections
Overhang Effect 1.20
Architectural Shading Factor
1.00
0.80
0.60
0.40
0.20
0.00 N
NE
E
SE
S
SW
W
Orientation Base case Overhang 0.80
Overhang 0.10 Overrhang 1.00
Energy Analysis and Economic Feasibility Study
Overhang 0.20 Overhang 1.50
:: 33 ::
Overhang 0.40
Overhang 0.60
NW
2.5.2
Fins Analysis
Figure 35 presents the results of the parametric runs made for the fins. The results are expressed as a ratio of the annual energy usage caused by the windows on the orientation and with the shading devices selected compared to the annual energy usage of a window on the south orientation. The dimensions of the fins are expressed as the projection factor (figure 34) Figure 34 - Fins Projection Factor Parameters
PF fins =
A B
PFfins = Projection factor for fins (dimensionless) A = Horizontal extension of the fins from the vertical plane of the wall that containing the window (m) B = Distance between the farthest side of the window to the face of fin that is close to the window (m)
Figure 35 presents the impact of fins on the architectural shading coefficient. We can see that fins are less effective than overhangs in reducing the energy usage for a building and this is understandable as they produce their main effect only when solar rays are coming from the left or right. Fins are not effective when the sun approaches the zenith (on south orientation).
Figure 35 - Fins Effect on the Architectural Shading Factor
Fins Effect 1.20
Architectural Shading Factor
1.00
0.80
0.60
0.40
0.20
0.00 N
NE
E
SE
S
SW
W
Orientation Base case
Fins 0.10
Energy Analysis and Economic Feasibility Study
Fins 0.20
:: 34 ::
Fins 0.30
Fins 0.40
NW
2.5.3
Combined Fins and Overhangs
Another series of simulations were conducted to analyze the combined impact of both fins and overhangs. The projections used for the fins and overhang are not the same. The test window used is about twice as wide as its height. To have an equal extension of the fins and overhangs from the wall plane, different projections have to be used. Figure 36 - Combined Effects of Fins and Overhang
Overhang and Fins Combined Effect 1.20
Architectural Shading Factor
1.00
0.80
0.60
0.40
0.20
0.00 N
NE
E
SE
S
SW
W
NW
Orientation Base case
2.6
Overhang 0.20, fins 0.10
Overhang 0.40, fins 0.20
Overhang 0.80, fins 0.40
Overhang 1.50, fins 0.60
Recommended Architectural Shading Factors
Tables 14, 15, 16 and 17 present the recommended values of the architectural shading factor for various configurations of fins, overhangs and for different orientations. These tables will be used directly in the thermal standard to determine the effective fenestration ratio (EFR) of a building. These architectural shading factors can be directly related to the energy requirements for a fenestration with a specific orientation and shading device compared to the energy consumption created by a clear single glass window with the same orientation but without shading devices. Table 17 should be used for windows that have no architectural shading devices i.e. without overhangs and fins. This Table just takes into consideration the orientation of the window to determine its impact on building energy consumption. For instance, an unprotected window facing north will induce 26% of the energy usage that will require a window of the same size, unprotected and facing south. If the orientation is centered between two orientations provided in the Table, the most conservative (higher) architectural shading factor should be used. Table 18 provides the values for the architectural shading factor for windows protected by overhangs only. The projection of each overhang should be calculated individually and applied to the specific window it protects. If the orientation is centered between two orientations provided in the Table, the most conservative (higher) architectural shading factor should be used.
Energy Analysis and Economic Feasibility Study
:: 35 ::
Table 19 provides the values for the architectural shading factor for windows protected by fins only. The projection of each fin should be calculated individually and applied to the specific window it protects. If the orientation is centered between two orientations provided in the Table, the most conservative (higher) architectural shading factor should be used. Table 20 provides the values for the architectural shading factor for windows simultaneously protected by fins and overhangs. The Table assumes that the horizontal extension will be the same for fins and overhangs if the window is twice as large as its height. The projection of each protection should be calculated individually and applied to the specific window it protects. For projection factors falling between the values provided in the table, the closest projection factor for the overhang should be used. If the orientation is centered between two orientations provided in the Table, the most conservative (higher) architectural shading factor should be used. Table 17 Architectural Shading Factor for Unprotected Windows PF - Fins or Overhangs PF < 0.05
N 0.26
ASF per Orientation NE,NW E,W 0.47 0.82
S,SE,SW 1.00
Table 18 Architectural Shading Factor for Windows Protected by Overhang Only PF - Overhangs 0.05 ≤ PF < 0.15 0.15 ≤ PF < 0.30 0.30 ≤ PF < 0.50 0.50 ≤ PF < 0.70 0.70 ≤ PF < 0.90 0.90 ≤ PF < 1.25 PF ≥ 1.25
N 0.24 0.23 0.21 0.19 0.18 0.17 0.16
ASF per Orientation NE,NW E,W 0.43 0.74 0.40 0.68 0.34 0.57 0.31 0.49 0.28 0.43 0.26 0.38 0.24 0.31
S,SE,SW 0.89 0.80 0.64 0.54 0.46 0.41 0.34
Table 19 Architectural Shading Factor for Windows Protected by Fins Only PF - Fins 0.05 ≤ PF < 0.15 0.15 ≤ PF < 0.25 0.25 ≤ PF < 0.35 PF ≥ 0.35
N 0.23 0.20 0.19 0.17
ASF per Orientation NE,NW E,W 0.42 0.76 0.38 0.71 0.35 0.67 0.32 0.63
S,SE,SW 0.92 0.85 0.78 0.74
Table 20 Architectural Shading Factor for Windows Protected by Fins and Overhang PF – Fins and Overhangs Overhangs: 0.05 ≤ PF < 0.30 Fins: 0.05 ≤ PF < 0.15 Overhangs: 0.30 ≤ PF < 0.60 Fins: 0.15 ≤ PF < 0.30 Overhangs: 0.60 ≤ PF < 1.05 Fins: 0.30 ≤ PF < 0.50 Overhangs: PF ≥ 1.05 Fins: PF ≥ 0.50
N
ASF per Orientation NE,NW E,W
S,SE,SW
0.20
0.35
0.63
0.72
0.15
0.26
0.47
0.50
0.11
0.17
0.30
0.27
0.08
0.11
0.17
0.13
Energy Analysis and Economic Feasibility Study
:: 36 ::
2.7
Selected Combined Simulations for Walls, Roofs and Windows
Selected combined simulations have been realized to explore the simultaneous impact of various energy conservation measures. This is important as the interaction among measures can sometimes be negative (a combined measure may yield less results than the individual measure) or sometimes be positive (the combination provides better savings). For instance, the combination of wall and roof insulation usually leads to a lower energy savings than the individual simulation of savings for the two components because they both have a tendency to reduce the heating season. On the other hand, an example of a beneficial combination would be the combination of reflective windows and architectural shading with wall insulation. As the reflective window and architectural shading will reduce the solar heat gain in winter, the insulation of walls usually becomes more cost effective. The reflective window and architectural shading will also reduce the negative effect of wall insulation observed during the cooling season as the solar load will be reduced during summer and mid-season. Table 21 presents the parameters used for the office and residential buildings. The base case is the same as the one used in the individual analysis. It corresponds to a non-insulated building with single clear glazing. Model 1 combines the insulation of roof, reflective glazing and architectural overhang on the south, southeast and south-west side. This model is optimized for coastal regions. Model 2 is a combination of roof and wall insulation together with a limitation of the solar heat gain in summer by a combination of reflective glazing and overhang. This model is a compromise anticipated for regions with higher heating loads. Table 21 Combined Simulation Parameters Combined Simulations for Offices and Residential Buildings Walls Roofs Windows Overhang
Base Case
Model 1
Model 2
Single, non insulated U=3.432 W/m2 C Non-insulated U=2.556 W/m2C Single 6 mm clear U=6.160 W/m2C
Single, non insulated U=3.432 W/m2 C Insulated 6 cm. EPS U=0.524 W/m2C Single reflective with SC=0.45 U=5.490 W/m2C
Double, insulated with 6 cm EPS U=0.500 W/m2 C Insulated 6 cm EPS U=0.524 W/m2C Single reflective with SC=0.45% U=5.490 W/m2C
No overhang
PF = 0.40 for E,W,S side
PF = 0.40 for E,W,S side
Results for office buildings Figure 37 presents the results of the combined simulations for the five weather stations. We can see that the model 1 building provides savings in all zones except in Cedars where the reduction in solar heat gain results in a net increase (3%) of the total energy usage. For Beirut, the model 1 building provides 17% energy savings. In the other locations (Bayssour, Qartaba and Zahle) the savings range from 0.8% to 3.4% of the annual heating and cooling energy usage. The results confirm what was expected i.e. that the model 1 building is more adapted for the coastal regions. The model 2 building (with wall insulation) performs quite well in all regions. The total savings in Beirut are 28.8% while the savings in Bayssour, Qartaba, Zahle and Cedars vary from 28% to 32%. This is due to the fact that the glazing and overhang for the model 2 building reduce the negative impact of wall insulation during the summer and mid-season which results in larger savings that reflect on the economic analysis. This is an improvement over the individual runs but the impact is not drastic as the combined simulations provide an added reduction of 5.7% of the total building energy consumption over the total of what will be provided by the summation of the individual measure results.
Energy Analysis and Economic Feasibility Study
:: 37 ::
Figure 37 - Combined Measure Results in Office Buildings Combined Measures Office Buildings 1,800
1,600
1,400
Energy Usage (GJ)
1,200 Beirut Bayssour Kartaba Zahle Cedars
1,000
800
600
400
200
0 Base
Mod 1
Mod 2
Building Model
Results for residential buildings Figure 38 presents the results of the combined simulations for the residential buildings. As for the office buildings, both models provide savings with the notable exception of Cedars where the model 1 results in an increase of energy consumption. This is due to the large reduction of positive solar heat gain during the heating season caused by the reflective windows included in the model 1 building. We can also see that the model 1 produces a more important energy reduction in Beirut than in Qartaba, Bayssour or Zahle as the reflective windows are more effective in the coastal areas. If we compare the combined results with the results of the individual simulations, we observe that for Beirut, the model 1 building provides a reduction in energy usage of 21.1 % while the model 2 provides a reduction of 30.5 %. In the regions of Bayssour, Qartaba and Zahle, the model 1 building provides 3.7% to 4% savings. The model 2 building in these zones ranges provides savings between 48% and 52%. For the other regions, we also observe that the model 2 building provides more savings than the individual measures as it allows the achievement of a better balance between the solar heat gain and the building losses over the heating and cooling seasons. For the Cedars, this large difference comes from the fact that the combination of measures corrects the negative effect of reflective windows which produce a negative impact if applied alone. Figure 38 - Combined Measure Results Residential Buildings Combined Measures Residential Buildings 1,400
1,200
Energy Usage (GJ)
1,000
Beirut Bayssour Kartaba Zahle Cedars
800
600
400
200
0 Base
Mod 1 Building model
Energy Analysis and Economic Feasibility Study
:: 38 ::
Mod 2
3
ECONOMIC ANALYSIS
The Net Present Value of the savings associated with the conservation measures was evaluated (over 20 years) for each of the building types in each climatic zone, changing one parameter at a time. Then a combined simulation was made to see the interaction between the most promising measures. The calculations presented in this section are based on scenario number 4 (preferred scenario). The Results of the economic analysis and the resulting Net Present Value will be presented per climatic zone. The results were examined to determine the optimum level of roof and wall insulation in each climatic zone, and to note the difference in optimum U values according to building types.
3.1
Economic Results of Roof U-value
For the office buildings, the optimum insulation level and the corresponding NPV are presented in Tables 22 and 23. The first table presents the results as predicted by DOE2 but not taking into consideration the user’s behavior (i.e increasing natural ventilation to evacuate excess heat when necessary). The second table is corrected to consider that no additional cooling load will be created due to the roof insulation. The occupants are expected to open the windows to evacuate any extra heat. For the office building, the result is the same for all regions except the High Mountain. Even for the High Mountain region, the difference is small as the effect of insulation on cooling is close to neutral. Table 22 Optimum Roof U-value (with additional cooling load) – Office Buildings Climatic Zone 1- Coastal 2- Western Mid-mountain 3- Inland Plateau 4- High Mountain
Reference City Beirut Qartaba Zahle Cedars
Roof U Value (W/m2.K) 0.524 0.713 0.524 0.415
Typical EP Thickness (cm) 6 4 6 8
Direct Payback (Year) 4.8 3.3 3.2 1.9
NPV (USD)
(USD/m2)
1,232 1,764 2,624 6,250
0.64 0.92 1.37 3.26
Table 23 Optimum Roof U-value (without additional cooling load) – Office Buildings Climatic Zone 1- Coastal 2- Western Mid-mountain 3- Inland Plateau 4- High Mountain
Reference City Beirut Qartaba Zahle Cedars
Roof U Value (W/m2.K) 0.524 0.713 0.524 0.415
Typical EP Thickness (cm) 6 4 6 8
Direct Payback (Year) 4.8 3.3 3.2 1.8
NPV (USD)
(USD/m2)
1,232 1,764 2,624 6,493
0.64 0.92 1.37 3.38
We can observe that the negative air conditioning effect that can result from a better insulation of envelope has no effect on the recommended level of insulation. The NPV increases slightly in the high mountain region when we do not take the air conditioning effect into consideration.
Energy Analysis and Economic Feasibility Study
:: 39 ::
For the residential buildings, the results are summarized in Tables 24 and 25. In this case also, there are some differences between the results for the High Mountain zone but it is also small. A sensitivity analysis was made to vary the efficiency of the cooling system between a 0.32 kW input per kW cooling and an efficiency of 0.37 kW input per kW cooling. The first one would be typical of a good efficiency unit. The last one would be more representative of a low efficiency product. The sensitivity analysis shows that the thickness of insulation does not change with the efficiency (the variation in savings is not enough to make the insulation recommendation move by the minimum increment of 2 cm considered in the analysis). The payback is however shorter and the NPV higher. The results presented herewith are for an efficiency of 0.37 kW input to kW cooling effect. Table 24 Optimum Roof U-value (with additional cooling) – Residential Buildings Climatic Zone 1- Coastal 2- Western Mid-mountain 3- Inland Plateau 4- High Mountain
Reference City Beirut Qartaba Zahle Cedars
Roof U Value (W/m2.K) 0.524 0.524 0.524 0.415
Typical EP Thickness (cm) 6 6 6 8
Direct Payback (Year) 1.3 1.6 1.1 0.9
NPV (USD)
(USD/m2)
3,487 3,470 4,702 7,120
2.50 2.49 3.37 5.11
Table 25 Optimum Roof U-value (without additional cooling) – Residential Buildings Climatic Zone 1- Coastal 2- Western Mid-mountain 3- Inland Plateau 4- High Mountain
3.2
Reference City Beirut Qartaba Zahle Cedars
Roof U Value 2 (W/m .K) 0.524 0.524 0.524 0.415
Typical EP Thickness (cm) 6 6 6 8
Direct Payback (Year) 1.3 1.6 1.1 0.9
NPV (USD)
(USD/m2)
3,487 3,470 4,702 7,260
2.50 2.49 3.37 5.21
Economic Results of Wall U-value
For the office buildings, the optimum insulation levels and the corresponding NPV are presented in Tables 26 and 27. The first Table presents the results as predicted by DOE2 but not taking into consideration the users’ behavior (i.e increasing natural ventilation to evacuate excess heat when necessary). The second table is corrected to consider that no additional cooling load will be created due to the wall insulation since the occupants are assumed to open the windows to evacuate any extra heat. These simulation results are for the wall insulation alone and show that there is no economic return to insulate walls in the coastal climate of Lebanon. Some insulation can be justified economically for the other regions. The set of results of presented in Table 27 are considered more representative of the reality and thus will be retained for setting the thermal standard requirements. This result also considers an efficiency of 0.37 kW input to kW cooling. Table 26 Optimum U-value for Walls (with additional cooling) – Office Buildings Climatic Zone 1- Coastal 2- Western Mid-mountain 3- Inland Plateau 4- High Mountain
Reference City Beirut Qartaba Zahle Cedars
Wall U Value (W/m2.K) NA 1.011 0.669 0.500
Typical EP Thickness (cm)
Direct Payback (Year)
(USD)
(USD/m2)
2 4 6
7.3 4.7 2.2
637 4,503 16,444
0.33 2.35 8.56
NPV
Table 27 Optimum U-value for Walls (without additional cooling) – Office Buildings Climatic Zone 1- Coastal 2- Western Mid-mountain 3- Inland Plateau 4- High Mountain
Reference City Beirut Qartaba Zahle Cedars
Wall U Value (W/m2.K) NA 0.669 0.669 0.399
Energy Analysis and Economic Feasibility Study
Typical EP Thickness (cm)
Direct Payback (Year)
(USD)
(USD/m2)
4 4 8
6.7 4.0 2.2
1,464 6,206 18,783
0.76 3.23 9.78
:: 40 ::
NPV
Tables 28 and 29 present the results for residential buildings. The results show a higher requirement for the thermal insulation compared to the office buildings because of the longer operating hours. One difference is that there is a requirement for Beirut in both tables. The other regions have the same requirement for the scenario with and without added AC. The results of Table 29 will be used as a guideline to set the requirements of the thermal standard. Table 28 Optimum U-value for Walls (with additional cooling) – Residential Buildings Climatic Zone 1- Coastal 2- Western Mid-mountain 3- Inland Plateau 4- High Mountain
Reference City Beirut Qartaba Zahle Cedars
Wall U Value (W/m2.K) 0.669 0.500 0.500 0.399
Typical EP Thickness (cm) 4 6 6 8
Direct Payback (Year) 2.8 2.0 1.0 0.8
NPV (USD)
(USD/m2)
4,983 10,919 18,877 30,442
3.57 7.83 13.54 21.84
Table 29 Optimum U-value for Walls (without additional cooling) – Residential Buildings Climatic Zone 1- Coastal 2- Western Mid-mountain 3- Inland Plateau 4- High Mountain
3.3
Reference City Beirut Qartaba Zahle Cedars
Wall U Value (W/m2.K) 0.669 0.669 0.500 0.399
Typical EP Thickness (cm) 4 6 6 8
Direct Payback (Year) 2.8 2.0 1.0 0.7
NPV (USD)
(USD/m2)
4,983 10,919 18,877 32,975
3.57 7.83 13.54 23.65
Economic Results of Window U-value and Shading Coefficient
For the office buildings, the optimum U-values for glazing and the corresponding NPV are presented in Table 30. In this case, all windows with a positive NPV are presented instead of just the ones providing maximum NPV. With respect to windows, the decision of what to take into consideration is more complex than for roof and wall U-value. Windows are considered under two separate requirements: first, the requirement for the thermal transmittance (U-value) and second the requirement for the shading characteristic when calculating the effective fenestration ratio. If we want to first tackle U-value requirement, we can compare in the table 30 the three following options with clear glass where just the U value is a factor in the economic analysis. • • •
Window type 4 : Single glass with low-e coating (U = 4,27 W/m2.K) Window type 5: Double glass (U = 2,739 W/m2.K) Window type 8: Double clear glass with low-e coating (U = 1,956 W/m2.K)
For the Coastal region, none of these three types of windows are cost effective options. So there should be no U-value requirement for windows in this climate. For Qartaba and Zahle, just the Single glass low-e is an option which suggests that the requirement should be put near the 4.27 W/m2.K value. For the high mountain region, the double window is a cost effective option and even if its NPV is less than the single glass with low-e coating, it is still acceptable as the positive NPV suggests. It would be appropriate to put a more stringent requirement on Cedars than on Zahle and Qartaba so a thermal conductance of about 2.8 W/m2.K should be an appropriate requirement. Referring to the residential results presented in Table 31, we observe that more types of windows are providing an economic return in this scenario. As for the U-value requirement for the different regions, they will be similar to the office buildings with no requirement in the Beirut area; a U-value equivalent to a low-e single glazing in the western mid-mountainous and inland climatic zones, and a requirement equivalent to a clear double glazing in the high mountain zones. We can notice that several glazing also have a negative payback meaning that they have an immediate return on investment. In these cases, the incremental cost of the selected glazing is covered by the reduction in size and cost of the mechanical equipment used to provide heating and cooling.
Energy Analysis and Economic Feasibility Study
:: 41 ::
Table 30 Optimum Performance of Windows (with additional cooling) – Office buildings Climatic Zone 1- Coastal
2- Western Midmountain
3- Inland Plateau
4- High Mountain
Window Composition
Tint
Reflective
Single (6 mm) Single (6 mm) Single (6 mm) Double (6/12/6 mm) Double (6/12/6 mm) Single (6mm) Single (6mm) Single (6mm) Single (6mm) Double (6/12/6 mm) Double (6/12/6 mm) Single (6 mm) Single (6 mm) Single (6 mm) Single (6 mm) Double (6/12/6 mm) Double (6/12/6 mm) Single (6 mm) Double (6/12/6 mm) Double (6/12/6 mm) Double (6/12/6 mm)
Bronze Clear Clear Bronze Clear Bronze Clear Clear Clear Bronze Clear Bronze Clear Clear Clear Bronze Clear Clear Clear Bronze Clear
--Tin-Oxide Titanium --Stainless H --Tin-Oxide Titanium ----Stainless H --Tin-Oxide Titanium ----Stainless H ---------
Characteristics Low-E Filling ----------------e = 0.2 ----------e = 0.2 ----e = 0.2 ----e = 0.2
------Air Air --------Air Air --------Air Air --Air Air Air
U value (W/m2.K) 6.163 6.124 5.500 2.739 2.495 6.163 6.124 5.500 4.270 2.739 2.495 6.163 6.124 5.500 4.270 2.739 2.495 4.270 2.739 2.739 1.956
SC (%) 0.71 0.58 0.45 0.57 0.26 0.71 0.58 0.45 0.84 0.57 0.26 0.71 0.58 0.45 0.84 0.57 0.26 0.84 0.81 0.57 0.78
Payback
NPV
(Years)
(USD)
0.71 1.32 1.62 6.95 4.80 1.09 2.46 2.61 6.63 8.01 6.92 -0.52 -0.06 0.45 2.30 4.17 3.70 2.21 6.51 7.42 7.19
9637 13903 17126 2594 12067 5820 6344 8793 896 506 3246 9207 12080 16560 6366 10074 15685 8408 3100 1479 2475
Table 31 Optimum Performance of Windows (without additional cooling) –Residential buildings Climatic Zone 1- Coastal
2- Western Midmountain
3- Inland Plateau
4- High Mountain
Window Composition
Tint
Reflective
Characteristics Low-E Filling
Single (6 mm) Single (6 mm) Single (6 mm) Single (6 mm) Double (6/12/6 mm) Double (6/12/6 mm) Single (6 mm) Single (6 mm) Single (6 mm) Single (6 mm) Double (6/12/6 mm) Double (6/12/6 mm) Single (6 mm) Single (6 mm) Single (6 mm) Single (6 mm) Double (6/12/6 mm) Double (6/12/6 mm) Double (6/12/6 mm) Single (6 mm) Single (6 mm) Double (6/12/6 mm) Double (6/12/6 mm) Double (6/12/6 mm) Double (6/12/6 mm)
Bronze Clear Clear Clear Bronze Clear Bronze Clear Clear Clear Bronze Clear Bronze Clear Clear Clear Bronze Clear Clear Clear Clear Clear Bronze Clear Clear
--Tin-Oxide Titanium ----Stainless H --Tin-Oxide Titanium ----Stainless H --Tin-Oxide Titanium ----Stainless H --Titanium ------Stainless H ---
------e = 0.2 ----------e = 0.2 ----------e = 0.2 ----e = 0.2 --e = 0.2 ------e = 0.2
Energy Analysis and Economic Feasibility Study
:: 42 ::
--------Air Air --------Air Air --------Air Air Air ----Air Air Air Air
U value (W/m2.K) 6.163 6.124 5.500 4.270 2.739 2.495 6.163 6.124 5.500 4.270 2.739 2.495 6.163 6.124 5.500 4.270 2.739 2.495 1.956 5.500 4.270 2.739 2.739 2.495 1.956
SC (%) 0.71 0.58 0.45 0.84 0.57 0.26 0.71 0.58 0.45 0.84 0.57 0.26 0.71 0.58 0.45 0.84 0.57 0.26 0.78 0.45 0.84 0.81 0.57 0.26 0.78
Payback
NPV
(Years)
(USD)
0.0 0.4 0.6 5.0 4.2 2.9 -0.1 0.8 0.8 3.4 4.7 3.9 -2.1 -2.4 -1.6 0.8 2.2 1.2 8.3 4.7 1.0 4.0 3.9 5.0 0.0
10413 15417 19253 1350 7275 17561 6815 7924 11245 2261 5456 9668 8387 11405 15951 6450 12024 19034 70 965 9134 7034 7235 5443 7489
3.4
Economic Results of Architectural Shading (Fins and Overhangs)
For fins and overhangs, the economic return will vary according to the location and orientation of the window. The optimum economic result of the various considered projections were analyzed for different orientations. The Results of this economic analysis will not be used directly to put mandatory requirement for fins and overhangs in the thermal standard as it will be too limitative of the architectural expression to force a specific type and form of architectural shading. The architects have other options to control the solar heat gain. For instance a combination of window area, orientation and glazing shading coefficient can be added to the architectural shading to provide an optimum design. So the results of this section will be analyzed later in combination with the glass shading coefficient to determine a reasonable effective fenestration ratio (EFRreq).
Results for fins and overhangs – Office buildings Tables 32, 33, 34 and 35 present the results for each of the four climatic zones considered. The Tables provide information about the optimum economic level or the upper limit of the parametric analysis which is a projection factor of 1.50 for overhangs and 0.60 for fins. The NPV is also mentioned near the optimum projection level. We can observe from these figures that the fins and overhangs measures are very profitable in the coastal region. In fact, on several orientations, the most cost effective option was the upper limit of the range of parametric options. In the coastal zone, some shading on the north side is cost effective even if it just reduces the diffuse solar component and the direct component from north east in the early morning and north west in the late afternoon. In the western mid-mountain and inland zone, the projection for overhang is about half the one for the coastal zone and the NPV is two to three times lower. This is understandable as the solar heat gain is more beneficial in this region during the winter time. In the high mountain zone, where the solar heat gain is beneficial most of the year, only a minimum of overhang shading makes sense on the eastern, southern and western orientation. This minimum overhang will just slightly reduce the solar heat gain component when the sun is high over the horizon in summer but will let most of the direct solar heat gain component get into the building in winter period.
Table 32 Optimum Projection and Net Present Value – Office Building, Climatic Zone 1
Overhang Projection NPV (USD) Payback Fins Projection NPV (USD) Payback Combined Projection NPV (USD) Payback
Orientation SE S
N
NE
E
0.40 511 4.5
1.00 2311 3.3
1.50 6979 2.0
1.50 9491 1.6
0.20 642 4.0
0.40 1404 3.9
0.40 1476 3.8
0.40 0.20 891 4.8
0.80 0.40 3400 3.5
1.50 0.60 7845 2.9
Energy Analysis and Economic Feasibility Study
SW
W
NW
1.50 9006 1.6
1.50 9400 1.3
1.50 6507 1.7
0.80 2102 2.7
0.60 2472 3.6
0.60 2768 3.4
0.40 2259 2.8
0.20 1129 2.9
0.20 905 3.2
1.50 0.60 11221 2.2
1.50 0.60 10722 2.3
1.50 0.60 8496 2.0
1.50 0.60 7016 2.8
0.80 0.40 2751 3.7
:: 43 ::
Table 33 Optimum Projection and Net Present Value – Office Building, Climatic Zone 2
Overhang Projection NPV (USD) Payback Fins Projection NPV (USD) Payback Combined Projection
Orientation SE S
N
NE
E
---
0.40 1067 2.9
1.00 3646 2.4
0.80 4672 1.7
0.10 23 7.7
0.20 398 5.0
0.20 199 6.3
0.40 0.20 1282 3.9
0.80 0.40 2853 3.8
---
NPV (USD) Payback
SW
W
NW
0.60 3722 1.6
0.80 4047 1.3
0.80 3119 1.6
0.40 991 2.2
0.20 993 3.1
0.20 944 3.3
0.20 814 3.3
0.20 185 6.3
0.10 109 6.0
0.80 0.40 4790 2.8
0.40 0.20 3847 2.0
0.40 0.20 721 1.5
0.40 0.20 2278 2.2
0.40 0.20 1003 4.0
SW
W
NW
Table 34 Optimum Projection and Net Present Value – Office Building, Climatic Zone 3
Overhang Projection NPV (USD) Payback Fins Projection NPV (USD) Payback Combined Projection NPV (USD) Payback
Orientation SE S
N
NE
E
1.00 1991 4.0
1.00 8377 2.8
1.50 15450 2.4
1.00 12557 2.1
0.80 5462 2.0
1.00 12500 2.1
1.50 15200 2.4
1.00 8300 2.8
0.20 855 4.4
0.60 4151 3.2
0.60 4961 2.6
0.60 5341 2.2
0.20 1401 2.0
---
---
---
0.40 0.20 2388 3.8
0.80 0.40 9730 3.0
1.50 0.60 16181 2.5
0.80 0.40 14030 2.3
0.80 0.40 5870 2.1
0.80 0.40 14000 2.3
1.50 0.60 16000 2.3
1.50 0.60 16000 2.6
SW
W
NW
Table 35 Optimum Projection and Net Present Value – Office Building, Climatic Zone 4
Overhang Projection NPV (USD) Payback Fins Projection NPV (USD) Payback Combined Projection NPV (USD) Payback
Orientation SE S
N
NE
E
---
---
0.40 369 4.9
0.20 334 3.5
0.10 275 1.6
0.20 421 1.0
0.20 191 2.4
---
---
---
---
---
---
---
---
---
---
---
---
---
---
---
---
---
Energy Analysis and Economic Feasibility Study
:: 44 ::
Results for fins and overhangs – Residential buildings Tables 36, 37, 38 and 39 present the results for each of the four climatic zones considered. We can observe a similarity with the office results for the coastal zone. The highest limit in the parametric analysis was often the most economic. It reflects the context of a high solar load in a region where the cooling season is long and heating season short. The maximum value for fins in residential buildings has been put to 60% as it more closely reflects the form factor of windows for this category of buildings. In the coastal zone, some shading on the north side is cost effective even if it just reduces the diffuse solar component and the direct component from north east in the early morning and north west in the late afternoon (the NPV for fins is higher than for overhang). In the western mid-mountain and inland zone, the optimum projection for overhang is about two third the one for the coastal zone so it is slightly more cost effective than for office buildings. In the high mountain zone where the solar heat gain is beneficial most of the year, only a minimum of overhang shading makes sense on the eastern, southern and western orientation. This minimum overhang will just slightly reduce the solar heat gain component when the sun is high over the horizon in summer but will let most of the direct solar heat gain component get into the building in the winter period. In the case of the high-mountain zone, we observe also that the shading optimal levels are a bit higher than for office buildings. Several NPV are just near the border (0-value) so the table is not symmetrical due to the fact that the weather file represents real weather and the peak days on each orientation are not necessarily the same when the building is rotated.
Table 36 Optimum Projection and Net Present Value – Residential Building, Climatic Zone 1
Overhang Projection NPV (USD) Payback Fins Projection NPV (USD) Payback Combined Projection NPV (USD) Payback
Orientation SE S
N
NE
E
SW
W
NW
0.60 671 4.8
1.00 2838 2.9
1.50 8969 1.6
1.50 11687 1.3
1.50 11524 1.2
1.50 13064 0.3
1.50 11576 immediate
1.00 4005 0.7
0.40 1481 3.7
0.60 3759 2.7
0.60 3991 2.6
0.60 6125 1.8
0.60 6964 1.5
0.60 6874 0.9
0.60 4322 2.2
0.60 4625 1.4
0.40 0.40 2070 3.9
0.60 0.60 6283 2.5
0.60 0.60 10341 1.6
0.60 0.60 14988 1.1
0.60 0.60 15874 1.0
0.60 0.60 13425 0.1
0.60 0.60 12208 0.5
0.60 0.60 8136 0.7
Table 37 Optimum Projection and Net Present Value – Residential Building, Climatic Zone 2
Overhang Projection NPV (USD) Payback Fins Projection NPV (USD) Payback Combined Projection NPV (USD) Payback
Orientation SE S
N
NE
E
0.40 435 4.2
0.60 1996 2.3
1.50 5981 2.1
1.00 5783 1.6
0.60 4669 1.1
0.20 1124 1.8
0.60 2118 3.7
0.40 977 4.5
0.60 2889 3.1
0.20 0.20 1413 2.2
0.60 0.60 3891 3.3
0.60 0.60 5248 2.6
0.60 0.60 7528 2.0
Energy Analysis and Economic Feasibility Study
:: 45 ::
SW
W
NW
0.80 7092
1.50 8341
0.80 3286
immediate
immediate
immediate
0.40 2055 2.9
0.60 3399 2.0
0.60 1401 4.3
0.60 3029 1.8
0.40 0.40 5468 1.9
0.60 0.60 4241 0.3
0.60 0.60 7033 0.8
0.60 0.60 5820 0.9
Table 38 Optimum Projection and Net Present Value – Residential Building, Climatic Zone 3
Overhang Projection NPV (USD) Payback Fins Projection NPV (USD) Payback Combined Projection NPV (USD) Payback
Orientation SE S
N
NE
E
SW
W
NW
0.60 537 3.8
1.00 2270 2.3
1.50 7175 1.2
1.50 9350 1.0
1.50 9220 1.0
1.50 10451 0.3
1.50 9261 0.3
1.00 3204 0.5
0.40 1185 2.9
0.60 3007 2.1
0.60 3192 2.1
0.60 4900 1.5
0.60 5571 1.2
0.60 5499 0.7
0.60 3457 1.8
0.60 3700 1.1
0.40 0.40 1656 3.1
0.60 0.60 5027 2.0
0.60 0.60 8273 1.3
0.60 0.60 11990 0.9
0.60 0.60 12699 0.8
0.60 0.60 10740 0.0
0.60 0.60 9767 0.4
0.60 0.60 6508 0.6
Table 39 Optimum Projection and Net Present Value – Residential Building, Climatic Zone 4
Overhang Projection NPV (USD) Payback Fins Projection NPV (USD) Payback Combined Projection
NE
E
---
0.40 493 4.2
1.00 1798 0.0
0.40 968 2.3
0.20 581 1.2
0.20 45 7.7
---
0.10 6 8.2
---
0.20 0.20 308 5.9
0.20 0.20 30 8.0
0.20 0.20 401 5.1
0.20 0.10 98 6.1
0.10 4 8.2
---
NPV (USD) Payback
3.5
Orientation SE S
N
SW
W
NW
0.60 1953
1.00 3990 0.0
0.60 1407
immediate
0.40 387 0.0
---
immediate
---
0.40 433 0.0
0.40 0.40 1010 0.0
0.40 0.40 1508 0.0
Economic Results of Selected Combined Measures
The economic analysis of the combined energy conservation measures was realized to see if the combination of some measures could lead to better savings than individual measures.
Office building results Table 40 presents the result for the combined analysis for the office building. The combination of selected measures always provides a positive NPV. This means that as a group, the measures provide a good return on investment but it does not mean that all measures constituting the group have an equal saving. If we examine more closely the result for the Coastal zone, building model 2, we find a NPV of US$9,026 that seems interesting at first glance. However, if we compare with the results of individual measures we can see that the windows and overhangs are the ones that are highly positive with US$11,953 of savings, while the wall insulation is negative. The roof insulation also allows some savings while the wall insulation does not. So the combination of measures in a coastal climate does not seem to bring much advantage, especially the combination of wall and roof insulation.
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If we make an analysis for the high mountain zone, the results are quite different than the coastal zone. First, we notice that the NPV of the combined measures is US$17,364. This is higher than the sum of individual measures which is US$9,026. Furthermore, if we analyze just the difference for model 1 and 2 that correspond to the wall insulation only, we now find that it has a much higher NPV than when calculated individually. The NPV of the wall measures implemented after the roof and windows optimization allows an additional US$17,024. In the inland region, the total of all individual measures was US$11,300. The total for the combined measures is US$14,392 for model 1 so the combination better performs than the individual measures. When we analyze in detail the difference between model 1 and 2 building, it is possible to isolate the NPV for the wall measure only. In this case, the model 2 NPV is larger than the model 1 NPV so the wall measure has a positive NPV of US$ 8,078.
Table 40 Combination Measures Economic Analysis – Office Buildings NPV (USD)
1 - Coastal
2 - Western Mid Mountain
3 - Inland Plateau
4 - High Mountain
19,942
9,069
14,392
340
9,026
11,004
22,470
17,364
Base Case Wall: Single, non insulated, U=3.432 W/m2 C Roof: Non-insulated, U=2.556 W/m2C Window: Single 6 mm clear, U=6.160 W/m2C Overhang: No overhang
Model 1 Wall: Single, non insulated, U=3.432 W/m2C Roof: Insulated 6 cm. EPS, U=0.524 W/m2C Window: Single reflective, SC=0.45, U=5.490 W/m2C Overhang: PF = 0.40 for E,W,S side
Model 2 Wall: Double, insulated 6 cm EPS, U=0.500 W/m2C Roof: Insulated, 6 cm EPS, U=0.524 W/m2C Window: Single reflective, SC=0.45%, U=5.490 W/m2C Overhang: PF = 0.40 for E,W,S side
Residential building results For the residential buildings all combined results provide a positive NPV but the detailed analysis led to the conclusion that the difference in result is small and does not affect substantially the recommendation. The analysis for the coastal zone and the western mid-mountain showed a negative result for the wall alone (difference between the NPV of model 1 and 2). For the inland plateau and high mountain, the two models perform well and the wall insulation is economically attractive. Table 41 presents the result for the combined analysis for residential buildings. Table 41 Combined Measures Economic Analysis - Residential Buildings NPV (USD)
1 - Coastal
2 - Western Mid Mountain
3 - Inland Plateau
4 - High Mountain
26,124
13,871
16,098
3,482
25,695
11,007
31,497
16,855
Base Case Wall: Single, non insulated, U=3.432 W/m2 C Roof: Non-insulated, U=2.556 W/m2C Window: Single 6 mm clear, U=6.160 W/m2C Overhang: No overhang
Model 1 Wall: Single, non insulated, U=3.432 W/m2C Roof: Insulated 6 cm. EPS, U=0.524 W/m2C Window: Single reflective, SC=0.45, U=5.490 W/m2C Overhang: PF = 0.40 for E,W,S side
Model 2 Wall: Double, insulated 6 cm EPS, U=0.500 W/m2C Roof: Insulated, 6 cm EPS, U=0.524 W/m2C Window: Single reflective, SC=0.45%, U=5.490 W/m2C Overhang: PF = 0.40 for E,W,S side
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3.6
Sensitivity Analysis
This section presents a sensitivity analysis from building to building and for different economic scenarios. Ranges of optimum insulation levels and glazing characteristics for office buildings in each climatic zone are presented in Table 42. The ranges shown are the ones obtained from the nine scenarios used in the sensitivity analysis and demonstrate how the economic parameters used affect the recommendation. It is important to note that the maximum value of the NPV varies from scenario to scenario, as one would expect: highest NPV for high fuel price and low discount factor, and lowest NPV for low fuel price and high discount factor. The results shown are for the economic analysis without the impact of insulation on additional air conditioning. For the roof insulation, we can see that the sensitivity analysis produces small changes in the level of insulation recommended (equivalent to 2 cm to 8cm thickness of expanded polystyrene). For the wall, all scenarios in the Coastal region prove that insulation is not economical for these regions. In zones 2 and 3, the sensitivity of the recommendation to the economic parameter variation is high with a range from no insulation at all to 6 cm equivalent of polystyrene (0.500 U value). For the glazing, we can see that the economic analysis is not quite sensitive to a variation in the economic parameters. For zones 1 and 2, the economic analysis result for the optimum glazing indicate the same type of window, the single clear window with reflective coating to reduce solar heat gain. In Cedars also, one type of glazing is the optimum selection whatever the scenario used. In this case, it corresponds to the single glazing with low-e coating (good for large heating load). In the Inland zone, the optimum selection varies from 0.45 to 0.26 in shading coefficient and includes some single and double glazing window which explains the variation in thermal transmittance values obtained. Table 42 Range in Optimum Shell Properties to Maximize NPV (for all 9 scenarios) – Office Climate Zone
Roof U value W/m2.K
Wall U value W/m2.K
1 – Coastal
0.713 (2 cm. EPS) to 0.524 (6 cm. EPS)
---
2- Western midmountain 3- Inland Plateau
0.713 (2 cm EPS) to 0.524 (6 cm EPS)
No insulation to 0.669 (4 cm EPS).
0.413 (4 cm EPS) to 0.415 (8 cm EPS)
0.669 (4 cm. EPS) to 0.500 (6 cm EPS).
4- High Mountain
0.524 (6 cm EPS) to 0.415 (8 cm EPS)
0.524 (6 cm EPS) to 0.415 (8 cm EPS)
Glass U value W/m2.K 5.5 for all scenarios
Glass Shading Coefficient 0.45 for all scenarios
5.5 for all scenarios 5.5 To 2.5 4.3 For All
0.45 for all scenarios 0.45 To 0.26 0.84 For All
Table 43 presents the results of the sensitivity analysis for the residential building. The roof results show a variation in the same range as for the office building. For the wall, the range is quite variable with a 2 cm to 4 cm of equivalent polystyrene insulation in Beirut and a gradual increase in other zones. From the glazing in zones 1 and 2, only two types of glazing are providing the optimum economic return. One of the glazing types is a single glass with reflective coating and the second is a double glazing with also a reflective coating. In this case, it is more likely that it was not the thermal transmittance characteristic of the double glazing that makes it a good choice but rather the very low shading coefficient associated with this glazing. Table 43 Range in Optimum Shell Properties to Maximize NPV (for all 9 scenarios) – Residential Climate Zone 1 – Coastal 2- Western midmountain 3- Inland Plateau 4- High Mountain
Roof U value W/m2.K
Wall U value W/m2.K
0.524 (6 cm EPS) All scenarios
0.669 (4 cm EPS) all scenarios
0.524 (6 cm EPS) to 0.415 (8 cm EPS)
0.669 (4 cm EPS) to 0.500 (6 cm EPS).
0.524 (6 cm EPS) to 0.415 (8 cm EPS) 0.415 (8 cm EPS) All scenarios
0.500 (6 cm EPS) To 0.399 (8 cm EPS). 0.399 (8 cm EPS) All scenarios
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Glass U value W/m2.K 5.5 To 2.5
Glass Shading Coefficient 0.45 To 0.26
5.5 To 2.5 2.5 for all scenarios 4.27 To 2.0
0.45 To 0.26 0.26 for all scenarios 0.84 To 0.57
4
RECOMMENDATIONS FOR THE THERMAL STANDARD
Results of the economic analysis based on an average scenario (Scenario #4) indicate that certain additions in roof and wall insulating values and reduction of the coefficient of heat transfer for windows would result in economic benefits to the building owner over the 20 year evaluation period. The economic analysis also revealed that the selection of windows with a low shading coefficient and the installation of architectural shading devices (fins and overhang) can be beneficial in the warmer part of Lebanon. The economic analysis will allow the determination of the appropriate effective fenestration ratio requirement (EFRreq) which includes various fenestration characteristics like orientation, glazing shading coefficient and architectural shading factor. This section summarizes the results presented in the economic analysis section and discusses further the establishment of the effective fenestration ratio (ERFreq) based on the economic analysis results.
4.1
Maximum U-value for Roofs and Walls
The analysis of cases studied for Scenario 4 suggest that optimum insulation levels for roofs and walls should be as presented in Section 3.1 for the roof. For the wall, the requirement should be set at the level presented in Section 3.2 except in Beirut because the cumulative effect of wall insulation will be small if the roof is already insulated in this zone with lower heating requirement. For that region, it will be required that a double cavity wall without insulation be the minimum requirement for wall construction. In the case of office buildings, this recommended level takes also into consideration that we should avoid large difference in requirements between zones. Additional consideration in the selection of the regulatory level is to provide some margin for change in construction methods. With the addition of this margin, the promoters will not be in obligation to increase the insulation j level just because they have deviated from the base case construction scenarios used to establish the level in this analysis. The level recommended in the table below takes this margin into account. Finally, the required wall level in zones 3 and 4 has been adjusted to a lower requirement than what was found in section 3.2 due to the fact that the walls have a lower economic return than roofs and as such would have a lower requirement. The increment of 2 cm used in the analysis was not fine enough to find the real optimum level of insulation (probably between 4 cm and 6 cm for zone 3 and between 6 cm and 8 cm for zone 4). In order to keep with the rationale of a minimum requirement for this voluntary standard, the lower boundary of insulation has been retained. A last deviation from the wall recommendations in section 3.2 is applied for the Western mid-mountain region where the recommendation leads to 6 cm of insulation, but by a too close margin with the 4 cm alternative. The difference in terms of NPV is just 63 USD over a 20 year period and the difference in payback is just 2.0 years instead of 2.03. Considering this small difference in result and the important incremental cost that is added by considering 6 cm insulation, then a conservative level at 4 cm equivalent of polystyrene insulation was retained. The resulting values are consolidated in Table 44, along with the equivalent thickness of expanded polystyrene (R=0.25). These are the recommended levels of roof and wall U-values for the Thermal Standard for Buildings for the various climatic zones of Lebanon. The recommendations are presented for the two categories of buildings. Category 1 is for residential buildings while category 2 is for the other types of buildings.
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Table 44 Optimum Roof and Wall Insulation levels by Climate Zone Climate zone
1 - Coastal
2 – Western midMountain 3 – Inland Plateau 4 – High Mountain
Building Type 1
U Value (W/m2.K) 0.57
Roof Equivalent Polystyrene (cm) 6
U Value (W/m2.K) 2.10
2
0.57
6
2.10
1 2 1 2 1 2
0.57 0.57 0.57 0.57 0.44 0.44
6 6 6 6 8 8
0.77 0.77 0.77 0.77 0.55 0.55
Wall Equivalent Polystyrene (cm) Double cavity wall No insulation Double cavity wall No insulation 4 4 4 4 6 6
Once insulation is added to a masonry building, one has to look at thermal bridging that occurs across the joints between walls and floors and projections such as balconies. No evaluation was made at this stage of the various options to insulate these additional areas of heat gains and losses, but future analysis should look at the issue of thermal bridges before the Thermal Standard becomes mandatory in the target year 2010.
4.2
Maximum U-value for Windows
Table 45 summarizes the recommendations from the economic analysis for the selection of the glazing Uvalue in Lebanon. This section just considers the heat transfer characteristic of the glass. The shading coefficient will not be directly subject to an absolute requirement but will rather be incorporated into the effective fenestration ratio requirement (EFRreq) were the orientation and architectural shading devices will also be taken into consideration. Table 45 Glazing thermal transmittance requirement Climate Zone
1 - Coastal 2 – Western mid-Mountainous 3 – Inland Plateau 4 – High Mountainous
4.3
Building Type 1 2 1 2 1 2 1 2
Window U Value (W/m2.K) 6.2 6.2 4.3 4.3 4.3 4.3 2.8 2.8
Typical Characteristics Single glass Single glass Single glass, low-e Single glass, low-e Single glass, low-e Single glass, low-e Double glazing, clear, low-e Double glazing, clear, low-e
Maximum Effective Fenestration Ratio
This section discusses the selection of the proposed effective fenestration ratios (EFRreq) in the thermal standard. This is an important requirement as it complements the U-value requirements. The objective of the EFRreq is to limit the solar load to a reasonable range for the two categories of buildings. Determining the reasonable EFRreq requires a simultaneous analysis of various parameters that the building designer may act upon in order to reduce the solar heat gain of the proposed building. These are: • • •
the orientation of the building, the glass shading coefficient, the architectural shading factor (fins and overhang)
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Each of these factors has already been analyzed in the previous sections for its technical and economical interests. The conclusion for the fenestration ratio, the glass shading coefficient and the architectural shading factors are summarized in the following:
Fenestration Ratio Windows facing west, south and east are surfaces that can allow solar heat gain in summer and contribute to increasing the air conditioning load. They can also allow solar heat in winter and help offset some of the space heating energy requirements. Because of the higher U-value of glass compared to walls, windows have a higher heat loss per unit area than walls, particularly in cold weather, during cloudy days and at night. Therefore, we can say that in general increasing the window to wall ratio will result in a higher energy requirement for both space heating and cooling. This was confirmed in all Visual-DOE3 runs where the effect of increasing the WWR from the base case also increased the energy usage. So, from a strict point of view of thermal energy, the percentage of fenestration should be kept to a reasonable level for higher efficiency. However, this cannot be a practical requirement in a thermal standard as it may impact building design and day-lighting. The approach that will be used instead is to put a limit on the fenestration ratio in the prescriptive path. This will result in the need to compensate a high fenestration ratio by other improvements in the building envelope to insure that the building will not use more energy than a building using the maximum allowed WWR. Since windows are desirable from the point of view of natural lighting, natural ventilation (for opening windows), occupant general visual comfort and aesthetics, it is necessary to allow reasonable WWRs, as long as this does not adversely affect the thermal energy requirements of the building. Options to offset heat losses/gains incurred by greater window areas can include specifying better U-values for walls and roofs, addition of external horizontal or vertical window shading devices and lower U-value window configurations (two panes or better frame).
Glazing Shading Coefficient The results of the NPV analysis indicate that in all climates, there are some types of tinted or reflective windows that are cost effective. This result may seem contradictory at first glance as the global energy requirement for a building actually increases in regions like mid-mountain and inland when reflective windows are installed. However, when the detailed economic analysis results in the cooling and heating season are performed using the relative price of energy for cooling and heating, reflective windows are cost effective in most climates because the cooling energy cost saved is higher than the heating energy cost. The only exception would be in the high mountain zone where only one tinted double glass window was providing a positive NPV but a closer examination of the NPV results reveal that a clear double window outperforms the tinted window in this region. So the positive NPV was solely due to the lower coefficient of heat transfer of the double glazing and not to its shading coefficient.
Architectural Shading Factor The architectural shading factor is an interesting measure that should be considered in all climates and can be combined with glass shading coefficient, orientation and fenestration ratio. In the coastal zone, the economic analysis revealed that the higher the architectural shading factor, the better, even to the point that the economical size of fins and overhang may not be consistent with building aesthetic or natural lighting objectives. Thus the architectural shading factor must not be selected in the thermal standard based on the economic return only. For the western mid-mountain and inland region, the architectural shading factor also makes sense but the economically optimal size of fins and overhang is more in a reasonable range (architecturally speaking) and could be applied to reduce the effective fenestration ratio of buildings in most designs.
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For the high-mountain region, solar heat gain is beneficial most of the year so the only cost effective alternative here are moderate overhangs and fins that will just limit the solar heat gain during summer and will still let most of the solar heat gain in winter get in. Based on the above information, to the study proceeded to calculating the effective fenestration ratio of different building shapes with combinations of cost effective measures to establish a reasonable level requirement in the thermal standard should be. In the thermal standard, the following relationship will be used to define the effective fenestration ratio of the building by considering the individual impact of each window (vertical window or skylight) in the building. EFR = Σ (Awi x SCwi x ASFwi) / Σ Av + 2 Σ (Asi x SCsi) / Σ Ah EFR
Awi SCwi ASFwi Av Asi SCsi Ah
(3)
= The effective fenestration ratio of the building considering the combined impact of glass shading coefficient, orientation and architectural shading factor on the solar heat gain and on the resulting heating and cooling requirement for the building. = Area of a specific individual window (m2) = Shading coefficient of the individual window = Architectural shading factor of the individual window = Area of all vertical surfaces (opaque walls + windows) (m2) = Area of the individual skylight (m2) = Shading coefficient of the individual skylight = Area of all horizontal surfaces (roofs + skylights) (m2)
In the above equation, the area of individual windows Awi and skylights Asi can be determined from a simple examination of the building drawings. The shading coefficient SCwi and SCsi can be determined by the optical characteristics of the selected windows and skylights. These are typically provided by the manufacturer. The architectural shading factor ASFwi, is a function of the physical (architectural) protections used for the windows and the orientation of the window considered. Furthermore, in the above equation, a weighting factor of 2 is applied to all skylights as their impact on cooling load is much higher than vertical windows. The calculated EFR for the proposed building will then be compared to the effective fenestration ratio requirement (EFRreq) proposed in the thermal standard to determine if the proposed building complies with the thermal standard. In order to determine the appropriate EFRreq levels for Lebanon, different typical buildings and scenarios were considered. By direct examination of the results, a judgment could be applied to what level or EFR should be required for the purpose of the thermal standard. The typical buildings considered have two different shapes and orientations for a total of four building variations. Then, each of these four buildings were tested with five different scenarios of window to wall ratios and for different glazing shading coefficient and architectural shading factors for a total of 20 calculations for each climate zone.
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4.3.1
EFRreq Analysis for Office Buildings
Table 46 presents the EFRreq variations considered for the office building analysis . Table 46 Variations of Buildings for the EFRreq Analysis Alternative for Buildings Building form factor and orientation
Building window to wall ratio and shading
20 m x 40 m x 5 stories, main façade toward south 20 m x 40 m x 5 stories, main façade toward east 20 m x 60 m x 5 stories, main façade toward south 20 m x 60 m x 5 stories, main façade toward east 20% WWR and no shading 25% WWR and selected SC and overhang 25% WWR and selected SC and overhang 30% WWR and selected SC and overhang Maximum WWR and limits for SC and overhang
Table 47 presents the five scenarios for window shading. The first one that corresponds to 20% WWR is a building with single clear glass (SC = 0.95) and no overhang or fins. This scenario was set up just to see if a building with a minimum window area will automatically comply to the EFRreq even if the designer does nothing to limit the solar radiation. The three next scenarios for 25%, 30% and 35% WWR consider a selected SC and overhang value. These values were selected by inspection of the economic returns of glazing types and overhangs and fins. The retained value is generally lower than the alternative that provides the maximum economic return for the following reasons: - Shading coefficient: We can observe from the economic analysis that it is possible to economically justify windows with a very low shading coefficient. These windows are generally highly reflective and thus impose a quite important restriction on the architectural concept of the building (mirror like building façade) and on the daylight quality. Going for a thermal standard that is too strictly tied to the economic value of the glass type will have a negative impact on the freedom of architectural expression. It is also important to consider the fact that highly reflective windows do not provide a good natural lighting level inside buildings. This will have the negative effect of increasing the artificial lighting usage and consequently the energy requirement for it. - Overhangs: We can observe from the economic analysis that the measure for fins and overhangs is very economical, particularly in a coastal climate. The projection that would be recommended from a strict economical point of view will be 1.50 which means 1.5 times the window height. If such a high level is put as a requirement in the thermal standard, it will impose again a severe limitation to architectural expression. For aesthetic and practical reasons, it is recommended that the overhangs and fins used to recommend the EFR required be selected to a level that makes practical sense. According to these considerations, a “selected” level of shading coefficient and overhang projection was selected for the calculation of the EFR. This level of SC and projection for overhangs will be used to test the case study building with 25%, 30%, and 35% WWR. From the above scenarios, a decision was made about the level of EFR that should become the requirement in the thermal standard. Once the EFR was selected, a last case study of building scenario was calculated to determine if the proposed EFRreq could be achieved by a building with a very high percentage of fenestration and using all cost effective measures (i.e. using the minimum possible shading coefficient and the maximum overhang). This last simulation reveals the absolute practical limitation that can be put on a building with the proposed EFRreq. Table 47 presents the selected and limit values of SC and overhang projection factors used in the case study.
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Table 47 Selected and Limit Values of SC and projection factor for overhang Selected Overhang Projection factor
Climatic Zone SC
SC
Limit Overhang Projection factor
Zone 1 - Coastal Zone 2 - Western Mid-mountain Zone 3 - Inland Plateau
0.6 0.7 0.7
0.60 0.40 0.60
0.26 0.26 0.26
1.50 0.80 1.50
Zone 4 - High Mountain
0.95
0.20
0.57
0.20
The results of the simulations are summarized in Table 48. In this table, we can see one section for each climatic zone considered and one line in each table section for the four variations of building form and orientation. Then, each section presents the case studies for five different scenarios of WWR, SC and overhang according to Table 47. Table 48 Case Study Analysis to Determine the WWRreq – Office Buildings Climatic zone
Façade Width (m)
Façade Length (m)
20 20 20 20
40 40 60 60
Façade Width (m)
Façade Length (m)
20 20 20 20
40 40 60 60
Façade Width (m)
Façade Length (m)
20 20 20 20
40 40 60 60
Climatic zone
Façade Width (m)
Façade Length (m)
Zone 4 - High mountainous
20 20 20 20
40 40 60 60
Zone 1 - Coastal
Climatic zone
Zone 2 - Western mid-mountain
Climatic zone
Zone 3 – Inland plateau
Orientation Main façade S E S E Orientation Main façade S E S E Orientation Main façade S E S E Orientation Main façade S E S E
WWR 20% SC=.95 NO PF 13.2% 14.4% 17.2% 19.6% WWR 20% SC=.95 NO PF 13.2% 14.4% 17.2% 19.6% WWR 20% SC=.95 NO PF 13.2% 14.4% 17.2% 19.6% WWR 20% SC=.95 NO PF 13.2% 14.4% 17.2% 19.6%
WWR 25% SC=.6 0.6 PF Overhang 6.1% 6.7% 7.9% 9.2% WWR 25% SC = .7 0.4 PF Overhang 8.3% 9.1% 10.7% 12.4% WWR 25% SC = .7 0.6 PF Overhang 7.1% 7.8% 9.3% 10.7% WWR 25% SC = 0,95 0.2 PF Overhang 13.5% 14.8% 17.6% 20.2%
Case studies WWR 30% SC=.6 O.6 PF Overhang 7.3% 8.1% 9.5% 11.0% Case studies WWR 30% SC = .7 0.4 PF Overhang 9.9% 10.9% 12.9% 14.9% Case studies WWR 30% SC = .7 0.6 PF Overhang 8.5% 9.4% 11.1% 12.8% Case studies WWR 30% SC = 0,95 0.2 PF Overhang 16.2% 17.8% 21.1% 24.3%
WWR 35% SC=.6 0.6 PF Overhang 8.5% 9.4% 11.1% 12.9%
WWR = 98% SC = .26 1.5 PF Overhang 6.8% 7.4% 8.9% 10.0%
WWR 35% SC = .7 0.4 PF Overhang 11.6% 12.8% 15.0% 17.4%
WWR = 93% SC = 0.26 0.8 PF Overhang 8.6% 9.5% 11.2% 13.0%
WWR 35% SC = .7 0.6 PF Overhang 10.0% 11.0% 13.0% 15.0%
WWR = 100% SC = .26 1.5 PF Overhang 7.0% 7.5% 9.1% 10.2%
WWR 35% SC = 0,95 0.2 PF Overhang 18.9% 20.8% 24.6% 28.3%
WWR = 39% SC = 0,57 0.2 PF Overhang 13.0% 14.2% 16.9% 18.9%
By inspection, the value for the scenario at 30% WWR with the selected SC and overhang projection and for the 20 m x 60 m building oriented south was chosen as the one that should represent the required EFR in each zone. For instance, in Beirut, the EFRreq would be 9.5% that can be rounded to 10%. If we look at the other scenarios, we can see that, at this level of requirement, all buildings with a low WWR (20%) would not comply unless they install some sort of shading devices or by using glass with lower SC. The case study for the building that has 30% WWR (the maximum allowed WWR in the prescriptive approach) and the slightly disadvantaged shape (large façade toward south) would be complying if it uses the “selected” SC and overhang projection that represents a selection.
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The last column in table 48 presents what could be the maximum possible WWR of a building if the designer decides to apply measures that offer the best protection against sun. We can also see from the table than in almost all regions except Cedars, the designer would still have the possibility to go close to 100% fenestration if he selects the best glazing and longest overhang while installing cost effective measures. For Cedars, the practical limit would be a WWR of 39% because the minimum SC that make economic sense is 0.57 while in others regions it goes as low as 0.26. As a result of this analysis, the EFRreq levels presented in Table 49 will be recommended for office buildings. Table 49 Thermal Standard Requirement for WWRreq – Office Buildings Climatic Zone Zone 1 - Coastal Zone 2 - Western Mid-Mountain Zone 3 - Inland Plateau Zone 4 - High Mountain
4.3.2
EFRreq 10% 13% 11% 21%
EFRreq Analysis for Residential Buildings
For residential buildings, a process similar to the one described above for office buildings was applied. Similarly to office buildings, a “selected” and “limit” value of SC and overhang was selected considering the economic analysis of windows and the architectural shading device and practical constraint that the EFRreq could put in the construction market in Lebanon. Table 50 presents the selected and limit values of SC and overhang used in the case study. Table 50 Variations of Buildings for the EFRreq Analysis Alternatives for Buildings 20 m x 40 m x 5 stories, main façade toward south 20 m x 40 m x 5 stories, main façade toward east 20 m x 60 m x 5 stories, main façade toward south 20 m x 60 m x 5 stories, main façade toward east 15% WWR and no shading 18,5% WWR and selected SC and overhang 22% WWR and selected SC and overhang 26% WWR and selected SC and overhang
Building form factor and orientation
Building window to wall ratio and shading
Maximum WWR and limits for SC and overhang
Table 51 Selected and Limit Values of SC and projection factor for overhang Climatic Zone
SC
Limit Overhang Projection Factor
Zone 1 - Coastal Zone 2 - Western Mid-mountain Zone 3 - Inland Plateau
0.95 0.95 0.95
0.60 0.40 0.60
0.26 0.26 0.26
1.50 0.80 1.50
Zone 4 - High Mountain
0.95
0.20
0.57
0.20
SC
Selected Overhang Projection Factor
Energy Analysis and Economic Feasibility Study
:: 55 ::
Table 52 presents the result of the analysis for the four climatic zones and the various case studies. In the case of residential buildings, we notice that the requirement for windows will be lower due to the base hypothesis used for the typical window to wall ratio in current construction. This lower initial WWR means that in all regions, the requirement proposed will put a practical economic limit on the size of the window that can be designed in the building. This maximum economic ratio varies from 68% to 83% in the coastal, western mid-mountain and lower inland and inland plateau. For the high mountain region, the maximum economic EFR will be around 28% which is the most stringent requirement that will be put in the standard. It may be advisable to slightly increase the EFRreq for Cedars residential buildings to avoid requesting from building owners uneconomical choice of glazing and overhang for their buildings. A level of 18% instead of the 14% suggested by the analysis should be sufficient to allow some more flexibility to designers.
Table 52 Case Study Analysis to Determine the WWR req – Residential Buildings Climatic zone
Façade Width (m)
Façade Length (m)
20 20 20 20
40 40 60 60
Façade Width (m)
Façade Length (m)
20 20 20 20
40 40 60 60
Façade Width (m)
Façade Length (m)
20 20 20 20
40 40 60 60
Climatic zone
Façade Width (m)
Façade Length (m)
Zone 4 - High mountainous
20 20 20 20
40 40 60 60
Zone 1 - Coastal
Climatic zone
Zone 2 - Western mid-mountain
Climatic zone
Zone 3 – Inland plateau
Orientation Main façade S E S E Orientation Main façade S E S E Orientation Main façade S E S E Orientation Main façade S E S E
9.9% 10.8% 12.9% 14.7%
WWR 18,5% SC = .95 0.6 PF Overhang 7.1% 7.9% 9.3% 10.8%
WWR 15% SC=.95 No PF 9.9% 10.8% 12.9% 14.7%
WWR 18,5% SC = 0.95 0.4 PF Overhang 8.3% 9.2% 10.8% 12.5%
WWR 15% SC=.95 No PF 9.9% 10.8% 12.9% 14.7%
WWR 18,5% SC = 0.95 0.6 PF Overhang 7.2% 7.9% 9.3% 10.8%
WWR 15% SC=.95 No PF 9.9% 10.8% 12.9% 14.7%
WWR 18,5% SC = 0.95 0.2 PF Overhang 10.0% 11.0% 13.0% 15.0%
WWR 15% SC=.95 No PF
Case studies WWR 22% SC = .95 0.60 PF Overhang 8.5% 9.4% 11.0% 12.8% Case studies WWR 22% SC = 0.95 0.4 PF Overhang 9.9% 10.9% 12.8% 14.9% Case studies WWR 22% SC = 0.95 0.6 PF Overhang 8.5% 9.4% 11.1% 12.8% Case studies WWR 22% SC = 0.95 0.2 PF Overhang 11.9% 13.1% 15.5% 17.8%
The resulting recommended EFRreq levels for residential buildings are shown in Table 53. Table 53 Thermal Standard Requirement for EFR req – Residential Buildings Climatic Zone
EFRreq
Zone 1 - Coastal Zone 2 - Western Mid-mountain Zone 3 - Inland Plateau
11% 13% 11%
Zone 4 - High Mountain
16%
Energy Analysis and Economic Feasibility Study
:: 56 ::
WWR 26% SC =.95 0.6 PF Overhang 10.0% 11.1% 13.0% 15.1%
WWR = 68% SC = .95 1.50 PF Overhang 17.3% 18.7% 22.7% 25.4%
WWR 26% SC = 0.95 0.4 PF Overhang 11.7% 12.9% 15.1% 17.6%
WWR = 71% SC = 0.95 0.8 PF Overhang 24.0% 26.5% 31.2% 36.2%
WWR 26% SC = 0.95 0.6 PF Overhang 10.1% 11.1% 13.1% 15.1%
WWR = 83% SC = 0.95 1.5 PF Overhang 21.2% 22.8% 27.7% 30.9%
WWR 26% SC = 0.95 0.2 PF Overhang 14.1% 15.4% 18.3% 21.0%
WWR = 28% SC = 0.95 0.2 PF Overhang 15.1% 16.6% 19.7% 22.6%
5
IMPACT ASSESSMENT
The Thermal Standard for Buildings in Lebanon is anticipated to become mandatory in the target year 2010. This chapter presents a forecast of the impact of the application of the thermal standard on the macroeconomic level. This impact is based on an estimation of the area of residential buildings and office buildings which will be constructed on a 20 year horizon between the period 2010 and 2029.
5.1
Projected Economic Growth Rate
The assumptions about economic growth from 2005 to the end of the study period will be considered as follows: Table 54 Economic Growth Rate Economic growth rate Medium growth
5.2
Years 2005-2009 1.0%
Years 2010-2029 3.0%
Projected Population Growth Rate
Assumptions about the number of residential buildings meeting the thermal standards on a 20-year horizon will consider the following: • •
Demographic growth of the resident population. Family sizes assuming that each family will need one residential unit.
The projected population growth and the related family size are taken from the official projections used for the Schéma D’Aménagement du Territoire Libanais (SDATL), and are presented in Table 55 and Table 56.
Table 55 Projected Population Growth Year Mohafazat Beirut Mt-Liban North Beqaa South Nabatieh Total
2000 401,174 1,514,914 826,314 545,058 485,039 280,032 4,052,531
2010 424,121 1,668,223 984,156 623,372 579,820 326,344 4,606,036
2020 433,069 1,782,217 1,153,656 705,372 676,505 372,738 5,123,557
2030 428,891 1,851,129 1,327,677 781,265 769,717 414,719 5,573,398
Annual Growth Rate 20002030 0.21% 0.63% 1.49% 1.13% 1.45% 1.23% 0.96%
Source: Schema D’Amenagement du Territoire Libanais (SDATL), Rapport de la Phase 1, projections demographiques, Conseil du Développement et de la Reconstruction (CDR).
Energy Analysis and Economic Feasibility Study
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Table 56 Projected Number of Households Mohafazat Family size Beirut Total number Family size Mt-Liban Total number Family size North Total number Family size Beqaa Total number Family size South Total number Family size Nabatieh Total number Family size Total Total number
2000 4.23 94,748 4.42 342,770 5.47 150,987 4.93 110,490 4.94 98,223 4.35 64,441 4.74 861,659
2010 4.02 105,460 4.16 400,832 5.38 182,832 4.72 132,169 4.74 122,392 4.17 78,281 4.56 1,021,966
2020 3.82 113,374 3.92 454,738 5.29 217,900 4.51 156,423 4.54 148,852 4.00 93,198 4.38 1,184,485
2030 3.63 118,212 3.69 501,568 5.21 254,957 4.31 181,210 4.36 176,538 3.84 108,088 4.20 1,340,573
Source: Schéma D’Aménagement du Territoire Libanais (SDATL), Rapport de la Phase 1, projections démographiques, Conseil du Développement et de la Reconstruction (CDR).
5.3 5.3.1
Projected Building Growth Projected Built-up Area of Residential Buildings
The projected number of residential units, on a 20-year horizon, was taken from the projections of the Schéma D’Aménagement du Territoire Libanais (SDATL), and is presented in Table 57. Table 57 Projected Number of Residences Mohafazat 2000 2010 2020 2030 Primaire 103,024 114,670 123,276 128,536 Secondaire 2,436 2,595 2,764 2,944 Beirut Vacant 13,771 11,818 10,141 8,702 Total 118,130 125,065 132,408 140,182 Primaire 345,537 404,067 458,408 505,617 Secondaire 28,336 30,725 33,316 36,126 Mt-Liban Vacant 102,827 90,695 79,995 70,557 Total 475,185 517,087 562,683 612,300 Primaire 147,856 179,041 213,382 249,670 Secondaire 10,511 12,142 14,026 16,202 Nord Vacant 28,457 26,677 25,008 23,443 Total 187,016 216,293 250,154 289,315 Primaire 94,359 112,873 133,586 154,754 Secondaire 8,218 9,310 10,548 11,950 Beqaa Vacant 21,845 20,149 18,585 17,142 Total 124,763 141,974 161,559 183,846 Primaire 89,249 111,209 135,252 160,408 Secondaire 7,284 9,420 12,183 15,757 Sud Vacant 20,762 19,931 19,133 18,367 Total 117,100 138,687 164,253 194,532 Primaire 53,511 65,004 77,391 89,756 Secondaire 8,253 8,447 8,647 8,851 Nabatieh Vacant 13,893 12,729 11,662 10,686 Total 75,787 85,618 96,725 109,272 Primaire 833,536 986,864 1,141,295 1,288,741 Secondaire 65,038 72,639 81,484 91,830 Total Vacant 201,555 181,999 164,524 148,897 Total 1,097,981 1,224,724 1,367,782 1,529,447 Source: Schéma D’Aménagement du Territoire Libanais (SDATL), Rapport de la Phase 1, projections démographiques, Conseil du Développement et de la Reconstruction (CDR).
Energy Analysis and Economic Feasibility Study
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In addition to the building growth, another parameter that could be considered is the demolition and replacement of buildings that are over 75 years of age. This parameter contributes to an increase in the number of new buildings but will not be considered in this analysis. In order to translate the number of residential units constructed during the study period to square meters, an assumption was used for the surface area of the residential unit as an average of 140 m2 per residential unit. Table 58 presents the annual and total number of primary residential building units constructed during the study period and the resulting built-up area in m2. Table 58 Forecast of the Residential Building Area that will comply with the Thermal Standard Year 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029
Housing Number (units) 15,648 15,600 15,441 15,398 15,447 15,062 14,849 14,848 14,717 14,722 14,549 14,424 14,440 14,359 14,336 14,153 14,013 14,026 13,965 14,004
Housing Area (m2) 2,190,788 2,184,019 2,161,705 2,155,746 2,162,598 2,108,687 2,078,897 2,078,699 2,060,363 2,061,130 2,036,791 2,019,407 2,021,612 2,010,290 2,007,031 1,981,354 1,961,849 1,963,599 1,955,089 1,960,563
294,002
41,160,216
Total (2010 to 2029 - 20 years)
5.3.2
Projected Built up Area of Office Buildings
The projected growth of offices should normally follow the economic growth. However, since the 1996 survey of the Central Administration of Statistics (CAS) revealed a large number of empty offices, projections should therefore take into account the fact that there might be a gap between economic growth and the growth in the number of new offices. According to the 1996 survey of the CAS, the number of vacant office units reached an average of 31% on the national level. Considering the high level of vacancy, the following assumptions were made about the number and the occupancy of offices built according to the thermal standards: • •
The number of new office units up to 2010 will be considered negligible, and the 3% growth rate will be applied from 2010 onwards. It will be considered that all new offices constructed after 2010 will meet the thermal standard. The average surface area per office unit will be considered as 25 m2.
Table 59 presents the projection of the growth rate and occupancy of office space in the history period and for the forecast period. The resulting building area in m2 per climatic zone constructed each year is also identified in this Table. We can see from this Table that it is expected that the thermal standard will apply to a projected total constructed built-up area of 1,548,928 m2.
Energy Analysis and Economic Feasibility Study
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Table 59 Forecast of the Office Building Area that will comply with the Thermal Standard Year
Economic growth
Working offices
(%) 4.0 2.2 1.2 0.4 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029
(units)
New offices rate (%)
New offices Annual (units)
Total number of offices (units)
(units)
188,162 195,688 199,993 202,393 203,203 203,203 203,203 203,203 205,235 207,287 209,360 211,454 213,569 219,976 226,575 233,372 240,373 247,584 255,012 262,662 270,542 278,658 287,018 295,629 304,498 313,633 323,042 332,733 342,715 352,996 363,586 374,494 385,729 397,301
0 2 1.1 0.6 0.2 0 0 0 0 0 0 0 0 0 0 0 0 0 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 3.0 3.0 3.0 3.0 3.0 3.0 3.0
0 3,763 2,153 1,200 405 0 0 0 0 0 0 0 0 0 0 0 0 0 3,714 3,825 3,940 4,058 4,180 4,305 4,434 4,567 4,704 4,846 4,846 4,846 4,846 4,846 4,846 4,846
301,853 305,616 307,769 308,969 309,374 309,374 309,374 309,374 309,374 309,374 309,374 309,374 309,374 309,374 309,374 309,374 309,374 309,374 313,087 316,913 320,852 324,911 329,090 333,396 337,830 342,398 347,102 351,948 351,948 351,948 351,948 351,948 351,948 351,948
113,691 109,928 107,775 106,575 106,170 106,170 106,170 106,170 104,138 102,086 100,013 97,919 95,804 89,397 82,798 76,001 69,000 61,789 58,075 54,250 50,310 46,252 42,072 37,767 33,333 28,766 24,062 24,062 24,062 24,062 24,062 24,062 24,062 24,062
Total (2010 to 2029 - 20 years)
5.4
Empty offices
79,170
2
Area (m ) of office the thermal Zone 1 Zone 2 50% 20% 0 0 47,038 18,815 26,913 10,765 15,000 6,000 5,063 2,025 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 46,425 18,570 47,813 19,125 49,250 19,700 50,725 20,290 52,250 20,900 53,813 21,525 55,425 22,170 57,088 22,835 58,800 23,520 60,575 24,230 60,575 24,230 60,575 24,230 60,575 24,230 60,575 24,230 60,575 24,230 60,575 24,230 895,614
complying standard Zone 3 20% 0 18,815 10,765 6,000 2,025 0 0 0 0 0 0 0 0 0 0 0 0 0 18,570 19,125 19,700 20,290 20,900 21,525 22,170 22,835 23,520 24,230 24,230 24,230 24,230 24,230 24,230 24,230
358,245 358,245 1,791,228 m2
with Zone 4 10% 0 9,408 5,383 3,000 1,013 0 0 0 0 0 0 0 0 0 0 0 0 0 9,285 9,563 9,850 10,145 10,450 10,763 11,085 11,418 11,760 12,115 12,115 12,115 12,115 12,115 12,115 12,115 179,124
Projected Reduction in Energy Consumption
The potential impact of the LTSB on energy consumption in Lebanon can be estimated once the following numbers are derived: Di
= Difference in annual specific energy consumption (GJ/m2 of floor area) that each category (i) of building upgraded to the corresponding recommended level in the LTSB in each climatic zone (k) will consume (compared to a base case building of the same configuration)
Aik1
= Floor area (m2) by building category (i) of new buildings constructed in climatic zone (k) in year 1 since the implementation of the TSBL.
P1
= Potential energy savings at the end of year 1 can then be calculated using the following formula:
Energy Analysis and Economic Feasibility Study
:: 60 ::
Equation 4 – Potential energy saving for the first year summed over all building classes and over all climatic regions built in the first year.
P1 =
∑{∑ D × A
ik1 }
i
k
(Equation 4)
i
P2
= Potential energy savings at end of year 2
Aik2
= Floor area of new buildings by building class (i) built during year 2
Equation 5: potential energy savings for the second year
P2 = P1 +
∑ {∑ D × A
ik 2 }
i
k
(Equation 5)
i
P2 is summed over all building classes and over all climatic regions built in the second year. And so on for future years
5.4.1
Impact of Office Buildings
The results of the Economic Analysis of various improvements in thermal transmission levels of walls, windows and roofs indicate that there would be substantial savings in requiring new buildings and building expansions to comply with the optimum levels of thermal insulation. The savings per m2 of floor area between base office buildings and similar buildings complying with the thermal standard were calculated from model studies. By subtracting the energy budget per unit of floor area between the base building and the compliant building, we obtain the net annual savings per unit of floor area. The results of these subtractions are presented on the next table. Table 60 Annual Base Case Office Building Energy per m2 and Savings for Heating and Cooling
1 - Coastal 2 - Western Mid-mountain 3 - Inland Plateau 4 - High Mountain
Base Case Building Energy Usage GJ GJ/m2 437 0.228
Percentage Energy Savings % 9.8%
Energy Savings with the Thermal Standard Total Heating Cooling GJ GJ/m2 GJ GJ/m2 GJ GJ/m2 43 0.022 16.7 0.0087 26.0 0.0135
540
0.281
22.4%
121
0.063
119.0
0.0620
2.5
0.0013
794
0.414
46.5%
369
0.172
368.9
0.1921
6.1
0.0032
1242
0.647
56.7%
704
0.367
686.0
0.3573
1.8
0.0009
We can observe in the previous results that the Coastal region is the one where the potential is the least important. This is understandable as this is the zone where there is a lower set of measures for the improvement of thermal transmittance that are cost effective. The main characteristic of buildings that can be improved in this case is the solar radiation reduction but there are practical limits that have to be considered in this case to avoid putting too much restriction on the architectural expression. The overall result for the Coastal zone is a lower potential. For Cedars, we have the coldest climate and the one where the largest improvement in thermal transmittance is possible. Thus a very high potential of improvement exists which translates into a 56.7% percentage of improvement. The two others regions fall in between. We can see that the Inland Plateau zone, which has larger temperature amplitudes, has a higher potential than the Western mid-mountain.
Energy Analysis and Economic Feasibility Study
:: 61 ::
When the figures above are applied to the projected built-up area of which will be constructed in the future and which will comply with the thermal standard, we can find the following projection (Table 61) of energy savings caused by the thermal standard application. Table 61 Projected Cumulative office built-up area (2010-2029)
Yearly 2 (m ) 0 0 0 0 46,425 47,813 49,250 50,725 52,250 53,813 55,425 57,088 58,800 60,575 60,575 60,575 60,575 60,575 60,575 60,575
Zone 1 50% Cumulative 2 (m ) 0 0 0 0 46,425 94,238 143,488 194,213 246,463 300,276 355,701 412,789 471,589 532,164 592,739 653,314 713,889 774,464 835,039 895,614
895,614
7,262,405
Year
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2
Total (m )
Area of offices complying with the thermal standard Zone 2 Zone 3 20% 20% Yearly Cumulative Yearly Cumulative 2 2 2 2 (m ) (m ) (m ) (m ) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 18,570 18,570 18,570 18,570 19,125 37,695 19,125 37,695 19,700 57,395 19,700 57,395 20,290 77,685 20,290 77,685 20,900 98,585 20,900 98,585 21,525 120,110 21,525 120,110 22,170 142,280 22,170 142,280 22,835 165,115 22,835 165,115 23,520 188,635 23,520 188,635 24,230 212,865 24,230 212,865 24,230 237,095 24,230 237,095 24,230 261,325 24,230 261,325 24,230 285,555 24,230 285,555 24,230 309,785 24,230 309,785 24,230 334,015 24,230 334,015 24,230 358,245 24,230 358,245
Yearly 2 (m ) 0 0 0 0 9,285 9,563 9,850 10,145 10,450 10,763 11,085 11,418 11,760 12,115 12,115 12,115 12,115 12,115 12,115 12,115
Zone 4 10% Cumulative 2 (m ) 0 0 0 0 9,285 18,848 28,698 38,843 49,293 60,056 71,141 82,559 94,319 106,434 118,549 130,664 142,779 154,894 167,009 179,124
358,245
179,124
1,452,495
2,904,955
358,245
2,904,955
Table 62 Projected Energy Savings from Office Buildings (2010-2029) Climatic Zone 1 - Coastal
Distribution of projected built up area (%)
Cumulative m2 per zone 2010-2029
50%
7,262,405
2 - Western 20% 2,904,955 Mid-mountain 3 - Inland 20% 2,904,955 Plateau 4 - High 10% 1,452,495 Mountain Projected energy savings by type (GJ) Total projected energy savings (GJ)
Energy Analysis and Economic Feasibility Study
Heating Savings
Cooling Savings
GJ/m2
GJ
GJ/m2
GJ
Projected Energy Savings per zone (GJ)
0.0087
63,183
0.0135
98,042
161,225
0.0620
180,107
0.0013
3,776
183,883
0.1921
558,041
0.0032
9,295
567,336
0.3573
518,976
0.0009
1,307
520,283
1,320,307
112,420 1,432,727
:: 62 ::
5.4.2
Impact for Residential Buildings
Table 63 Annual Base Case Residential Building Energy per m2 and Savings for Heating and Cooling Climatic Zone
Base Case Bldg Energy Use GJ GJ/m2
Energy Savings %
Energy Savings with the Thermal Standard Total Heating Cooling GJ GJ/m2 GJ GJ/m2 GJ GJ/m2
1 - Coastal 2 - Western Mid-mountain 3 - Inland Plateau 4 - High Mountain
341
0.245
12.8
44
0.031
8.8
0.0063
35.0
0.0251
501
0.359
42.8
215
0.154
202.0
0.1449
12.5
0.0089
683
0.490
44.7
305
0.219
276.9
0.1986
28.3
0.0203
1283
0.921
58.0
745
0.534
743.2
0.5331
1.7
0.0012
Table 64 Projected cumulative residential built up area (2010-2029) Year
Area of residential buildings complying with the thermal standard Yearly (m2) Cumulative (m2) 2,190,788 2,190,788 2,184,019 4,374,806 2,161,705 6,536,511 2,155,746 8,692,257 2,162,598 10,854,855 2,108,687 12,963,542 2,078,897 15,042,439 2,078,699 17,121,138 2,060,363 19,181,501 2,061,130 21,242,631 2,036,791 23,279,421 2,019,407 25,298,829 2,021,612 27,320,441 2,010,290 29,330,731 2,007,031 31,337,762 1,981,354 33,319,116 1,961,849 35,280,965 1,963,599 37,244,565 1,955,089 39,199,653 1,960,563 41,160,216 41,160,216 440,972,169
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 Total (m2)
Table 65 Projected Energy Savings from Residential Buildings (2010-2029) Climatic Zone 1 - Coastal
Distribution of projected built up area (%)
Cumulative m2 per zone 2010-2029
50%
220,486,084
2 - Western 20% 88,194,433 Mid-mountain 3 - Inland 20% 88,194,433 Plateau 4 - High 10% 44,097,217 Mountain Projected energy savings by type (GJ) Total projected energy savings (GJ)
Energy Analysis and Economic Feasibility Study
Heating Savings
Cooling Savings
GJ/m2
GJ
GJ/m2
GJ
Projected Energy Savings per zone (GJ)
0.0063
1,389,062
0.0251
5,534,200
6,923,262
0.1449
12,779,373
0.0089
784,930
13,564,303
0.1986
17,515,414
0.0203
1,790,347
19,305,761
0.5331
23,508,226
0.0012
52,916
23,561,142
55,192,075
8,162,393 63,354,468
:: 63 ::
5.5
Summary of the Energy Saving Results
Over a 20 year period (2010-2029), the thermal standard for Buildings in Lebanon can generate a reduction in energy use at building input consisting of around 56 million GJ of avoided heating energy and around 8 million GJ of avoided cooling energy, as summarized in Table 66. Table 66 Summary of the Energy Savings at Building Input Building Category
Heating Savings (GJ) 55,192,075 1,320,307 56,512,382
Residential Offices (Non-Residential) Total
Cooling Savings (GJ) 8,162,393 112,420 8,274,813
Energy savings (GJ) 63,354,468 1,432,727 64,787,195
The environmental benefits include the avoidance of around 6 million tons of CO2, as summarized in Table 67. Table 67 projected avoided CO2 emissions (2010-2029) Energy Type
Energy in GJ
Energy in Mtoe
Cooling Energy
Electricity
8,274,813 GJ
0.18 Mtoe
Million tons of CO2 emissions 1.75
Heating Energy
Electricity Diesel Oil/Gas Wood
5,651,238 GJ 45,209,906 GJ 5,651,238 GJ
0.12 Mtoe 1.00 Mtoe 0.13 Mtoe
1.16 3.34 0.62
Total 64,787,195 GJ 1.43 Mtoe 6.87 Note: The CO2 emissions related to electricity have been calculated using the current electricity supply mix which is fuel based and which results in the emissions of 780g of CO2 for every KWh of electricity.
To be noted that the associated economic savings will vary in magnitude depending on the price of fuel and diesel oil. Average estimations indicate savings in the range of 500 million USD. These savings come from cost effective measures and further highlight the positive impacts of the application of the thermal standard for Buildings in Lebanon.
Energy Analysis and Economic Feasibility Study
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ANNEXES
Energy Analysis and Economic Feasibility Study
:: 65 ::
ANNEX 1.
DESCRIPTION OF THE RESIDENTIAL BUILDING
This Annex provides a description of the typical residential building used in the simulations. The units are given for convenience in SI and IP units as the simulations on DOE 2 was run in IP formats.
Overall Dimensions Building type: Residential building Shape: Rectangular with important window to wall ratio (WWR) on the South façade Dimension: 18.3 m x 15.2 m Number of floor: 5 floors, 3.3 meters (10.83 ft) height each. Construction •
Roof
There are two options for entering roof characteristics in Visual DOE3. The first one is the quick wall option where just the U value is specified. This option does not take wall mass into consideration and the calculation assumes steady state heat transfer. The second approach is more precise and the input defines each layer of materials including its position in the roof and the mass characteristics. The detailed option has been used in the simulations to take into consideration the mass effect that can be important in mild climate like Lebanon. The R value and U factor for the roof, excluding the interior and exterior air films are shown in the following table. Table A-1 1 Roof Thermal Conductance Layer Material Thickness RSI value (mm) (m2 ºC/W) 1 Tile 30 0.0882 2 Sand 100 0.1176 3 Weatherization 10 0.0588 4 Concrete 200 0.1129 5 Plaster 10 0.0139 Total 0.3913 U value (W/m2 ºC) 2.56 •
Exterior walls
The wall was entered in Visual DOE3 as a wall with complete layers and mass similar to the approach used for the roof. U value calculation for the exterior wall, excluding the interior and exterior air film is shown on the following table. Table A-1 2 Wall Thermal Conductance Material Thickness RSI value (mm) (m2 ºC/W) 1 Plaster 10 0.0139 2 Hollow blocks 150 0.2636 3 Plaster 10 0.0139 Total 0.2914 U value (W/m2 ºC) 3.432 Layer
•
Windows
Clear single pane window 6 mm thick. With a U factor of 6.16 W/m2 C (1.087 BTU/h.ft2.F) and a shading coefficient of 0.95. This model corresponds to window type 1001 in the DOE2.1E window library.
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The building façade has three dimensions for windows, namely: − Type n°1: Window and frame of 1.2 meters x 1.5 meters (3.94 ft x 4.92 ft) − Type n°2: Window and frame of 2.2 meters x 1.5 meters ( 7.22 ft x 4.92 ft) − Type n°3: Window and frame of 2.2 meters x 2.5 meters ( 7.22 ft x 8.2 ft) The number of windows per façade is as follows: − South Façade: Four windows of type n°3 per floor − North Façade : Four windows of type n°2 − East and West Façade: One window of type n°1 and one of type n°2 There is no external shading for the window on the base case. The window frame is defined as “Thermally unbroken aluminum” with a thickness of 1.5 cm (0.05 ft). The window to wall ratio is 20.5%. •
Intermediate floor
The U values for the internal floor are as follow:
Layer 1 2 3 4 5 6 U value
•
Table A-1 3 Internal Floor Thermal Conductance Material Thickness R value (mm) (m2 ºC/W) Tiles 30 0.0882 Mortar 10 0.0139 Sand 60 0.0694 Weatherization 80 0.0444 Concrete 120 0.2164 Plaster 10 0.0139 Total 0.4462 (W/m2 ºC) 2.24
Internal partition
Internal partitions are constituted of a 10 cm (4 in) concrete hollow blocks with plastering on both side. The U factor for the partition is as follows:
Layer 1 2 3 U value
•
Table A-1 4 Conductance of Internal Partition Material Thickness R value (mm) (m2 ºC/W) Plaster 10 0.0139 Hollow blocks 100 0.1646 Plaster 10 0.0139 Total 0.192 (W/m2 ºC) 5.197
Slab on ground
The slab on ground is located in the basement that is an unconditioned space. It is simulated with a 30 cm (12 inches) concrete layer then 100 mm (4 in.) earth then a mat of insulation to limit the exchange to more realistic value that take into consideration the thermal mass of the earth below the slab. This approach is consistent with the recommendation of Lawrence Berkeley laboratory, the designer of the DOE 2 software engine for simulating slabs on ground.
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Zones The building has two thermal zones per floor and divided in two apartments; the East and West apartments. Both apartments have windows on the south and north façade and two windows on the east or the west façade depending of the apartment considered. The staircase is located inside the building and is non conditioned area. Schedules and Set-points •
Occupancies, lighting and equipment schedules 2 − Lighting density: 16.14 W/m for load calculation. − Light to space fraction: 1 2 − Equipment power density (EPD) = 10.7 W/m for load calculation. 2 − Occupant density = 25.6 m /person − Zone type = conditioned for both apartments, unconditioned for the staircase and the parking (basement) − Occupancy type = Residential − Infiltration = 0.333 ACH
The occupancy is defined as follows: Weekday: Midnight to 6 a.m. 6 a.m. to noon Noon to 1 p.m. 1 p.m. to 6 p.m. 7p.m. to midnight
= = = = =
100% 65% 100% 65% 100%
Friday and Saturday:
Midnight to 6 a.m. = 6 a.m. to 2 p.m. = 2 p.m. to 3 p.m. = 3 p.m. to 7 p.m. = 7 p.m. to midnight =
100 % 65% 100 % 65% 100%
Sunday
Midnight to 7 a.m. = 7 a.m. to noon = Noon to midnight =
100% 65% 100%
• •
Heated at 21.1°C (70°F). Cooled at temperature 23.8°C (75°F).
Heating schedule Cooling schedule
• Fan schedule The fan schedule is on 24h all day. The fan schedule has no incidence on building heating or cooling energy as the fan energy will not be included in the report where the cooling and heating energy requirement will be extracted from Visual DOE3. Outside air schedule on ventilation system is on 24 h all day. • Domestic hot water schedule The domestic hot water schedule and consumption was put at zero to have no thermal load associated to this usage as we are interested on the energy impact of the envelope only. • System Packaged air conditioned system for cooling. A sensitivity analysis was made to vary the efficiency of the cooling system between a 0.32 kW input per kW cooling and an efficiency of 0.37 kW input per kW cooling. The heating is a hydronic hot water type with boiler efficiency of 0.8.
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ANNEX 2.
DESCRIPTION OF THE COMMERCIAL BUILDING
This section provides a description of the typical building used in simulations. Units are given for convenience in SI and IP as simulations on DOE 2 has been run in IP format. Overall Dimensions Building type: Office building Shape: Rectangular with the largest side facing south and north Dimension: South and north façade 24 meters (78.7 ft), east and west façades 16 meters (52.5 ft). Number of floors: 5 floors, 3.3 meters (10.83 ft) height each. Construction •
Roof
The detailed option of Visual DOE3 has been used in simulations to take into consideration the mass effect that can be important in mild climate like Lebanon. The R value and U factor for the roof, excluding interior and exterior air films is shown in the following table. Table A-2 1 Roof Thermal Conductance Layer Material Thickness RSI value (mm) (m2 ºC/W) 1 Tile 30 0.0882 2 Sand 100 0.1176 3 Weatherization 10 0.0588 4 Concrete 200 0.1129 5 Plaster 10 0.0139 Total 0.3913 U value (W/m2 ºC) 2.56 •
Exterior wall
The wall was entered in Visual DOE3 as a wall with complete layers and mass similar to the approach used for the roof. U value calculation for the exterior wall, excluding interior and exterior air films is shown in the following Table. Table A-2 2 Wall Thermal Conductance Material Thickness RSI value (mm) (m2 ºC/W) 1 Plaster 10 0.0139 2 Hollow block 150 0.2636 3 Plaster 10 0.0139 Total 0.2914 U value (W/m2 ºC) 3.432 Layer
•
Window
Clear single pane window of 6 mm thick with a U factor of 6.16 W/m2 C (1.087BTU/h.ft2.F) and a shading coefficient of 0.95. This model corresponds to window type 1001 in the DOE2.1E window library. The building façade has two dimensions for windows, namely: − Type n°1: window and frame of 1.4 meter x 2.9 meters (4.5 ft x 9.47 ft) − Type n°2: window and frame of 1.4 meter x 2.4 meters (4.5 ft x 8 ft)
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The number of windows per façade is as follows: − South and north façade: five windows of type n°1 per floor − East and west façade: four windows of type n°2 per floor There is no external shading for windows on the base case. The window frame is defined as “thermally unbroken aluminum” with a thickness of 1.5 cm (0.05 ft). The window to wall ratio is 25.4%. •
Intermediate floor
The U value for the internal floor is as follows: Table A-2 3 Internal Floor Thermal Conductance Layer Material Thickness R value (mm) (m2 ºC/W) 1 Tile 30 0.0882 2 Mortar 10 0.0139 3 Sand 60 0.0694 4 Weatherization 80 0.0444 5 Concrete 120 0.2164 6 Plaster 10 0.0139 Total 0.4462 U value (W/m2 ºC) 2.24 •
Internal partition
Internal partitions are constituted of a 10 cm (4 in) concrete hollow blocks with plastering on both sides. The U factor for the partition is as follows: Table A-2 4 Partition Thermal Conductance Layer Material Thickness R value (mm) (m2 ºC/W) 1 Plaster 10 0.0139 2 Hollow block 100 0.1646 3 Plaster 10 0.0139 Total 0.192 U value (W/m2 ºC) 5.197 •
Slab on ground
The slab on ground is located in the basement, which is an unconditioned space. It is simulated with a 30 cm (12 inches) concrete layer then 100 mm (4 in.) earth then a mat of insulation to limit the exchange to more realistic value that take into consideration the thermal mass of the earth below the slab. This approach is consistent with the recommendation of Lawrence Berkeley laboratory, designer of the DOE 2 software engine for simulating slabs on ground. Zones The building has five thermal zones per floor and they are divided in four orientations plus an internal zone per floor. There is also a basement that is considered as a parking and is not conditioned. All other spaces above ground are conditioned. Schedules and Set-points • Occupancy, lighting and equipment schedules 2 • Lighting density: 16.14 W/m design • Light to space fraction: 1 • Equipment power density (EPD) = 10.76 W/m2 design 2 • Occupant density = 16 m /person • Occupancy type = office • Infiltration = 0.333 ACH
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The same schedule is used to define occupancy, lighting operation and equipment (outlet for computers, faxes, printers, etc.) in zones. Weekdays:
Saturdays:
Sundays
12 midnight to 7 a.m. 7 a.m. to 8 a.m. 8 a.m. to 12 noon 12 noon to 1 p.m. 1 p.m. to 5 p.m. 5 p.m. to 7 p.m. 7 p.m. to 12 midnight 12 midnight to 7 a.m. 7 a.m. to 1 p.m. 1 p.m. to 12 p.m. Off
= = = = = = = = = =
0% 40% 90% 80% 90% 20% 0% 0% 90% 0%
• Heating schedule The building is heated at 21.1°C (70°F) from 6 a.m. to 7 p.m. weekdays and 7 a.m. to 1 p.m. Saturday. The rest of the time, the temperature is allowed to float down to a setback minimum temperature of 12.8 C (55°F). The system starts at 6 a.m. to insure that at 8 a.m. the temperature is close to comfort level. • Cooling schedule The temperature set-point is 23.3°C (74°F) from 6 a.m. to 7 p.m. on weekday and 7 a.m. to 1 p.m. on Saturday. The rest of the time, the temperature is allowed to float to a set-up maximum temperature of 37.2°C (99°F). The system is scheduled to start 2 hours before building occupancy to insure comfort during occupied time. • Fan schedule The fan schedule is on 24h all days. It does not have incidence on building heating or cooling energy as the energy used by the fan will not be included in the report where the cooling and heating energy requirement will be extracted from DOE2. •
Outside air schedule on ventilation system Weekday: 7 a.m. to 7 p.m. Saturday: 8 a.m. to 1 p.m. Sunday: off
•
Infiltration schedule:
0.33 air change per hour.
• Domestic hot water schedule The domestic hot water schedule and its associated consumption were put at zero to have no thermal load associated to this usage in order to not affect the results of the study that is focused on the thermal envelope. • System Packaged air conditioned system for cooling with an efficiency of 0.32 kW input/kW thermal. Air volume and fresh air are constant. The heating is a hydronic hot water type with a boiler efficiency of 0.8. The system was automatically sized once by DOE2 to see the recommended air volume in steady state conditions. However, as the system will be used with variable set-points, an automatic sizing is not adequate as the resulting system will be too small to rapidly bring the interior temperature in the comfort range following a period of set-up or set-back temperature in the night or during the weekend. An adjustment of air volume was then manually made to take into account the extra capacity required for rapidly bringing the interior temperature to comfort level. This added capacity is needed since for a normal operation the systems temperature set-points are set to start the system only two hours before the occupation time to reach a comfort level. The adjustment of the ventilation system was made by specifying a minimum air flow per square feet for the system.
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ANNEX 3.
HYPOTHESES OF ENERGY PRICE FORECAST
This annex provides information on how the energy forecast hypothesis were selected for the study. Crude oil cost forecasts hypothesis For the purpose of the study, the crude oil price forecast has been considered for a 20 year period (2005-2024). The following three studies were used as references for the selection of the forecast values: a)
International Energy Outlook 2004 (IEO 2004): the yearly outlook published by the American Department of Energy. Its conclusions are summarized in Table A-3 1 and provides expected prices for i crude oil per barrel for three different scenarios. Table A-3 2 provides information about a previous release of the long-term forecast issued in 2000. This information will be useful to analyze the variation between the two forecasts. Table A-3 1 Prices of Oil in 2002 USD per Barrel Forecasts 2005 2010 2015 2020 Reference Case 24.17 25.07 26.02 27.00 High Price Case 34.27 34.23 34.63 35.03 Low Price Case 16.98 16.98 16.98 16.98 Note: IEO 2004 projections are for average landed imports to the United States.
Table A-3 2 Prices of Oil in 2000 USD per Barrel (Reference) Forecasts 2005 2010 2015 2020 Reference Case 22.73 23.36 24 24.68 High Price Case 29.56 30.01 30.44 30.58 Low Price Case 17.41 17.64 17.64 17.64 Note: IEO 2000 projections are for average landed imports to the United States. The difference in price between the 2000 and 2004 forecast for the reference case over a 20 years period is a 6.5% increase. This percentage will be later used in the calculation for forecasting future price of electricity. b)
World Energy Outlook 2000 (WEO 2000): The yearly outlook published by the International Energy ii Agency (IEA) . This study provides forecasts in 2000 USD constant value at around $20.4/barrel until iii 2010 followed by a constant and linear increase till 2020 at USD 27.83 /barrel . As this analysis is older, it was not retained as the basis for this study.
c)
A study published in 2000 by the “Conseil d’Analyse Économique”, the official consulting office of the French Prime Minister, adopts the figure of USD 24,00 /barrel in constant year 2000 USD value, as the most suitable for OPEP producers on a 20-year horizon. This figure, which is close enough to what is found in Table A-3 3, is thus in general agreement with the 2000 projection of the US agency.
All three studies reveal the difficulty of making forecasts over long periods. Political instability in the region can have dramatic impacts on future oil prices. The level of volatility of oil price in 2004 and 2005 combined with the forecasted shortage of traditional fossil fuel (with variable scenarios) energy somewhere in the future and fuelled by the growth of economies in transition and in development add some more uncertainties to these forecasts. The recent surge in price in 2005 for crude oil at USD 50 per barrel shows that the official projections have difficulty taking into account the complexity of the market. Also, official projections from countries that are net importers of energy will obviously not project in the future large increases in cost as this could be perceived by producers as an indication of the willingness to pay of these countries and will be rather an unwise move in an official government publication. Nevertheless, the trend speaks for itself as we see the world energy usage growing rapidly, fuelled by the development of large economies like China. It is interesting to look at the recent short term forecast releases by the International Energy Agency (USA) that projects for the period 2005 to 2006 an average price between USD 40 and 45 /barrel. It also predicted that volatility in the market during peak consumption season could bring the market price to over 45 USD/barrel. Energy Analysis and Economic Feasibility Study
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On the other hand, the increase in world energy demand is still strong and increased by 2.4% in 2004. The report from the US agency for the base case scenario is entirely based on the hypothesis that OPEP will be willing to control the price in the market by regulating the production in order to keep the oil price in a range of USD 22 to 28 per barrel. But in a context of declining availability of fossil fuel global resources, introduction of new types of energy sources at higher production cost and the strategy for oil producers to get the maximum value from their remaining reserve, the base hypothesis seems a bit optimistic or merely reflecting the wishes of the largest importers of oil for the future price of this resource. Nevertheless, to be on the conservative side in the study for the thermal standard, the base case from the US agency was selected as the most probable scenario and the basis to develop our three scenarios for electricity cost. Prices are an average cost for the next 20 year period (2004 to 2024) in constant 2003 US dollars (not taking into consideration the general rate of inflation). IEA’s projections have been kept for the Lebanon thermal standard analysis. Table A-3 3 Crude Oil 20 Year Forecast Assumptions Alternative Low Base High
Price (USD 2002/Barrel) 20.452 25.565 30.678
Remarks - 20% from the base case + 20% from the base case
Diesel Oil Cost Forecasts Diesel price to distributor hypothesis After establishment of the hypothesis for crude oil price, the assumption for price of domestic oil used for heating in residential dwellings was determined. To determine a representative diesel cost in Lebanon, a review of the local price policy was conducted. After which the base case cost of EN590 for local distributors was set at USD 256.2 /kl. Domestic diesel oil prices for consumers After determining the cost of diesel oil to distributors, public authorities still need to determine the final cost at the retail outlet and oil station for consumers. This is done by adding the following components to the distributor price: a) Product taxes that are generally applied with the notable exception of domestic diesel oil. b) The maximum benefit of distribution companies that is currently fixed at 7,000 LP/kl. c) The transportation cost from the storage tanks of importers to oil stations that are fixed at a maximum of 8,000 LP/kl. d) The maximum benefit of station owners is fixed at 20,000 LP/kl. e) Since all maximum benefits and intermediary costs are fixed by the government, the maximum selling price to the customer is easy to evaluate. This allows the determination of a maximum VAT that will be paid by importers to the government on behalf of all other actors of the distribution chain. The VAT amount is currently fixed at 45,000LP/kl. With a maximum selling price to distributors of 415,000LP/kl, the final price for a liter of EN590 to the consumer reaches 495,000 LP. This corresponds to 9,900 LP/20 liters or 49.5 LP/l.
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This calculation supposes that all actors in the distribution chain will take the maximum allowed benefit which is not always the case but it can be taken as a general rule. Therefore, the following assumptions will be used: a) b) c) d) e)
A price of USD 256.2 /kl for local distributors. Product taxes unchanged at 0%, as diesel oil is mainly used by industries. VAT is not changed. Other various components of the tariff structure unchanged. US dollar rate exchange is maintained at 1,500 LP per USD.
In these conditions, the selling price in constant USD 2002 for the Lebanese consumer would be: 256,320 + (7,000+8,000+20,000+45,000)/1,500 = USD 309.6 /kl, i.e. USD 0.3 /liter. In that context, considering the base price of USD 0.3 per liter and variations of +/-20% as per IAE to determine a high and low scenario, we can forecast the following prices of EN590 for Lebanese consumers as follow:
Scenario Low Base High
Table A-3 4 Diesel Oil 20 Years Forecast Assumptions Price Remarks (USD 2002/litre) 0.24 20% lower than base case 0.30 0.36 20% higher than base case
Cost of electricity The forecasts for the cost of electricity to consumers is based on the current structure of the power tariff in Lebanon and on a study conducted in 1999 by “Électricité de France” (EDF) for “Électricité du Liban” (EDL). Table A-3 5 presents an overview of the main tariffs applied by EDL. Other sector rates are shown for comparison.
End User Building Sector
Hotels and Large Buildings Small Industries Agriculture Sector Public Sector
Table A-3 5 EDL Tariff Structure Monthly Price (LP/kWh) Consumption (kWh) < 100 35 100-300 55 300-400 80 400-500 120 >500 200 150 approx. Flat 115 Flat 115 Flat 140
Price (US cents/kWh) 2.3 3.6 5.3 8.0 13.2 10.0 7.6 7.6 9.3
Cost of electricity for residential, small offices and schools Residential, office, and school buildings subscribe to EDL at the low voltage distribution level and thus follow the low voltage rate structure. Table A-3 5 shows that when consumption exceeds 500 kWh, the highest rate is imposed i.e. 200 LP/kWh.
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The following sections will discuss the marginal rate corresponding to different categories of customers. •
Residential, small offices and private schools
The assumption retained for the determination of the marginal electricity cost to consumers in this category is that residences, small offices and private schools consume more than 500 kWh for base needs (lighting, refrigerators, water heating, electronic equipment and other appliances). Electric consumption for cooling and heating is considered a secondary usage added to base needs. A survey about the spending on energy purchases for families in the Beirut area showed that all income brackets except for the two poorest exceed 414.000 LP/year or an average of 34.500 LP/month which is the threshold to be consuming the last kWh in the most expensive bracket. Given the fact that basic needs such as lighting, food refrigeration, water heating and washing have to be satisfied at first, it is logical to estimate the cost of electricity for air conditioning in residential houses at 200 LP/kWh. Therefore, the study considered the following scenarios for the price of the kWh for residential buildings, small offices and private schools: a)
b)
c) d)
Low case: things remain unchanged for government’s policy of subsidizing electricity. However, the current rate structure is adjusted to reflect the increase in long-term projection of the crude oil cost over the next 20 years. The difference between the 2000 IEA’s projection (when the study on which the rate analysis is based) and the 2004 IEA’s projection was consulted to determine this adjustment. The proposed adjustment will be 6.5% of the oil price in the market. With this hypothesis, the Lebanese government still pays for all losses incurred by EDL during current and previous years. Therefore, the base scenario will consider that residential houses, small offices and public schools pay 200 LP/kWh plus 6.5% or 213 LP/kWh. VAT (10%) must be added for residential houses only, for a total of 234,300 LP/kWh. High case: the international oil price is adjusted by 6.5% to reflect the change in long-term trend concerning the international oil price between 2000 and 2004. The rate structure is changed by the government so there is a break even for EDL. Bill recovery is complete (no arrears in payment from customers). The energy rate increase required is 13% so that EDL can break-even. The new rate for the highest category of users would be: 200 x (1.00+0.13) = 226,000 LP/kWh. When VAT is added, this raises the customer cost for energy to 248,600 LP/kWh. Base case: this base case scenario is between the low and high scenario. So the electricity cost would be 219,500 LP/kWh before VAT and 241,450 LP/kWh with VAT. As far as VAT is concerned, the study considered that the present rate of 10% would remain unchanged for the coming years.
The three following scenarios for electricity price in residential buildings, small offices and private schools were consequently adopted as shown in Table A-3 6.
Scenario Low Case Base Case High Case
Table A-3 6 Cost of Electricity in LP and US$ per kWh Residential Houses Offices and Others (LP or USD/kWh) (LP or USD/kWh) 234,300 LP – USD 0.1562 213,000 LP – USD 0.1420 241,450 LP – USD 0.1609 219,500 LP – USD 0.1463 248,600 LP – USD 0.1657 226,000 LP – USD 0.1506
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ANNEX 4.
METHODOLOGY OF ECONOMIC ANALYSIS
The method One purpose of the present study is to show the impact of additional investments in thermal improvement measures on energy consumption and operating cost of buildings. The method used will consist of a comparison between two life cycle cost scenarios. The difference in life cycle cost for a base alternative and an improved alternative is equal to the net present value (NPV) of the proposed improvement. The two life cycle costs that will be compared are as follows: a)
The life cycle cost for building envelope and heating and cooling operation cost if buildings are kept unchanged in the future (LCC1 – base case). The life cycle cost of for the improved building (LCC2). This life cycle cost will include additional cost for construction and reduced energy expenses during operation.
b)
The difference between the LCC1 and LCC2 is equivalent to the net present value of the investment realized to improve the building thermal envelope. The incremental cost becomes the difference in investment and the difference in operating cost between the two scenarios defines the stream of energy savings. The future stream of money is discounted in the LCC’s analysis by an amount representing the minimum acceptable rate of return (MARR) of the investment realized. A NPV of zero means that the investment projected just meets the criteria of return of the investment projected. So a NPV of zero means that the investor should decide to invest in the proposed project. A positive NPV means that the investment provides a return above the expectation of the investor. The discount rate for the cash flows of the two alternatives is the same because both projects carry the same amount of risk. As we are using an average cost for energy over 20 years, the formula for discounting of future flow of money can be analytically solved. The following formula is used to calculate the NPV:
n ( 1+ d ) − 1 Vd = A × n d ×(1+d ) Where: NPV = Net present value of the investment I = Incremental cost of the investment in thermal envelope improvement S = Energy savings d = Discount rate N = Number of years in the study period Each of the components used in the formula is discussed below: •
Incremental cost or investment
This is evaluated for each measure as the difference in material and construction cost for the base case and the improved thermal envelope. The avoided cost for heating and cooling equipment is also taken into consideration in the analysis.
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•
Study period
In our case, the study period was fixed at 20 years. Despite the fact that buildings have life durations of more than 20 years, this period is long enough as: a) Discounted cash flows become negligible after 20 years, b) Forecasts on energy prices over a very long period are difficult to make. •
Energy savings
Heating expenses will be based on the cost forecast for fuel as it is the more conservative (less expensive) choice for heating building structure. Cooling expenses will be based on the cost forecast of electricity as all cooling devices work with electricity (except absorption machines or internal combustion engine driven chillers that are not widely used in Lebanon). •
Discount rate
The discount rate is an important element in project economic evaluations. Its determination is always difficult and, most often, the proposed values can be challenged as it depends of the perception of various categories of investors on what could be the minimum expected return of a project to trigger a decision for investment. The study considered the discount rates that reflect interest rates proposed by the local banking sector for investments with the same level of risk. In the case of investments with no commercial risks, such as energy conservation, to the comparable investment is in treasury bonds of the Lebanese government. Three investor discount rates are presented below and will be used to determine the low, base and high alternatives for discount rates. a) Interest rates on treasury bonds of the Lebanese government in US$ The issuing of treasury bonds used as reference for this study took place in May and August 2002. Their duration was five years and they had an interest rate of 10.5%. The duration of another bond issued during the same period was 15 years, which is close to the 20-year horizon of our study. It has an interest rate of 11.625%. Therefore the study considered a discount rate of 12% for investments by local people on a 20-year horizon as a base scenario for discount rate. This choice can also be justified by the fact that local banks currently ask a minimum of 11-12% on personal loans in USD. b) Interest rates on treasury bonds of the Lebanese government in LP The reference issuing of treasury bonds in LP took place on February 2, 2003. Durations were 3 and 6 months and bonds had interest rates of 6.96% and 8.18% respectively. Before that, a bond issue with two-year duration and 9.38% rate took place on December 23, 2002. Therefore, given the previous considerations, it seems logical to take a discount rate of 8% for investments in LP on a 20-year horizon by local people. This will be the low scenario for discount rate. c) Discount rates for investors with a broader choice of investments iv Country risk premiums for long-term investments in countries rated B- are currently around 12.6% . For 10 to 30-year long investments in the United States, current premiums for risk are around 4.5%. This means that, for foreign investors, the discount rate for an investment in Lebanon would be: 4.5% + 12.6% = 17.1%. For investments in Lebanese pounds, this discount rate should be adjusted with the differential in inflation rates. The consultants will consider that inflation in both the United States and Lebanon will be at very low and similar percentages. In such conditions, the discount rates for investments in US$ and in LP should be the same. For the 20-year period of the study, it seems realistic to count the total discount rate for Lebanon including country risk premium at around 18%. This value will also be the discount rate used for the cash flows related to investments in energy conservation measures. This will be considered the high scenario.
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ANNEX 5.
SCENARIOS FOR ENERGY AND DISCOUNT RATE VARIATIONS
In order to assess the sensitivity of the economic analysis of measures given variation in energy price and discount rate, nine scenarios were prepared to see the range of economics results that will be achieved with each thermal improvement measure. The sensitivity analysis is therefore based on combinations of various oil prices and discount rates. By combining three scenarios for energy price and three scenarios for discount rate, we come up with nine different scenarios for the economic analysis. Table A-5 1 Presentation of the Nine Scenarios Parameter 1 – Energy Price (at right) Low Base High Alternative Alternative Alternative Domestic diesel oil (EN 590) price ($/liter) 0.24 0.30 0.36 Electricity price for residential houses ($/kWh) 0.1562 0.1609 0.1657 Electricity price for offices and other buildings ($/kWh) 0.1420 0.14633 0.1506 Parameter 2 – Discount Rate (below) Resulting Combined Scenarios Low alternative for discount rate at 8% Scenario 3 Scenario 1 Scenario 2 Base alternative for discount rate at 12% Scenario 6 Scenario 4 Scenario 5 High alternative for discount rate at 16% Scenario 9 Scenario 7 Scenario 8 Scenario 4 is considered as the preferred scenario and will be used to determine the requirements of the thermal standard. The other scenario will be used to perform sensitivity analysis to see the range of variation caused by the variation in both main parameters used for the NPV calculation and what is the resulting optimum level of prescription for the thermal standard in each case. Scenario 3 is also interesting as it can be considered as a scenario that represents the investment position of a typical customer if EDL maintains a policy of subsidized energy. Scenario 8 is also interesting as it can be considered as the country level economic analysis where EDL wants to maintain a break even position on its electricity sales in the market and the investment is compared to other international investors’ requirement for discount rate. The combination of parameters for the nine scenarios is summarized in the table below. Table A-5 2 Parameters That Will be Applied in the Nine Scenarios
Residential Buildings
Office Buildings and all Non-Residential
Scenario
Discount Rate (%)
Diesel Oil Price (USD/liter)
Electricity Price (USD/kWh)
1 2 3 4 (Preferred) 5 6 7 8 9 1 2 3 4 (Preferred) 5 6 7 8 9
8 8 8 12 12 12 16 16 16 8 8 8 12 12 12 16 16 16
0.30 0.36 0.24 0.30 0.36 0.24 0.30 0.36 0.24 0.30 0.36 0.24 0.30 0.36 0.24 0.30 0.36 0.24
0.1609 0.1657 0.1562 0.1609 0.1657 0.1562 0.1609 0.1657 0.1562 0.1463 0.1506 0.1420 0.14633 0.1506 0.1420 0.1463 0.1506 0.1420
Energy Analysis and Economic Feasibility Study
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ANNEX 6.
INCREMENTAL CONSTRUCTION COSTS
Formulas below were used to determine the incremental cost for every change in the thermal characteristics of roof, wall, window and architectural shading devices. Prices in the base case scenario are referenced as P. 1) 2) 3) 4)
Roof insulation: 2 2 Incremental cost per unit area of building = (Incremental cost per m of roof x roof surface area in m ) Wall insulation: 2 2 (Incremental cost per m of wall x wall surface area in m ) Window improvement (U value or SC): 2 2 (Incremental cost per m of window x window area in m ) Fins and overhang cost: 3 (Incremental cost per m of concrete needed to build these architectural shading devices x fins and 3 overhang volume in m )
The following prices were used for the determination of the incremental cost. a)
Cost of insulating material and installation
A range of insulating materials exists on the market with different technical characteristics and prices. The 2 economic analysis was done considering the use of expanded polystyrene at USD 0.70 /cm/m . The cost for installation is estimated at USD 2/m2 whatever the thickness. b) Cost for double wall construction with insulation The preferred approach for the insulation of walls in Lebanon would be to install the insulation between two layers of walls. This approach is well suited to the actual construction market and would allow implementing better thermal insulation with a minimum disruption and market transformation requirement. Wall is typically composed of hollow concrete blocks covered with interior and exterior painted cement plaster. Some cladding could also be used. The following costs were considered in the analysis: • One of the two following options for wall thickness: 2 − Hollow blocks 20 cm thick = USD 8.5 /m (labor + material) 2 − Hollow blocks 15 cm thick = USD 6.5 /m (labor + material) • Interior and external plaster = USD 7 /m2 (2x USD 3.5 /m2) • Internal painting = USD 3 /m2 • One of the three following options for finishing − Exterior painting = USD 5 /m2 − Natural stone cladding = USD 22 /m2 − Granite cladding = USD 49 /m2 2 Therefore, the total incremental cost will vary between USD 21.50 and 23.50 /m for external painting; USD 2 2 38.50 and 40.50 /m for natural stone; and USD 65.50 and 67.50 /m for granite finishing.
For the purpose of a thermal standard, the incremental cost will be determined only by the hollow blocks and insulation cost, as the external cladding or the finishing is entirely at the discretion of the promoter. The incremental price that will be used for walls with insulation will be as follows:
Energy Analysis and Economic Feasibility Study
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Simulation
Base Case Alternative n° 1 Alternative n° 2 Alternative n° 3 Alternative n° 4
Table A-6 1 Double Wall with Insulation Incremental Cost Incremental Cost Composition Baseline Incremental Total USD/m2 USD/m2 USD/m2 Single wall without insulation: 20mm. light weight 15.5 0 15.5 cement, 200 mm blocks, 20mm light weight cement. Double wall and insulation: 2 cm. light weight cement, 15 cm hollow concrete blocks, 2 cm. polystyrene 11.9 27.4 insulation, 10 cm hollow concrete blocks. Alternative n°1 but with 4 cm. polystyrene insulation 13.3 28.8 Alternative n°1 but with 6 cm polystyrene insulation 14.7 30.2 Alternative n°1 but with 8 cm polystyrene insulation 16.1 31.6
c) Incremental cost for roof insulation The incremental cost for the roof will be based on insulation and installation costs only as the rest of the roof composition will remain the same. Table A-6 2 Incremental Cost for Roof Insulation Composition
Incremental cost Per m2 USD/m2
Base case
20 cm undried agregate, 10mm built-up roofing and air films, 10 cm. of sand, 3.2 cm. tile,
0.00
Alternative n° 1
Base case with a layer of 2 cm polysterene insulation
3.40
Alternative n° 2
Base case with a layer of 4 cm polysterene insulation
4.80
Alternative n° 3
Base case with a layer of 6 cm polysterene insulation
6.20
Alternative n° 4
Base case with a layer of 8 cm polysterene insulation
7.60
Simulation
d) Cost for window replacement The following incremental cost will be used for window improvements: Table A-6 3 Cost for Window Replacement Composition
Characteristics
Option Base 1 2 3 4 5 6 7 8 9
Single (6 mm) Single (6 mm) Single (6 mm) Single (6 mm) Single (6 mm) Double (6mm, 12mm gap, Double (6mm, 12mm gap, Double (6mm, 12mm gap, Double (6mm, 12mm gap, Double (6mm, 12mm gap,
6mm) 6mm) 6mm) 6mm) 6mm)
Tint
Reflective
Low-E
Filling
Clear Bronze Clear Clear Clear Clear Bronze Clear Clear Clear
----Tin-Oxyde Titanium ------Stainless H --0
--------e = 0.2 ------e = 0.2 e = 0.2
----------Air Air Air Air Argon
Base USD/m2 50
Incremental cost Increment Total USD/m2 USD/m2 4 54 15 65 20 70 50 100 50 100 78 128 85 135 160 210 250 300
f) Cost for fins and overhang A uniform price of USD 110 per cubic meter of concrete needed to build the fins and overhang has been considered. This price includes formwork, reinforced concrete and work.
Energy Analysis and Economic Feasibility Study
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ANNEX 7.
AVOIDED COST ON HVAC SYSTEMS
The improvement of the thermal envelope of buildings will allow reducing the capacity of heating and cooling equipment installed in these buildings. A superficial market survey was conducted to assess the cost of these equipment in the market and to derive average avoided cost for these systems. The avoided cost will not include the labor as very often it will remain the same for the installation of the heating system. For instance, the same number of convectors will probably be needed but smaller units will be installed. So the material cost will decrease but the installation will be quite similar. Avoided cost of Heating equipment Heating avoided cost will vary according to the type of heating system used in the building. For electrical heated building, the avoided cost will only be the reduction in electric convector (for baseboard heating systems). For hydronic systems, savings will be the summation of the reduction in size for the boilers and the hot water convector. Two ranges of price reduction will be considered, one for the residential sector and the other one for the rest of the market. Table A-7 1 shows the approximate price for boilers and burners in the market. A range was established by surveying some equipment vendors and average values were selected from this Table as typical of the market value. They are presented in Table A-7 2. The cost provided from the survey is in Euro but the final avoided cost per kW will be converted in US$ to be consistent with other figures used in the study. Table A-7 1 Typical Price Range for Boiler Units Component Capacity (kW) Price Range (Euro) Boilers and Burners 21 kw 651 – 1,812 28 kw 718 – 1,949 34 kw 777 – 2,102 40 kw 910 – 2,306 47 kw 980 – 2,461 58 kw 1,050 – 2,746 70 kw 1,694 – 2,917 85 kw 1,834 – 3,367 105 kw 2,044 – 4,264 Table A-7 2 Typical Price of Boilers Used in the Study Component Capacity Average Type (kW) Price ( ) Boilers + 26 kW 1,330 Cast iron boiler Burners (fuel) 50 kW 1,701 78 kW 2,160 105 kW 2,772 The incremental cost for boilers in the residential sector has been retained as the cost per kW for the difference between a 26 kW unit and a 50 kW unit. Increment (boiler – residential) = (1701 – 1330 ) / (50 kW – 26 kW) = 15.5 /kW Incremental (boiler – others) = (2160 - 1701 )/(78 kW – 50 kW) = 22.7 /kW Table A-7 3 shows the typical price of the market for electric and hot water radiators. A typical avoided cost per kW has been determined by comparing the 1,000 W units to the 1,500 W units. The same price will be applied in all the building types as they are both popular power. The incremental cost for electricity and hot water is then calculated as: Increment (electric) = (90 – 80 ) / (1.0 kW – 0.75 kW) = 40 /kW Incremental (hydronic) = (63 - 49 )/(78 kW – 50 kW) = 56 /kW Energy Analysis and Economic Feasibility Study
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Table A-7 3 Cost for Heating Convectors Capacity Price Notes Electric Convector
Hot Water Convector
500 w 750 w 1,000 w 1,500 w 500 w 750 w 1,000 w 1,500 w
50 80 90 105 35 49 63 91
Cast radiator
iron
Based on the avoided cost for boilers and convectors, we can calculate the total avoided cost for electricity and for hydronic systems. Electricity avoided heating cost = 40 /kW Hydronic – residential avoided heating cost = 56 /kW + 15.5 /kW = 71.5 /kW Hydronic – other buildings avoided heating cost = 56 /kW + 22.7 /kW = 78.7 /kW Then, a blended cost for electricity and fossil systems were calculated to obtain an average cost weighted by the penetration of each type of heating system used in the market. The establishment of the exact proportion of electricity and hydronic systems in the market was not possible as no studies have been made on this subject. However, the following estimates were made to represent the current blend of heating systems and sources: • Wood : 23.9% • Electric heating : 16.4% • Gas/diesel fossil fuel: 59.7% Based on these values, the resulting weighted avoided cost was considered. For the wood system, no reduction in installations was considered. Residential – heating = 49.22 /kW (US$63.97 /kW) Other buildings – heating = 53.53 /kW (US$69.56 /kW) Avoided Cost of Cooling system Table A-7 4 provides the price range observed in the Lebanese market for some types of air conditioning equipment. Table A-7 5 provides prices for lesser used heat pumps and large chillers. The establishment of the avoided cost for the residential system will be done by comparing the price of the 1.5 tons and 1.0 ton units. Average price for 1.0 ton = (300 + 750) / 2 = US$525 Average price for 1.5 tons = (450 + 1250) / 2 = US$850 The difference will thus be US$325 for a difference of 0.5 ton. Considering that the cooling unit has an input power of 0.32 kW/kW thermal cooling then, the avoided cost will equal USD 577.55 per kW input to the cooling unit. The establishment of the avoided cost for the other types of buildings will be based on the comparison of a 5 ton unit and a 4 ton unit. Average price for a 4 ton = (1,650 + 2,550) / 2 = USD 2,100 Average price for a 5 ton = (1,850 + 2,750) / 2 = USD 2,300 The difference will be USD 200 for a 1 ton capacity difference. Considering that the cooling unit has an input power of 0.32 kW/kW thermal cooling then, the avoided cost will equal USD 177.71 per kW input to the cooling unit.
Energy Analysis and Economic Feasibility Study
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Table A-7 4 Price Range for Air Conditioning Equipment Capacity Price Range (USD) Split Unit 0.75 T 250 – 650 1.0 T 300 – 750 1.5 T 450 – 1,250 2.0 T 500 – 1,450 Central Unit 2.5 T 750 – 1,650 3.0 T 1,450 – 2,350 4.0 T 1,650 – 2,550 5.0 T 1,850 – 2,750 Table A-7 5 Price for Heat Pump and Chillers Wall Split Unit 0.75 TR USD 490 Heat pump 1 TR USD 525 1.5 TR USD 775 2 TR USD 890 Water Chiller 50 TR 40,000 Cooling only 75 TR 55,000 100 TR 65,000 150 TR 75,000 Above equipment prices are subject to an additional 10% VAT
i
IEO 2004, Table 9, page 42. WEO 2000, p.39 iii These prices are average IEA’s crude oil import prices. iv Reuters financial news agency or www.bondsonline.com , among others ii [3]
Energy Analysis and Economic Feasibility Study
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ENERGY ANALYSIS AND ECONOMIC FEASIBILITY STUDY Development of viable solutions for the Thermal Standard for Buildings in Lebanon
Copyright © UNDP/GEF and MPWT/DGU – 2005 Reproduction is authorized provided the source is acknowledged and provided the reproduction is not sold. UNDP is the UN’s global development network, advocating for change and connecting countries to knowledge, experience and resources to help build a better life. We are on the ground in 166 countries, working with them on their own solutions to global and national development challenges. As they develop local capacity, they draw on the people of UNDP and our wide range of partners. For further information: United Nations Development Programme, www.undp.org.lb General Directorate of Urban Planning, www.public-works.gov.lb Note: The information contained within this document has been developed within a specific scope, and may be updated in the future.
Energy Analysis and Economic Feasibility Study
:: i ::
:: PREFACE This study has been developed in the context of Project “Capacity Building for the adoption and application of Thermal Standards for Buildings”. The project was funded by the Global Environment Facility, Managed by the United Nations Development Programme, and Executed under the Lebanese General Directorate of Urban Planning, Ministry of Public Works and Transport. The project falls under the Climate Change focal area and aims at the establishment of Thermal Standards for Buildings, and at enabling their adoption and application through the provision of capacity building and information dissemination.
:: ACKNOWLEDGEMENTS This study is the result of a collaborative effort between national inputs and international expertise. The project wishes to thank all individuals and institutions who supported and contributed to this study. Particular acknowledgements to: The General Directorate of Urban Planning The Directorate of Meteorological Services The Order of Engineers and Architects, Beirut
Energy Analysis and Economic Feasibility Study
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:: TABLE OF CONTENTS Preface and Acknowledgements List of figures List of Tables Executive Summary
ii iv v vi
1
INTRODUCTION 1.1 Methodology of the Energy Analysis 1.2 Methodology of the Economic Analysis
2
ENERGY ANALYSIS 2.1 Parametric Variations of Roof U-value 2.2 Parametric Variations of Wall U-value 2.3 Parametric Variations of Window U-value and Shading Coefficient 2.4 Parametric Variations of Window to Wall Ratio 2.5 Parametric Variations of Window Orientation and Architectural Shading 2.6 Recommended Architectural Shading Factors 2.7 Selected Combined Simulations for Walls, Roofs and Windows
9 9 15 19 19 32 36 38
3
ECONOMIC ANALYSIS 3.1 Economic Results of Roof U-value 3.2 Economic Results of Wall U-value 3.3 Economic Results of Window U-value and Shading Coefficient 3.4 Economic Results of Architectural Shading (Fins and Overhangs) 3.5 Economic Results of Selected Combined Measures 3.6 Sensitivity Analysis
40 40 41 42 44 47 49
4
RECOMMENDATIONS FOR THE THERMAL STANDARD 4.1 Maximum U-values for Roofs and Walls 4.2 Maximum U-values for Windows 4.3 Maximum Effective Fenestration Ratios
50 50 51 51
5
IMPACT ASSESSMENT 5.1 Projected Economic Growth rate 5.2 Projected Population Growth rate 5.3 Projected Building Growth 5.4 Projected Reduction in Energy Consumption 5.5 Summary of the Energy Savings Results
58 58 58 60 62 60
ANNEX 1 Description of the Residential Building used as Model ANNEX 2 Description of the Office Building used as Model ANNEX 3 Hypothesis for Energy Price Forecast ANNEX 4 Methodology of Economic Analysis ANNEX 5 Methodology of energy and Discount Rate variations ANNEX 6 Incremental Construction Costs ANNEX 7 Avoided costs on HVAC systems
66 69 73 78 81 82 85
Energy Analysis and Economic Feasibility Study
1 1 6
:: iii ::
:: LIST OF FIGURES Figure 1 Total Energy Usage vs. Roof U factor – Office Buildings
11
Figure 2 Cooling Energy Usage vs. Roof U factor – Office Buildings
11
Figure 3 Heating Energy Usage vs. Roof U factor – Office Buildings
12
Figure 4 Effect of the roof insulation – Cedars
12
Figure 5 Energy Usage vs. Roof U factor – Residential Buildings
13
Figure 6 Cooling Energy Usage vs. Roof U factor – Residential Buildings
14
Figure 7 Heating Energy Usage vs. Roof U factor – Residential Buildings
14
Figure 8 Total Energy Usage vs. Wall U factor - Office Buildings
16
Figure 9 Heating Energy Usage vs. Wall U factor - Office Buildings
16
Figure 10 Cooling Energy Usage vs. Wall U factor - Office Buildings
17
Figure 11 Energy Usage vs. Wall U factor - Residential Buildings
18
Figure 12 Cooling Energy Usage vs. Wall U factor - Residential Buildings
18
Figure 13 Heating Energy Usage vs. Wall U factor - Residential Buildings
19
Figure 14 Total energy usage with different Glazing - Office
20
Figure 15 Heating energy usage with different Glazings – Office
21
Figure 16 Cooling energy usage with different Glazings – Office
21
Figure 17 Single Glass Glazing Results - 3 Cities – Office
22
Figure 18 Double Glazing Analysis for 3 Cities – Office
23
Figure 19 Total Energy Usage With Different Glazings – Residential
23
Figure 20 Heating energy usage with different Glazings - Residential
24
Figure 21 Cooling energy usage with different Glazings - Residential
24
Figure 22 Single Glazing Analysis for 3 Cities - Residential
25
Figure 23 Double Glazing Analysis for 3 Cities - Residential
25
Figure 24 Variation of the Energy Usage with Increasing WWR – Office
27
Figure 25 Variation of Heating energy usage with increasing WWR - Office
28
Figure 26 Variation of Cooling energy usage with increasing WWR - Office
28
Figure 27 Recommended Threshold to trigger the Performance Path - Office
29
Figure 28 Variation of the Total Energy Usage with Increasing WWR - Residential
30
Figure 29 Variation of the Heating Energy Usage with Increasing WWR - Residential
30
Figure 30 Variation of the Cooling Energy Usage with Increasing WWR - Residential
31
Figure 31 Threshold Recommended to trigger the Performance Path - Residential
31
Figure 32 Overhang Projection Factor Parameters
33
Figure 33 Architectural Shading Factor for Various Overhang Projections
33
Figure 34 Fins Projection Factor Parameters
34
Figure 35 Fins Effect on the Architectural Shading Factor
34
Figure 36 Combined Effects of Fins and Overhang
35
Figure 37 Combined Measure Results in Office Buildings
38
Figure 38 Combined Measure Results Residential Buildings
38
Energy Analysis and Economic Feasibility Study
:: iv ::
:: LIST OF TABLES Table 1 Outdoor Design Conditions for Lebanon
2
Table 2 Breakdown of Existing Building Units according to use
2
Table 3 Average number of Floors per Building Permit
3
Table 4 Breakdown of Building Permits according to use use
4
Table 5 Base Case Roof and Wall Compositions of Residential and Office Buildings
5
Table 6 Crude Oil 20 Year Forecast Assumptions
6
Table 7 Assumed Diesel Oil Prices
6
Table 8 Cost of Electricity in LP and US$ Per Kwh
7
Table 9 Assumed Discount Rates
7
Table 10 Presentation of the Nine Scenarios
7
Table 11 Parameters That will be Applied in the Nine Scenarios
8
Table 12 Parametric Roof U factor Alternatives
9
Table 13 Parametric Wall U factor Alternatives
15
Table 14 Parametric Window U factor and Shading Coefficient Alternatives
20
Table 15 Parametric Window to Wall Ratio Alternatives
27
Table 16 Parametric Alternatives for the Evaluation of Architectural Shading Factors
32
Table 17 Architectural Shading Factor (ASF) for Unprotected Windows
36
Table 18 Architectural Shading Factor (ASF) for Windows Protected by Overhang Only
36
Table 19 Architectural Shading Factor (ASF) for Windows Protected by Fins Only
36
Table 20 Architectural Shading Factor (ASF) for Windows Protected by Fins and Overhang
36
Table 21 Combined Simulation Parameters
37
Table 22 Optimum Insulation Level for Roof (with additional cooling) – Office Building
39
Table 23 Optimum Insulation Level for Roof (without additional cooling) – Office Building
39
Table 24 Optimum Insulation Level for Roof (with additional cooling) – Residential Building
40
Table 25 Optimum Insulation Level for Roof (without additional cooling) – Residential Building
40
Table 26 Optimum Insulation Level for Wall (with additional cooling) – Office Building
40
Table 27 Optimum Insulation Level for Wall (without additional cooling) – Office Building
40
Table 28 Optimum Insulation Level for Wall (with additional cooling) – Residential Building
41
Table 29 Optimum Insulation Level for Wall (without additional cooling) – Residential Building
41
Table 30 Optimum Performance of Windows (with additional cooling load) – Office Building
42
Table 31 Optimum Performance of Windows (without additional cooling) – Residential Building
42
Table 32 Optimum Projection and Net Present Value – Office Building, Climatic Zone 1
43
Table 33 Optimum Projection and Net Present Value – Office Building, Climatic Zone 2
43
Table 34 Optimum Projection and Net Present Value – Office Building, Climatic Zone 3
44
Table 35 Optimum Projection and Net Present Value – Office Building, Climatic Zone 4
44
Table 36 Optimum Projection and Net Present Value – Residential Building, Climatic Zone 1
45
Table 37 Optimum Projection and Net Present Value – Residential Building, Climatic Zone 2
45
Table 38 Optimum Projection and Net Present Value – Residential Building, Climatic Zone 3
46
Table 39 Optimum Projection and Net Present Value – Residential Building, Climatic Zone 4
46
Table 40 Combination Measures Economic Analysis – Office Buildings
47
Energy Analysis and Economic Feasibility Study
:: v ::
Table 41 Combined Simulations for Residential Buildings
47
Table 42 Range in Optimum Shell Properties to Maximize NPV (for all 9 scenarios) – Office
48
Table 43 Range in Optimum Shell Properties to Maximize NPV (for all 9 scenarios) – Residential
48
Table 44 Optimum Roof and Wall Insulation levels by Climate Zone
50
Table 45 Glazing thermal transmittance requirement
50
Table 46 Variations of Buildings for the EFRreq Analysis
53
Table 47 Selected and Limit Values of SC and projection factor for overhang
54
Table 48 Case Study Analysis to Determine the WWRreq – Office Buildings
54
Table 49 Thermal Standard Requirement for WWRreq – Office Buildings
55
Table 50 Variations of Buildings for the EFRreq Analysis
55
Table 51 Selected and Limit Values of SC and and projection factor for overhang
55
Table 52 Case Study Analysis to Determine the WWRreq – Residential Buildings
56
Table 53 Thermal Standard Requirement for EFRreq – Residential Buildings
56
Table 54 Economic Growth Rate
57
Table 55 Projected Population Growth
57
Table 56 Projected Number of Households
58
Table 57 Projected Number of Residences
58
Table 58 Forecast of the Residential Building Area that will Comply with the Thermal Standard
59
Table 59 Forecast of the Office Building Area that will Comply with the Thermal Standard
60
Table 60 Office Building Annual Base Case Energy per m2 and Savings for Heating & Cooling
61
Table 61 Projected Cumulative office built-up area (2010-2029)
62
Table 62 Projected Energy Savings from Office Buildings (2010-2029)
62
Table 63 Residential Building Annual Base Case Energy per m2 and Savings for Heating & Cooling 63 Table 64 Projected cumulative residential built up area (2010-2029)
63
Table 65 Projected Energy Savings from Residential Buildings (2010-2029)
63
Table 66 Summary of the Energy Savings at Building Input
64
Table 67 projected avoided CO2 emissions (2010-2029)
64
Energy Analysis and Economic Feasibility Study
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:: EXECUTIVE SUMMARY This Study has sought to answer questions such as: • • • •
How much energy can be saved by building envelope conservation measures? How much increase in construction cost would occur? What would be the financial impacts on building developers and owners? What would be the economic and environmental impacts at the national level?
The methodology used in this study consisted of conducting parametric hourly energy simulations for selected representative building types and then of conducting an economic analysis featuring the calculation of the net present value (NPV) of each energy conservation measures in order to assess its economical interest. The projected heating and cooling energy savings resulting from the application of the Thermal Standard for buildings vary between 10% in climatic zone 1 (coastal) to over 50% in Climatic zone 4 (high mountain).
Residential Buildings
Office Buildings
1.000 0.900 0.800 0.700
GJ/m2
0.600 0.500 58%
0.400
57% 45%
0.300
47%
43% 13%
10%
23%
0.200 0.100 0.000 CZ 1
CZ 2
CZ 3
CZ 4
CZ 1
CZ 2
CZ 3
CZ 4
Climatic Zone Base Case
Thermal Standard
The incremental construction cost of the measures that have a positive net present value, vary from a no cost (savings on equipment are higher than the cost of envelop energy conservation measures) to a cost that is more significant. The average cost for office buildings was around 7.85 USD/m2 while for residential buildings it was around 5.92 USD/m2. The positive net present value obtained for all retained energy conservation measures translated into a payback period between zero (immediate payback) to a maximum of 6 years. The average payback period for office buildings was around 5.2 years while for residential buildings it was around 0.9 years if we consider that comfort conditions are to be met for higher hours of operation. Over a 20 year period, the projected national benefits resulting from the application of the Thermal Standard include the avoidance of around 1.4 million tons of oil-equivalent, and the avoidance of an associated 6 million tons of CO2. The economic savings resulting from the avoided energy will vary in magnitude depending on the price of fuel and diesel oil. Average estimations indicate savings in the range of 500 million USD. These savings come from cost effective measures and further highlight the positive impacts of the application of the thermal standard for Buildings in Lebanon. Energy Analysis and Economic Feasibility Study
:: vii ::
1
INTRODUCTION
The aim of the study is to determine cost effective thermal improvement levels for the Thermal Standard for Buildings in Lebanon. The heating and cooling energy used by various building types under various scenarios of envelope improvement was determined by computer simulations to establish a realistic economic analysis of improvement to building envelopes. Two types of mid-rise buildings were simulated, an office building and a residential building. The simulations were conducted using five different climatic weather files (four climatic zones were finally retained for the thermal standard) representing the full range of weather experienced in Lebanon. This provided a good representation of the energy usage in different regions. Then parametric simulations were realized from these ten base simulations (two building types and five weather files) to determine the impact of variation in building construction practices and materials. The Parameters investigated included: thermal transmittance levels (wall. roof and windows), window sizes and solar (shading) characteristics as well as architectural shading devices (fins and overhangs). A total of 1460 model runs were conducted for the parametric analysis. The economic analysis of each improvement was evaluated by calculating the direct payback period and the net present value of the investment and the generated savings for the various climatic zones of Lebanon.
1.1 1.1.1
Methodology of the Energy Analysis Selection of Energy Simulation Software
The Visual-DOE3 program simulates the envelope heat gains and losses as well as the operation of various types of cooling and heating systems in buildings. The weather conditions are specified as typical year hourly weather files and are used to calculate the energy use, (electrical, fuel, gas) associated with cooling and heating loads. The software also takes into account temperature set point schedules, building occupancy schedules, lighting and equipment schedules and other energy end-uses as infiltration and ventilation. The simulation program is a powerful and flexible energy analysis tool for buildings. Professional journals have highlighted the value of this modeling tool for the evaluation of various alternative energy efficiency options. Nowadays, the dynamic simulation program DOE-2.1E and its interface Visual-DOE3 are one of the reliable energy prediction tools on the market. The software Visual-DOE3 was used to model the operation and energy consumption of space conditioning systems in each building type assuming a building mass representative of current construction practice in Lebanon. For walls, the insulation was assumed to be integrated between two wall layers to various levels of thermal transmittance. It is to be noted however, that the Visual-DOE3 software does not model Thermal Bridges. As such, the latter could not be integrated within the scope of the present study, but should be the subject of future analysis.
1.1.2
Outdoor Design Conditions
Weather records for each of the climatic zones in Lebanon were analyzed statistically to determine the 99% and 97.5 % probability values of minimum winter dry-bulb temperatures and maximum summer dry-bulb and wet-bulb temperatures. The results are presented in Table 1. Energy Analysis and Economic Feasibility Study
:: 1
::
Table 1 Outdoor Design Conditions for Lebanon Climatic Zone Zone 1: Coastal Zone 2: Western Mid-Mountain Zone 3: Inland plateau Zone 4: High mountain
1.1.3
Winter ºC Design Dry-Bulb 99% 97.5% 6 7 -2 -1 -5 -4 -10 -7
Summer ºC Design Dry-Bulb Design Wet-Bulb 1% 2.5% 5% 1% 2.5% 5% 34 33 32 27 26 25 32 31 31 22 22 21 37 36 35 22 21 21 31 30 30 22 21 21
Indoor Temperature Assumptions
The decision of average indoor temperatures that should be used for the simulations involves some hypothesis as there are two aspects of uncertainty in determining the appropriate comfort level for Lebanon: • •
First, the indoor temperatures chosen by the building occupant. Indoor temperatures in Lebanon are quite varied because most people heat or cool living areas intermittently. Second, the way that the thermostat location and settings in the living areas affect temperatures in other zones of the building and corresponding heat losses.
The occupants of the residences control to a large extent both temperature setting of the control thermostat(s) and the distribution of heated or cooled air through the building by leaving doors between zones of a building open or closed. However, the purpose of a thermal standard is not only to avoid excessive heating and cooling loads according to current practices, but also to insure that in the future buildings will have an envelope that could provide a good performance if heating or cooling equipment are added or if comfort level is increased in buildings. The penetration rate of heating and cooling equipment indicates that inhabitants in Lebanon need to meet a certain comfort level. For these reasons, the complete analysis was performed with residential buildings fully heated and conditioned to comfort level. For office buildings, the normal occupancy schedule was used as the period of time that the space conditioning was operated. The heating and cooling schedule which was considered as operation practice in Lebanon consisted of: Living zones heated and/or cooled during the day and night. Heating and cooling are determined by the thermostat set points. The set-point temperatures (operative temperatures) are 21.1ºC for heating in winter and 23.3 ºC for cooling in summer.
1.1.4
Selection of Building Types for Simulation
In order to select representative buildings, statistics of the existing building stock by region were examined. These data are presented in Tables 2 and 3. Table 2 Breakdown of Existing Building Units (up to 1995) according to use
Beirut Mount Lebanon North Lebanon Beqaa South Lebanon Nabatieh Total
No. of Bldg. Units 159,438 611,346 257,514 178,879 152,367 96,835 1,456,379
Residential
Commercial
115,728 462,804 177,961 121,198 111,136 73,018 1,061,845
42,703 146,544 76,417 57,239 40,924 23,671 387,498
Mixed R/C 510 779 1,317 107 206 67 2,986
Not Determined* 497 1,219 1,819 335 101 79 4,050
Source: Administration Centrale de la Statistique – Etude Statistique No. 3, 4, 6, 8, 11 – Recensement des immeubles et des etablissements – 1996
Energy Analysis and Economic Feasibility Study
:: 2
::
Table 3 Average number of Floors per Building Permit 1994
Total No. of Permits
Total Area in m2
Total No. of Floors
Beirut Suburbs of Beirut Rest Mount Lebanon North Lebanon Beqaa South Lebanon Nabatieh
222 1,273 2,853 1,292 961 951 770
733,000 1,497,000 1,806,000 807,000 520,000 712,000 343,000
1,689 3,884 6,230 2,821 1,669 2,161 1,393
Average Area (m2) per Permit 3,301.8 1,175.9 632.9 624.9 540.6 748.4 445.0
Total
8,322
6,418,000
19,847
771.0
2.3
1995
Total No. of Permits
Total Area in m2
Average No. of Floors per Permit 8.4 3.7 2.2 2.5 1.8 2.2 1.8
184 1,905 4,457 1,569 1,185 1,179 938
650,000 2,560,000 3,479,000 1,468,000 588,000 850,000 423,000
1,543 6,988 9,968 3,913 2,076 2,589 1,695
Average Area (m2) per Permit 3,531.9 1,344.0 780.5 935.8 495.9 721.1 450.7
Total
11,417
10,018,000
28,772
877.4
2.5
1996
Total No. of Permits
Total Area in m2
Average No. of Floors per Permit 7.3 3.1 2.3 3.5 1.8 2.2 1.8
Beirut Suburbs of Beirut Rest Mount Lebanon North Lebanon Beqaa South Lebanon Nabatieh
Total No. of Floors
Average No. of Floors per Permit 7.6 3.1 2.1 2.2 1.7 2.2 1.8
146 1,801 4,725 1,738 1,202 1,129 711
410,000 2,738,000 3,602,000 1,241,000 729,000 788,000 379,000
1,059 5,540 10,643 6,070 2,113 2,449 1,295
Average Area (m2) per Permit 2,806.5 1,520.3 762.4 714.3 606.5 698.2 533.2
Total
11,452
9,887,000
29,169
863.4
2.5
1997
Total No. of Permits
Total Area in m2
Average No. of Floors per Permit 7.6 3.6 2.4 2.6 2.2 2.4 1.9 2.6
Beirut Suburbs of Beirut Rest Mount Lebanon North Lebanon Beqaa South Lebanon Nabatieh
Beirut Suburbs of Beirut Rest Mount Lebanon North Lebanon Beqaa South Lebanon Nabatieh Total
Total No. of Floors
Total No. of Floors
164 1,452 3,297 2,210 1,288 1,533 1,055
520,000 2,025,000 2,963,000 1,468,000 1,102,000 2,032,000 613,000
1,244 5,214 7,941 5,725 2,773 3,688 2,024
Average Area (m2) per Permit 3,169.2 1,394.5 898.7 664.1 855.6 1,325.5 581.4
10,999
10,723,000
28,609
974.9
Source: Administration centrale de la statistique, Etude Statistique No. 15, Dec. 1999, Les permis de construire au Liban entre 1994 et 1997
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Table 4 Breakdown of building permits (1994-1997) according to Use
Year 1994 Year 1995 Year 1996 Year 1997
Total Area in m2 6,418,000 100% 10,018,000 100% 9,887,000 100% 10,723,000 100%
Residential
Mixed R/C
3,144,000 49% 5,964,000 59.5% 6,014,000 60.8% 6,541,000 61%
1,230,000 19.2% 1,888,000 18.9% 1,642,000 16.6% 2,177,000 20.3%
Commercial 244,000 3.8% 475,000 4.7% 779,000 7.9% 1,115,000 10.4%
Not determined* 1,800,000 28% 1,691,000 16.9% 1,452,000 14.7% 890,000 8.3%
Source: Administration centrale de la statistique, Etude Statistique No. 15, Dec. 1999, Les permis de construire au Liban entre 1994 et 1997.
For the purpose of selecting the most appropriate building types in order to conduct the analysis for the optimization of the envelope thermal characteristics within limited scope of this study, the following building types were selected to represent the residential and the other sectors:
Residential buildings: Multifamily housing It was decided that because of the limited scope of the project, only a five floor residential building would be modeled. It is expected that in future revisions of the thermal standard (for the mandatory introduction of the standard), this limitation will be addressed by modeling different types of residential buildings to see the sensitivity of the recommendations to the building major parameters. However, for the initial voluntary introduction of the thermal standard, the selected building can be considered representative of the building stock in Lebanon.
Other buildings: represented by an office building The majority of buildings in this category are commercial or institutional buildings and will both require space heating and cooling, which may or may not have been installed at the time the building was built. It was decided to use a five floor office building in the model study.
1.1.5
Current Construction Practices in Lebanon
The main elements that characterize the building envelopes in Lebanon include: • • •
•
Walls: Predominantly consist of hollow concrete blocks with a thickness of 15-20cm with 1 cm of plaster on each side. Exterior cladding when installed is typically stone cladding (limestone, sandstone, granite, etc.). Roofs: Are either flat or tilted. They are usually not insulated. Pitched roofs tend to be covered with tiles. Windows: Are typically made of single or double glazing (single is still the most popular) with aluminum frames. Glass panes are usually six millimeters thick and in some cases are tinted or with a reflective coating. Some high end buildings use double glazing but it is not the dominant trend. Low-e windows have been reported in some projects, but it is a relatively new trend. Other architectural features: Balconies are widely used in residential buildings. They provide some shading for windows.
Although this type of construction has the advantage of thermal mass, some shortcomings include: • •
Poor thermal performance and poor waterproofing of walls and roofs. Concrete is a good heat conductor and therefore allows heat to pass through either direction according to temperature differences between indoor and outdoor. Windows tend to have a high level of heat transmission due to air leakage across the frame and to heat transfer across the single glass pane and frame.
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1.1.6
Building Models
The two buildings selected for the analysis are described in this section. Residential Building Model The baseline residential building can be characterized as follows: The building has a rectangular floor plan, is five floors high and has a flat roof. It is built of hollow concrete walls, concrete floor slabs and is plastered inside and out. The details of construction, materials, fenestration, schedules and set points are described in detail in Annex 1. Commercial Building Model The base commercial building can be characterized as follows: The building has a rectangular floor plan, with the longer dimensions facing north and south. The building is five floors high and has a flat roof. The details of construction, materials, fenestration, operational schedules and set points are described in detail in Annex 2. Table 5 presents the composition and the thermal characteristics of Roofs and exterior walls including the thermal resistance (R-values) and thermal conductance (U values) excluding the effect of the air films. Table 5 Base Case Roof and Wall Compositions of the Residential and Office Buildings Component
Roof
Layer 1 2 3 4 5
Material Tile Sand Weatherization Concrete Plaster
Thickness (mm) 30 100 10 200 10
Total
Wall
U value 1 2 3
(W/m2 ºC) 10 150 10
Plaster Hollow block Plaster
Total (W/m2 ºC)
U value
RSI value (m2 ºC/W) 0.0882 0.1176 0.0588 0.1129 0.0139 0.3913 2.56 0.0139 0.2636 0.0139 0.2914 3.432
Both base models have 6 mm thick clear glass, single pane windows with a thermal conductance (U factor) of 6.16 W/m2.C and a shading coefficient of 0.95.
1.1.7
Building Operating Schedule
In order to avoid dealing with a multiplicity of building variations in the model, their operating schedule of operation were standardized to be the same for all weather zones. It was assumed that residential buildings are occupied full-time, whereas the commercial buildings are occupied parttime according to the day of the week and associated hours of occupancy.
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1.2
Methodology of the Economic Analysis
The methodology used in this study is to calculate the net present value (NPV) of each energy conservation measures in order to assess its economical interest. Annex 4 provides more information on the economic analysis approach and the discount rate selected. The main findings of these analyses are summarized in the following sections. The economic analysis was evaluated by determining the direct payback period and the net present value (NPV) of each improvement. The economic analysis took into consideration the energy cost forecast, discount rates, incremental costs and energy savings calculated for each option. The scenarios used for the economic analysis, This section discusses the hypothesis used for the economic analysis including the length of the study period, the price of energy, the discount rate and the combination of these hypotheses into nine individual scenarios for a sensitivity analysis. 1.2.1
General Parameters for the Net Present Value Calculation
The net present value analysis was conducted in constant value of money stream. The study period for the economic analysis was selected as 20 years. This period is appropriate as the measures considered have a very long life and in reality will extend for some of them over the 20 years period. For instance, wall and roof insulation and architectural shading can be considered permanent measures. A period of 20 years is reasonable for windows. No residual value for the energy conservation measure investments were considered at the end of the study period. 1.2.2
Energy Prices
The future evolution of energy prices cannot be predicted with certainty. For the purpose of this study, three scenarios were developed with different costs of energy in USD per barrel for crude oil and in USD per liter for diesel oil. Please see Annex 3 for the rationale and references. The assumed price of crude oil over the next 20 years (in constant dollars) is presented in Table 6.
Table 6 Crude Oil 20 Year Forecast Assumptions Alternative Low Base High
Price (USD 2002/Barrel) 20.452 25.565 30.678
Remarks - 20% from the base case + 20 % from the base case
Consequently, as explained in Annex 3 corresponding diesel oil prices are as presented in Table 7. Table 7 Assumed Diesel Oil Prices Scenario Low Base High
Price (USD 2002/liter) 0.24 0.30 0.36
Remarks 20% lower than base case 20% higher than base case
Based on usage of electricity by the base case buildings for space heating and cooling and the current rate structures for electricity for the various types of customers, the rates were estimated in Annex 3 for three scenarios. Table 8 represents the respective electricity prices for each customer class adjusted for the crude oil price forecasts presented in Table 6.
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Table 8 Cost of Electricity in LP and US$ Per Kwh Scenario Low case Base case High case
1.2.3
Residential houses (LP or USD/kWh) 234.300 – 0.1562 241.450 – 0.1609 248.600 – 0.1657
Offices and others (LP or USD/kWh) 213.000 – 0.142 219.500 – 0.14633 226.000 – 0.1506
Discount Rates
Several factors were taken into consideration when determining the discount rate (excluding the risk factor associated with the variability of energy consumption from year to year due to weather variations) The factors are described in Annex 5. Very briefly, they included: • • • •
Current national bond interest rates; A country risk interest premium; Consideration of a discount rate for investors without opportunities to invest outside of Lebanon (interest rates on Lebanese government treasury bonds were taken); Consideration of a discount rate for investors with opportunities outside of Lebanon; the country risk premium was taken from the debt rating given by rating agencies. The assumed discount rates are presented in Table 9.
Table 9 Assumed Discount Rates Name of Case Base Case High Case Low Case
1.2.4
Discount Rates 12% 16% 8%
Establishment of Simulation Scenarios
Using the above three scenarios about the price of crude oil (and the subsequent effects on the price of diesel fuel and electricity) and the three discount rate scenarios, the set of simulation scenario parameters in table 10 were established for the simulations. Table 10 Presentation of the Nine Scenarios Parameter 1 – Energy Price
Low alternative
Base High alternative alternative 0.24 0.30 0.36 0.1562 0.1609 0.1657 0.1420 0.14633 0.1506 Resulting combined scenarios
Domestic Diesel Oil (EN 590) Price ($/liter) Electricity Price - Residential ($/kWh) Electricity Price - Offices and Other ($/kWh) Parameter 2 – Discount Rate Low Alternative for Discount Rate at 8% Base Alternative for Discount Rate at 12% High Alternative for Discount Rate at 16%
Scenario 3 Scenario 6 Scenario 9
Scenario 1 Scenario 4 Scenario 7
Scenario 2 Scenario 5 Scenario 8
Finally, the nine scenarios that became the basis of the economic and sensitivity analysis assembled from the above price assumptions are presented in Table 11. Scenario number 4 is the preferred scenario which will be used to establish the recommendation for the thermal standard. The other eight scenarios will be used to perform a sensitivity analysis of the recommendations made.
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Table 11 Parameters that will be applied in the Nine Scenarios Scenario
Residential Buildings
Office Buildings and other Non-residential buildings
1.2.5
1 2 3 4 (Preferred) 5 6 7 8 9 1 2 3 4 (Preferred) 5 6 7 8 9
Discount Rate (%) 8 8 8 12 12 12 16 16 16 8 8 8 12 12 12 16 16 16
Diesel Oil Price (USD/liter) 0.30 0.36 0.24 0.30 0.36 0.24 0.30 0.36 0.24 0.30 0.36 0.24 0.30 0.36 0.24 0.30 0.36 0.24
Electricity Price (USD/kWh) 0.1609 0.1657 0.1562 0.1609 0.1657 0.1562 0.1609 0.1657 0.1562 0.14633 0.1506 0.1420 0.14633 0.1506 0.1420 0.14633 0.1506 0.1420
Assumptions on Material and Labor Cost for Energy Saving Measure
Incremental costs of adding insulation, better windows and shading were estimated to determine the additional capital cost required to achieve energy savings in combined space heating and cooling of the model buildings. The cost assumptions are detailed in Annex 6. 1.2.6
Assumptions for the Avoided Cost of HVAC Systems
The economic analysis took into consideration the avoided cost of the heating and cooling equipment that could be reduced in size if the building is thermally improved. Annex 7 provides the details of the values used. The avoided costs used for the residential and office buildings are the following: Building Type
Heating (USD/kW)
Cooling (USD/kW)
Residential Buildings
69.56
577.55
Office Buildings
63.97
177.71
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2
ENERGY ANALYSIS
Parametric simulations were conducted on the two selected building types by varying thermal transmittance levels of the following building components: walls, roofs, and glazing. Also, parametric simulations were conducted for the glass shading coefficient, the window-to-wall ratio and the architectural shading factor (fins and overhang). The simulations were conducted for each weather station to assess the difference in economic return of various improvements in different climates. Each simulation involved changing one of the parameters, while other parameters were maintained at the base case value. Additionally, limited selected combined simulations were conducted.
2.1
Parametric Variations of Roof U-value
The roof thermal insulation level was varied between the simulation runs as indicated in Table 12. Table 12 Parametric Alternatives of Roof U factor Case
Base Alternative 1 Alternative 2 Alternative 3 Alternative 4
U value (W/m2C) Excluding Air Films Residence Office 2.556 2.556 1.116 1.116 0.713 0.713 0.524 0.524 0.415 0.415
Equivalent Insulation (R=0.25) Thickness (cm) 0 2 4 6 8
Results for office buildings Figure 2 presents the results of the roof insulation parametric runs for the office building. The U factor, plotted along the x-axis, varies from 2.556 to 0.415. The space conditioning energy required for each roof insulation variation is plotted along the y-axis. The related reduction in energy usage for the whole building varies from 6% in Beirut to 11% in Cedars. This result is expectedly small, as the roof is a relatively small surface area compared to 5 floors of external wall area, and the window surfaces where a large portion of the heat loss/gain takes place. In Beirut, most of the savings result from the reduction in heating energy which is reduced by 39%. The cooling is nearly unaffected by the insulation with a mere 2% reduction. In the overall, the 39% reduction in heating is weighted by the fact that heating energy is much smaller than the cooling energy in Beirut, this results in an overall 6% in reduction. The small cooling reduction is a result of the double impact of the insulation during the cooling season. On the one hand, the insulation reduces the heat gain by transmission through the roof which has a tendency to reduce the cooling load in the building. On the other hand, added insulation makes the spaces at the top floor more efficient “greenhouses” so they trap the solar heat gain and are less able to dissipate the heat through conduction during the night. The insulation also results in a longer cooling period even during the day as the point of equilibrium of the building between the solar heat gain and the natural cooling through envelope walls shifts earlier at the beginning of the cooling season and later at the end of the cooling season. This results in an addition of load for the mechanical cooling system for the solar load portion.
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These effects (conduction and solar) cancel each other mostly and the resulting impact on the cooling energy consumption is very small. However, some results came from a limitation of the Visual-DOE3 system which cannot really model occupant behavior like the intentional opening of windows. In a case where the building was overheated during the week-end, it is likely that the occupants will open the windows (if the building has windows that can be opened) when arriving to their office Monday morning thus effectively creating a large natural ventilation that will help to rapidly dissipate the extra heat without much air conditioning. In buildings where the natural ventilation can be controlled by opening the window, we can even expect some savings on air conditioning because the insulation will help reduce the heat gain by transmission when the outside temperature is higher than the inside temperature. On the other hand, some buildings do not have opening windows and will require some air conditioning to remove the extra heat trapped inside during unoccupied hours (mainly week-ends) by the additional insulation of the building envelope. The tool limitation was somewhat mitigated by fixing the seasonal period where the air conditioning system is allowed to operate. With this approach, some of the period where there is a cooling load during the mid-season does not result in additional energy usage for the building. The assumption is that the occupants’ behavior to open windows will help reduce the negative effect of insulation by regulating the natural ventilation to evacuate any excess heat trapped inside the building and will effectively allow realizing some savings on air conditioning. These savings will be approximately the same as the extra cooling load added to building without opening windows so on a national basis it is expected that both trends will cancel each other. At the other end of the climate spectrum, Cedars climate has little effect on cooling load. In this case, the solar heat gain is more important in the building energy balance and the colder nights will allow a better evacuation of any excess heat. We can observe a slight increase in cooling consumption (1%). But as the cooling season is very short, this does not affect the results too much and the heating energy reduction of 12% provided an overall weighted energy reduction of 11% from worst to best case scenario. Here again, the added cooling energy was not taken into consideration in the economic analysis on the ground that occupants will open windows to dissipate any excess heat inside the buildings. When a building is insulated, this type of behavior for the cooling energy requirement is quite typical of Mediterranean climates where there is a delicate balance between the heat gain by conduction in summer and the solar and internal heat gain trapped in the insulated building. The insulated envelope is less able to dissipate excess heat when outside temperature falls at night or during the mid-season. During these periods we can simultaneously have a temperature outside that is below the desired internal temperature and important heat gain from the sun that will have a less important angle above the horizon than during the summer months. Both effects (conduction heat gain reduction and solar and internal heat trapping) are about the same order of magnitude and the resulting effect on the cooling energy if any will always be small. One of the conclusions we can draw from these simulations is that the thermal standard should focus on insulation in cold regions and on the rejection of solar heat gain in hot climate. Rejection of solar load can be achieved by reflective glazing, shading, and by reflective or light colored roof surfaces. For roofs, it is possible to use a reflective membrane to cover the roof and significantly reduce the cooling load but this faces several practical constraints that make this measure unacceptable as a requirement in a thermal standard. There is first the aesthetic and glaring effect the reflective roof could have on nearby buildings. The second issue is the access and cleaning of roof surfaces. As dirt accumulates on the reflective surface, there will be a degradation of the reflective effect and an increase in the interior cooling load. Thus as there is no way to insure a regular cleaning of the surface, this kind of measure is difficult to apply as a long term solution and as a requirement in a thermal standard but it can definitively be applied as a recommendation with the appropriate warning to designers and promoters.
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Figure 1 - Total Energy Usage vs. Roof U factor – Office Buildings Total Energy Usage vs Roof U Factor Office Buildings 1,400
1,200
600
Energy Usage (GJ)
800
Beirut Bayssour Kartaba Zahle Cedars
Cooling Energy (GJ)
1,000
Beirut Bayssour Kartaba Zahle Cedars
400
200
3.000
2.500
2.000
1.500
1.000
0.500
0 0.000
U factor (W/m2.C)
Figure 2 - Cooling Energy Usage vs. Roof U factor – Office Buildings Cooling Energy Usage vs Roof U Factor Office Buildings 350
300
250
200
150
100
50
3.000
2.500
2.000
1.500
1.000
U factor (W/m2.C)
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0.500
0 0.000
Figure 3 - Heating Energy Usage vs. Roof U factor – Office Buildings Heating Energy Usage vs Roof U factor Office Buildings 1,200
1,000
600
Heating Energy (GJ)
800 Beirut Bayssour Kartaba Zahle Cedars
400
200
3.000
2.500
2.000
1.500
1.000
0 0.000
0.500
U factor (W/m2.C)
It is interesting to compare the effect of the roof insulation on the last floor of the building. In the results shown for Cedars, the reduction in total energy is 11%. Figure 4 presents the effect of insulation on the last floor of the building. We can see that the reduction in total energy for this floor is higher with 34.1% of the total energy reduction. The reduction in heating is 37.0% while the cooling requirement increases by -4.4%. Figure 4 - Effect of the roof insulation – Cedars Energy usage for the 5th floor Cedars 500 450 400
300 250 200 150 100 50 0 3
2.5
2
1.5
1
U factor (W/m2.C)
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0.5
0
Energy Usage (GJ)
350
Heating energy Cooling energy Total Energy
Results for residential buildings For residential buildings, we observe a pattern similar to office buildings with the exception that the percentage savings generated are higher due to longer hours where comfort conditions are maintained compared to office buildings. For instance, the savings on total energy for Beirut, if 4 cm of insulation (R=0.25) is installed, are 7% compared to 6% in office buildings. Figure 5 presents the results for the total energy variation for the five considered weather files. In the case of residential buildings, the insulation of the roof has a neutral effect on the cooling load for the coastal zone. This is due to the fact that most of the cooling load comes from the solar gains, the internal gains and the infiltration. The insulation has also the perverse effect to trap more the internal gain and the solar gain inside the building thus increasing the duration of the cooling period and the energy usage for cooling. This effect reduces the gain that could be obtained by the reduction in conduction heat gain. The control strategy applied to residential buildings considered that the occupants will open windows as soon as the exterior temperature will be sufficiently low to insure a comfort level by natural ventilation.
Figure 5 - Energy Usage vs. Roof U factor – Residential Buildings Total Energy Usage vs Roof U factor Residential Buildings 1400
1200
Energy Usage (GJ)
1000
Beirut Bayssour Kartaba Zahle Cedars
800
600
400
200
0 3.000
2.500
2.000
1.500
1.000
U factor (W/m2.C)
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0.500
0.000
Figure 6 - Cooling Energy Usage vs. Roof U factor – Residential Buildings Cooling Energy Usage vs Roof U factor Residential Buildings 300
250
Cooling Energy (GJ)
200 Beirut Bayssour Kartaba Zahle Cedars
150
100
50
0 3.000
2.500
2.000
1.500
1.000
0.500
0.000
U factor (W/m2.C)
Figure 7 - Heating Energy Usage vs. Roof U factor – Residential Buildings Heating Energy Usage vs Roof U factor Residential Buildings 1400
1200
Heating Energy (GJ)
1000
Beirut Bayssour Kartaba Zahle Cedars
800
600
400
200
0 3.000
2.500
2.000
1.500
1.000
U factor (W/m2.C)
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0.500
0.000
2.2
Parametric Variations of Wall U-value
The alternative wall thermal transmittance levels used in the parametric analysis are presented in Table 13. The base case is a single wall without insulation. All the other cases include a double wall made of a 15 cm row of hollow concrete blocks and a 10 cm row of hollow concrete blocks. The insulation layer is installed in between the two rows of blocks. The alternatives consist of varying the thickness of insulation (R=0.25) by an increment of 2 cm. Table 13 Parametric Alternatives of Wall U factor Case
Base Wall Alternative 1 Alternative 2 Alternative 3 Alternative 4
Equivalent Insulation (R=0.25) Thickness (cm) 0 2 4 6 8
U value including air films (W/m2.C) Office Residence 3.432 1.011 0.669 0.500 0.399
3.432 1.011 0.669 0.500 0.399
Results of Wall insulation analysis for office buildings Figure 8 presents the results of the wall insulation parametric runs. The U factor varies from 3.432 W/m2.C with a single row of hollow block walls and no insulation to a minimum of 0.399 with double block walls and 8 centimeters of insulation (R=0.25) between rows of blocks. The U value constitutes the “x” axis while the global energy consumption at cooling and heating equipment input is shown in Gigajoules on the “y” axis. The related reduction in energy usage for the whole building varies from 9% in Beirut to 33% in Cedars when we insulate the walls from no insulation to a maximum of 8 cm expanded polystyrene. In all regions, most of the savings come from the reduction in heating energy. In Beirut, 8 cm insulation with expanded polystyrene will result in 77% reduction (indicating that the building will be in equilibrium between the losses and the internal and solar heat gains. However, the heating season and energy consumption is very small) while in Cedars, the reduction will be 36%. The effect of insulation on cooling requirement is approximately neutral or slightly negative. As it was the case for the roof, the mixed results we get for the cooling season is a result from a double effect of the insulation during the cooling season. On the one hand, the insulation reduces the heat gain by transmission through the wall which has a tendency to reduce the cooling load in the building (but the reduction is small). On the other hand, the added insulation makes the building a more efficient “greenhouse” so it traps the solar and internal heat gains more effectively and is less able to dissipate the heat through conduction (when the outside temperature drops below the interior temperature, especially in mid-season). These result in an additional load for the mechanical cooling system to extract the internal and solar loads for more hours in the year. Both effects (conduction transmission reduction and solar and internal gain trapping) mostly cancel each other and the resulting impact on the cooling energy consumption is neutral or negative. Also, for the wall analysis, it will be considered that the occupants’ control of natural ventilation in buildings (where windows can be opened) will offset any small increase in cooling energy requirement.
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Figure 8 - Total Energy Usage vs. Wall U factor - Office Buildings Total Energy Usage vs Wall U factor Office Buildings 1,400
1,200
Total Energy (GJ)
1,000
Beirut Bayssour Kartaba Zahle Cedars
800
600
400
200
0 4.000
3.500
3.000
2.500
2.000
1.500
1.000
0.500
0.000
U factor (W/m2.C)
Figure 9 and figure 10 present respectively the results for heating energy and cooling energy.
Figure 9 - Heating Energy Usage vs. Wall U factor - Office Buildings Heating Energy vs Wall U factor Office Buildings 1,200
1,000
Heating Energy (GJ)
800 Beirut Bayssour Kartaba Zahle Cedars
600
400
200
0 4.000
3.500
3.000
2.500
2.000
1.500
U factor Walls (W/m2.C)
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1.000
0.500
0.000
Figure 10 - Cooling Energy Usage vs. Wall U factor - Office Buildings Cooling Energy vs Wall U factor Office Buildings 350
300
Cooling Energy (GJ)
250
Beirut Bayssour Kartaba Zahle Cedars
200
150
100
50
0 4.000
3.500
3.000
2.500
2.000
1.500
1.000
0.500
0.000
U factor Walls (W/m2.C)
Results of Wall insulation analysis for residential buildings Figure 11 presents the results of the wall insulation parametric runs for residential buildings. The U factor varies from 3.432 W/m2.C with a single row of hollow block walls and no insulation to a minimum of 0.399 W/m2.C with double block walls and 8 cm of insulation (R=0.25) between the rows of blocks. The U factor is displayed on the x axis while the global energy consumption at cooling and heating equipment input is shown in Gigajoules on the y axis. The reduction in energy usage for the whole building varies from 11% in Beirut to 39-43% in the other regions when we insulate the walls from no insulation to a maximum of 8 cm expanded polystyrene. We observe the same trend as for the roof between office and residential buildings. Residential buildings produce more important savings than office buildings mainly because the hours of full time occupancy are considered higher. In all regions, most of the savings come from the reduction in heating energy as shown in figure 13. In Beirut, 8 cm of insulation will produce 94% reduction while in Cedars it will produce 47% reduction. The high result in Beirut just points to the fact that with this insulation level, the building does not need any additional heating and the already small heating load almost disappears. It is interesting to note that the impact in Beirut is the least important of the five weather files considered. In Beirut, we observe the same behavior as found for office buildings where the added insulation on walls has a positive and negative impact at the same time which results in little savings on the cooling energy. In Cedars, the effect is even negative but this will not be considered in the economic analysis as we will assume that the occupant will open the window to dissipate additional heat. In all the other climates, the heating impact is more important (Figure 13) since residential buildings are supposedly used for longer hours than office buildings.
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:: 17 ::
Figure 11 - Energy Usage vs. Wall U factor - Residential Buildings Total Energy Usage vs Wall U factor Residential Buildings 1400
1200
Total Energy (GJ)
1000
Beirut Bayssour Kartaba Zahle Cedars
800
600
400
200
0 4.00
3.50
3.00
2.50
2.00
1.50
1.00
0.50
0.00
U factor (W/m2.C)
Figure 12 - Cooling Energy Usage vs. Wall U factor - Residential Buildings Cooling Energy vs Wall U factor Residential Buildings 300
250
Cooling Energy (GJ)
200 Beirut Bayssour Kartaba Zahle Cedars
150
100
50
0 4.00
3.50
3.00
2.50
2.00
1.50
U factor Walls (W/m2.C)
Energy Analysis and Economic Feasibility Study
:: 18 ::
1.00
0.50
0.00
Figure 13 - Heating Energy Usage vs. Wall U factor - Residential Buildings Heating Energy vs Wall U factor Residential Buildings 1400
1200
Heating Energy (GJ)
1000
Beirut Bayssour Kartaba Zahle Cedars
800
600
400
200
0 4.00
3.50
3.00
2.50
2.00
1.50
1.00
0.50
0.00
U factor Walls (W/m2.C)
2.3
Parametric Variations of Window U-value and Shading Coefficient
Several alternative glazing options were tested in the simulations to determine the impact of various U factors and shading coefficients on the total energy usage of the residential and office building models during an entire year of operation. The following characteristics were changed in the alternatives: • The number of panes (1 or 2) • The shading coefficient of the glass • The U value (Width of air space, Filling gas, Low-e coating) These options not only represent the windows actually available in Lebanon but also the technologies that have not been widespread in Lebanon but are well proven in northern countries. The base case is a clear window with a single glazing and it represents the most frequently used window in Lebanon. The window types used for Options 1, 2 and 3 have a decreasing shading coefficient. These represent tinted or reflective windows that are popular in Lebanon. The window for Option 4 is a window with a low emissivity coating that is beneficial in winter to reduce heat losses through radiation by the windows. The low-e coating has little or no benefit during the cooling period. Options 5 to 9 are all double glazed windows. Option 5 is the most common clear window and it is becoming also popular in Lebanon partly because of its better thermal characteristics and partly because it provides a better sound attenuation. Windows for options 6 and 7 are tinted reflective glass that will reduce the solar heat gain. Options 8 and 9 are types of windows that achieve a very low thermal conductance through the combination of low-e films and/or argon filling between the panes. Alternative thermal conductance levels described above for use in both the residential building model and the commercial building model are summarized in Table 14.
Energy Analysis and Economic Feasibility Study
:: 19 ::
Table 14 Parametric Alternatives of Window U-factor and Shading Coefficient Composition
Option Base 1 2 3 4 5 6 7 8 9
Tint Clear Bronze Clear Clear Clear Clear Bronze Clear Clear Clear
Single (6 mm) Single (6 mm) Single (6 mm) Single (6 mm) Single (6 mm) Double (6mm, 12mm, 6mm) Double (6mm, 12mm, 6mm) Double (6mm, 12mm, 6mm) Double (6mm, 12mm, 6mm) Double (6mm, 12mm, 6mm)
Characteristics Reflective Low-e --------Tin-Oxide --Titanium ----e = 0.2 --------Stainless H ----e = 0.2 e = 0.2
Filling ----------Air Air Air Air Argon
U value W/m2 °C 6.16 6.16 6.12 5.50 4.27 2.74 2.74 2.50 2.0 1.7
SC (%) 0.95 0.71 0.58 0.45 0.84 0.81 0.57 0.26 0.78 0.79
Results of glazing analysis for office buildings The results achieved from these combinations of glazing properties are presented in figure 14. The base case window is identified with the letter “B” and the SC variations, low-e coating and gas filling are illustrated for Cedars (the shape is similar for the other regions except Beirut that is mirror like). The results are grouped into two main sections: single glazing on the left and double glazing on the right. If we first concentrate on the bottom left portion of the figure, we can observe an almost mirror shape curve between Beirut and Bayssour. For Beirut, we can see that as we decrease the shading coefficient of glass, the energy consumption has a tendency to decrease. This indicates that the solar load is important in this city and that the reduction of solar load by using tinted or reflective glass is beneficial. On the other hand, for the four other regions, we can see that an increase in shading coefficient alone without improvement of the U-value produces the inverse effect. The global energy consumption for the building increases as the shading coefficient is reduced. This means that for these locations, the solar heat gain in the heating season is more beneficial than the cooling load created during the cooling season. Thus reduction in solar heat gain in these zones may produce a global increase in the energy usage of the buildings. Figure 14 - Total energy usage with different glazing - Office Energy Consumed vs Glazing Types Office Buildings 1600
#2: SC= 0.58
1400
#3: SC= 0.45
#1: SC= 0.71
Cedars
SC= 0.95
1200
#7: SC= 0.26
B
#6: SC= 0.57
Beirut (Single) Bayssour (Single) Kartaba (Single) Zahle (Single) Cedars (Single) Beirut (Double) Bayssour (Double) Kartaba (Double) Zahle (Double) Cedars (Double)
#5: SC= 0.81
1000 Energy (GJ)
Cedars
#4: SC=0.84 & Low-e
#8: SC= 0.78 & low-e #9: SC= 0.79 & low-e & argon filling
Zahle 800 Zahle Kartaba 600
Bayssour
B
Kartaba
Bayssour
B
400 B
Beirut 200
Beirut
Single window
Double window
0 7
6
5
4
3
U Value (W/m.C)
Energy Analysis and Economic Feasibility Study
:: 20 ::
2
1
0
Figure 15 and figure 16 provide details for the heating and cooling energy for the various glazing types. Figure 15 - Heating energy usage with different Glazing – Office Heating Energy Consumed vs Glazing Types Office Buildings 1400 #2: SC= 0.58
Single window
#3: SC= 0.45
Double window
#1: SC= 0.71
1200
1000
#7: SC= 0.26
Cedars
SC= 0.95
#5: SC= 0.81
#4: SC=0.84 & Low-e
Energy (GJ)
Cedars
#6: SC= 0.57
B
Beirut (Single) Bayssour (Single) Kartaba (Single) Zahle (Single) Cedars (Single) Beirut (Double) Bayssour (Double) Kartaba (Double) Zahle (Double) Cedars (Double)
#8: SC= 0.78 & low-e
800
#9: SC= 0.79 & low-e & argon filling
Zahle 600 Kartaba 400
Zahle
B
Kartaba
B
Bayssour
200 Bayssour
Beirut
Beirut
B
0 7
6
5
4
3
2
1
0
U Value (W/m.C)
Figure 16 - Cooling energy usage with different Glazing – Office Cooling Energy Consumed vs Glazing Types Office Buildings 500 Single window
Double window
450 #8: SC= 0.78 & low-e
400
SC= 0.95
B
350
#1: SC= 0.71
Beirut
#4: SC=0.84 & Low-e
#5: SC= 0.81
Beirut
#9: SC= 0.79 & low-e & argon filling
#6: SC= 0.57
Beirut (Single) Bayssour (Single) Kartaba (Single) Zahle (Single) Cedars (Single) Beirut (Double) Bayssour (Double) Kartaba (Double) Zahle (Double) Cedars (Double)
#2: SC= 0.58
Energy (GJ)
#7: SC= 0.26
#3: SC= 0.45
300 B
250
Bayssour
Kartaba
B
Zahle
Bayssour
200 B
150
Zahle
Kartaba
B
100 Cedars
Cedars 50 0 7
6
5
4
3
U Value (W/m.C)
Energy Analysis and Economic Feasibility Study
:: 21 ::
2
1
0
Figure 17 and Figure 18 provide a zoom–in view of the Beirut, Bayssour and Qartaba single glazing. The shading coefficient values are marked near the data points so it may be easy to follow the trend. As the shading coefficient decreases in Beirut, so does the energy usage meaning that controlling the solar gain is beneficial. The point for option 5 that is at the far right is a window with a lower conductance but with a higher shading coefficient. We can immediately see that the low-e characteristics have not been significant compared to the fact that the shading coefficient is not improved too much for this window type. For Bayssour and Qartaba, windows with a shading coefficient of 0.95, 0.71 and 0.58 show a steady increase in energy consumption as the shading coefficient is reduced (higher window reflectivity). For the window with a shading coefficient of 0.48, the energy usage is decreasing contrary to the trend in Beirut. In this case, the best characteristics of the U factor for this window produce a reduction of heating energy in the winter season. The window with a clear glass and a low-e coating is clearly interesting in Bayssour and Qartaba that have a longer heating season. We observe that the same pattern for Bayssour and Qartaba is also applicable to Zahle and Cedars but with a higher amplitude in energy variations.
Figure 17 - Single Glass Glazing Results - 3 Cities – Office Single Glazing - 3 Cities 600
SC=0.58 550
SC=0.95 SC=0.71
500 Total Energy (GJ)
SC=0.45
SC=0.71 SC=0.58
SC=0.45
SC=0.95 SC=0.84 & Low-e Beirut (Single) Bayssour (Single) Kartaba (Single)
450 SC=0.95
SC=0.84 & Low-e
SC=0.71
400
SC=0.84 & Lowe
SC=0.58 SC=0.45 350
300 6.5
6
5.5
5
4.5
4
U value (W/m.C)
If we now concentrate on the right side of Figure 14, we can observe that a similar pattern emerges for double windows. Figure 18 enlarges the bottom right portion of Figure 14 for Beirut, Bayssour and Qartaba. Here again, we can observe for Beirut that the glass shading coefficient is the key characteristics for performance of a window. The incorporation of a low-e layer and argon gas filling have little effect and will not be economically attractive as their effect is almost neutral compared to a standard double glazed window. In this case, the added thermal resistance of these high performance windows has a double effect. In winter, they reduce the heat losses of the building which is beneficial, but in the summer and mid-season, they trap a larger quantity of heat that has entered during the day by solar radiation and they reduce the ability of the building to cool down naturally at night. This results in a higher cooling load that nullifies the positive effect in winter. This behavior of buildings in the Mediterranean climate is quite common. An improvement of the thermal envelope in certain climates may in fact have a detrimental effect on the overall energy consumption of buildings. This initial analysis from the raw results will be confirmed later in this document by an economic analysis.
Energy Analysis and Economic Feasibility Study
:: 22 ::
Figure 18 - Double Glazing Analysis for 3 Cities – Office Double Glazing - 3 Cities 550
SC=0.26
500
Total Energy (GJ)
SC=0.57 450
SC=0.26
SC=0.81 SC=0.81
400
SC=0.78 & low-e
SC=0.57
SC=0.81
SC=0.78 & low-e
Beirut (Double) Bayssour (Double) Kartaba (Double)
SC=0.79 & low-e & Argon
SC=0.79 & low-e & Argon
SC=0.57 350 SC=0.26
300 2.9
2.7
2.5
2.3
2.1
1.9
1.7
1.5
U value (W/m.C)
Glazing analysis results for residential buildings Figure 19 presents the results of the energy simulations for the glazing types in residential buildings. The shape is similar to the office buildings in general but the savings generated are more important due to the higher occupancy rate of the residential buildings. Reflective and tinted glass windows can reduce the energy consumption in Beirut by 11% which may be an attractive measure as the incremental cost will be quite limited. In other regions, the low-e coating on single glazing provides savings ranging between 14% (Bayssour) to 21% Cedars of the total energy for cooling and heating. For double windows, the savings follow a similar trend and the percentage reduction achieved by the double glazing with low-e coating is more important with 21% achieved in Bayssour and up to 34% in Cedars. Figure 19 - Total Energy Usage with Different Glazing – Residential Energy Consumed vs Glazing Types Residential Buildings 1,200
#2: SC= 0.58
1,000
#3: SC= 0.45 #1: SC= 0.71
Cedars
B: SC= 0.95
#7: SC= 0.26
800 Energy (GJ)
#6: SC= 0.57 #4: SC= 0.84 & low-e
#5: SC= 0.81
600
Beirut (Single) Bayssour (Single) Kartaba (Single) Zahle (Single) Cedars (Single) Beirut (Double) Bayssour (Double) Kartaba (Double) Zahle (Double) Cedars (Double)
Cedars #8: SC= 0.78 & low-e
Zahle #9: SC= 0.79 Low-e & argon
Zahle
Kartaba 400
Beirut
Bayssou
Kartaba
Beirut
Bayssour
200 Single window
Double window
0 7
6
5
4
3
U Value (W/m.C)
Energy Analysis and Economic Feasibility Study
:: 23 ::
2
1
Figures 20 and 21 present the detailed results of the simulation for the heating and cooling energy for the building. Figure 20 - Heating energy usage with different Glazing - Residential Heating Energy consumed vs Glazing types Residential building 1000 #2: SC= 0,58
Single window
#3: SC= 0,45
Double window
900 #1: SC= 0,71
800
Cedars
B: SC= 0,95
#7: SC= 0,26
700
Beirut (Single) Bayssour (Single) Kartaba (Single) Zahle (Single) Cedars (Single) Beirut (Double) Bayssour (Double) Kartaba (Double) Zahle (Double) Cedars (Double)
#6: SC= 0,57
Energy (GJ)
600
Cedars #4: SC= 0,84 & low-e
500 400
#5: SC= 0,81
#8: SC= 0,78 & low-e
#9: SC= 0,79 Low-e & argon
Zahle Zahle
300 Kartaba 200
Bayssou
Bayssour
Kartaba
100 Beyrouth
Beyrouth
0 7
6
5
4
3
2
1
U Value (W/m.C)
Figure 21 - Cooling energy usage with different Glazing - Residential Cooling Energy consumed vs Glazing types Residential building 400 Single window
B: SC= 0,95
Double window
#9: SC= 0,79 Low-e & argon
#5: SC= 0,81
350 #1: SC= 0,71
#4: SC= 0,84 & low-e
Beyrouth
#8: SC= 0,78 & low-e #6: SC= 0,57
#2: SC= 0,58
300
#3: SC= 0,45
#7: SC= 0,26
Beirut (Single) Bayssour (Single) Kartaba (Single) Zahle (Single) Cedars (Single) Beirut (Double) Bayssour (Double) Kartaba (Double) Zahle (Double) Cedars (Double)
Beyrouth
Energy (GJ)
250 Bayssour 200
Bayssou
Kartaba
150 Kartaba
Zahle
Zahle
100 Cedars
50
Cedars
0 7
6
5
4
3
U Value (W/m.C)
Energy Analysis and Economic Feasibility Study
:: 24 ::
2
1
Figure 22 and Figure 23 provide a close-up view of the single and double window results for Beirut, Bayssour and Qartaba.
Figure 22 - Single Glazing Analysis for 3 Cities - Residential Single Glazing - 3 Cities Residential Buildings 430 SC=0.58 410
SC=0.45
SC=0.71
390
Total Energy (GJ)
SC=0.95 SC=0.95
370
SC=0.58
SC=0.71
SC=0.45
Beirut Bayssour Kartaba
SC=0.84 & Low-e
SC=0.95
350
SC=0.71 330
SC=0.58 SC=0.45
SC=0.84 & Low-e
310 SC=0.84 & Low-e 290 6.5
6
5.5
5
4.5
4
U value (W/m.C)
Figure 23 - Double Glazing Analysis for 3 Cities - Residential Double Glazing - 3 Cities Residential Buildings 360
SC=0.78 & low-e
SC=0.81
SC=0.798 & low-e & Argon
350
SC=0.26
340
Total Energy (GJ)
330
SC=0.57 SC=0.57
320 SC=0.26
300
SC=0.798 & low-e & Argon
Beirut Bayssour Kartaba
SC=0.81
310
SC=0.78 & low-e SC=0.57
290
SC=0.81
SC=0.26
280 270
SC=0.798 & low-e & Argon
SC=0.78 & low-e
260 1.5
1.7
1.9
2.1
2.3
U value (W/m.C)
Energy Analysis and Economic Feasibility Study
:: 25 ::
2.5
2.7
2.9
2.4
Parametric Variations of Window to Wall Ratio
The Window to wall ratio of a building is defined as the ratio of the vertical window areas to the gross area of the vertical walls (the gross area is inclusive of the window area). The Skylight to roof ratio (SRR) is similarly defined as the ratio of the skylight area over the gross roof area (the gross roof area includes the skylight area). They are calculated respectively using Equations 1 and 2. WWR = Σ (Awi x SCwi x ASFwi) / Σ Av (1) SRR = Σ (Asi x SCsi) / Σ Ah (2) Awi SCwi ASFwi Av Asi SCsi Ah
= = = = = = =
Area of the individual vertical window (m2) Shading coefficient of the individual vertical window Architectural shading factor of the individual vertical window Area of all vertical surfaces (opaque walls + windows) (m2) Area of the individual skylight (m2) Shading coefficient of the individual skylight Area of all horizontal surfaces (roofs + skylights) (m2)
The Impact of the WWR and the SRR on the heating and cooling energy usage of a building are quite important. The thermal standard will set recommended effective fenestration ratios. These will take into consideration WWR and SRR ,the glazing characteristics, orientation, and architectural shading devices (fins and overhang). A series of simulations were thus performed for various percentages of windows to the gross wall ratios. In the Thermal Standard it will be recommended to put a limit to the WWR and SRR that designers or developers are allowed to use under the prescriptive path for compliance. To illustrate the necessity for this requirement, we can take the case of a building to be constructed in a region where there is no limit for WWR and no limit for window U-value but where there is a limit for wall and roof U-value. This can be the case in mild climates (like Lebanon Coastal regions) where double windows cannot be required by a regulation because of their low economic return. In such situation, a designer would construct a building with nearly 100% windows that would benefit from the absence of a thermal requirement for windows. This situation should be avoided. To avoid this type of situation, a maximum limit for WWR and SRR will be set in the prescriptive approach. It is important to point out that this will not place a constraint on building design, as buildings which do not comply with the prescriptive path, can demonstrate compliance through an alternative performance path. As such, in the proposed thermal standard for Lebanon, when the proposed building exceeds a certain maximum WWR and SRR set in the standard, the designer will need to take the performance path to demonstrate compliance. This requirement would insure that the thermal envelope of the proposed building will be thermally equivalent to a building with a more conservative WWR and SRR. In the performance path, the designer will need to simulate two buildings, the proposed building and a similar reference building that has the maximum allowed WWR and SRR as prescribed in the thermal standard. The total energy requirements of the proposed building should be less than or equal to those of the reference building. In practice, this will force the designer to take certain measures such as improving the U-values of walls or windows, or protecting the windows from the summer solar loads. Whatever solution is proposed, the designer will need to be demonstrated that the modified building will not consume more energy than the reference building. It is important to note that this threshold is implemented mainly because of the thermal transmittance characteristics of windows (U value) and their effect on the overall envelope thermal performance. Other characteristics related to the solar radiation heat gain like the solar reflection and architectural protection would be covered by another requirement called effective fenestration ratio (EFR) that will be discussed in a separate section.
Energy Analysis and Economic Feasibility Study
:: 26 ::
In order to determine what the maximum threshold value should be, a series of simulations were conducted by varying the WWR from 10% to 80% for office buildings and from 10% to 35% for residential buildings. The skylight to roof ratio was not determined analytically because of the limited scope of this study. It was rather fixed by inspection at a value of 0.05. The Window-to-wall ratios used in the parametric analysis for residential and office buildings are presented in Table 15. Table 15 Parametric Alternatives of Window to Wall Ratio Case Alternative 1 Alternative 2 Alternative 3 Base Case Alternative 4 Alternative 5 Alternative 6 Alternative 7 Alternative 8 Alternative 9 Alternative10
Office Buildings 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.50 0.60 0.70 0.80
Residential Buildings 0.10 0.13 0.16 0.20 0.22 0.25 0.28 0.30 0.35 ---
Results of the WWR analysis for office buildings The results of the simulations for office buildings are presented in Figure 24. The energy usage represented here is a combination of the energy input to a building heated by a central boiler and cooled by electric air conditioner.
Figure 24 - Variation of the Energy Usage with Increasing WWR – Offices Total Energy Usage vs WWR Office Buildings 1,600
1,400
Base case 1,200
GJ
1,000 Beirut Bayssour Kartaba Zahle Cedars
800
600
400
200
0 0.10
0.15
0.20
0.25
0.30
0.35
0.40
WWR
Energy Analysis and Economic Feasibility Study
:: 27 ::
0.50
0.60
0.70
0.80
Figure 25 and Figure 26 present the variation of the heating and cooling component caused by a change in the WWR for the office building.
Figure 25 - Variation of Heating energy usage with increasing WWR - Office Heating Energy Usage vs WWR Office Buildings 1,400
1,200
Base case 1,000
Beirut Bayssour Kartaba Zahle Cedars
GJ
800
600
400
200
0 0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.50
0.60
0.70
0.80
WWR
Figure 26 - Variation of Cooling energy usage with increasing WWR - Office Cooling Energy Usage vs WWR Office Buildings 700
600
Base case 500
Beirut Bayssour Kartaba Zahle Cedars
GJ
400
300
200
100
0 0.10
0.15
0.20
0.25
0.30
0.35
0.40
WWR
Energy Analysis and Economic Feasibility Study
:: 28 ::
0.50
0.60
0.70
0.80
From these results we can observe that the energy consumption for Beirut, Bayssour and Qartaba are relatively close. However, their climates are quite different as the largest energy usage in Beirut is for cooling. In Bayssour and Qartaba, the cooling load is lower than in Beirut but the heating load is higher which makes the building energy requirement slightly higher than Beirut. For Zahle and Cedars, the heating load is much larger than the cooling load and their energy requirements are much higher. The 0.25 WWR was considered to represent base case level of WWR for non residential buildings constructed in Lebanon. The same results can be presented in a different format to help determine what should be the maximum WWR. Figure 27 presents the information in a different format. In this figure, the energy usage related to 25% WWR was put as the reference, and the percentage variation from this value was calculated.
Figure 27 - Recommended Threshold to trigger the Performance Path - Office Energy Usage Increase (%) 60%
50%
Percentage Energy Increase
40%
Recommended Threshold for Performance Approach 30% Beirut Bayssour Kartaba Zahle Cedars
20%
10%
0% 0.10
0.15
0.20
0.25
0.30
-10%
0.35
0.40
0.50
0.60
0.70
0.80
WWR
-20%
-30%
We can observe from this figure that the energy usage for a building with 0.80 WWR will be between 32% and 55% more than a building with 0.25 WWR. The determination of the maximum WWR is essentially a judgment call as there is no specific formula that can fix this value. The threshold should be high enough to allow design flexibility and to avoid too many buildings going to the performance path that is more complex and more costly to implement (and more complex to handle for the government body in charge of compliance verifications). Much of the attention was put on the curve for Beirut since the coastal zone has the largest population and buildings and the maximum cooling loads. By inspection we can find that fixing a limit of 5 % of additional consumption over a “normal” building will fix the threshold of WWR at 0.30. If we want to allow 10% more energy for the threshold, then the WWR limit will go close to 0.35. In practice, a limit of 0.30 WWR would be a reasonable recommendation for Lebanon. As mentioned previously, this is not an absolute limitation, since designers have the option of using the performance path. For skylights, the current practice in Lebanon is to have very few skylights in the buildings. No simulation were realized for this particular components but using the same logics as for windows where a 5% deviation from the current trend is made acceptable, a 0.05 skylight should be used to trigger the switch to the performance approach. Energy Analysis and Economic Feasibility Study
:: 29 ::
Results of the WWR analysis for residential buildings A similar analysis was conducted for residential buildings and the results are presented in Figures 28, 29, 30 and 31.
Figure 28 - Variation of the Total Energy Usage with Increasing WWR - Residential Total Energy Usage vs WWR Residential Buildings 900
800
Base case
700
Energy Usage (GJ)
600 Beirut Bayssour Kartaba Zahle Cedars
500
400
300
200
100
0 0.10
0.13
0.16
0.205
0.22
0.25
0.28
0.30
0.35
WWR
Figure 29 - Variation of the Heating Energy Usage with Increasing WWR - Residential Heating Energy Usage vs WWR Residential Buildings 800
700
Base case
Energy Usage (GJ)
600
500
Beirut Bayssour Kartaba Zahle Cedars
400
300
200
100
0 0.10
0.13
0.16
0.205
0.22
0.25
WWR
Energy Analysis and Economic Feasibility Study
:: 30 ::
0.28
0.30
0.35
Figure 30 - Variation of the Cooling Energy Usage with Increasing WWR - Residential Cooling Energy Usage vs WWR Residential Buildings 500 450
Base case
400
Energy Usage (GJ)
350 300
Beirut Bayssour Kartaba Zahle Cedars
250 200 150 100 50 0 0.10
0.13
0.16
0.205
0.22
0.25
0.28
0.30
0.35
WWR
Figure 31 - Recommended Threshold to trigger the Performance Path - Residential Energy Usage Increase (%) Residential Buildings 40%
Percentage energy increase
30%
Recommended Threshold for Performance Approach 20% Beirut Bayssour Kartaba Zahle Cedars
10%
0% 0.10
0.13
0.16
0.205
0.22
0.25
0.28
0.30
0.35 WWR
-10%
-20%
Energy Analysis and Economic Feasibility Study
:: 31 ::
2.5
Parametric Variations of Window Orientation and Architectural Shading
This section presents the results obtained from a set of parametric simulations conducted for different orientations to determine the effect of the overhangs, fins or a combination of these two architectural shading devices on the solar load of windows over a complete heating and cooling season. The objective of this parametric analysis was to determine suitable architectural shading factors (ASF) that can be applied in the thermal standard to determine the effective fenestration ratio (EFRReq). This architectural shading factor incorporates the effect of the orientation of the façade, where each window of the building is located, and the shading effect of fins and overhangs. The architectural shading factor will then be combined with the window to wall ratio and the glazing shading coefficient to calculate the equivalent solar heat gain effect of the window compared to a clear single glass of 6mm thickness. To perform this analysis, windows on one façade (initially oriented toward the south) have been shaded with fins and overhangs of different dimensions as presented in Table 16. Table 16 Parametric Alternatives for the Evaluation of Architectural Shading Factors Parametric run Base case – No shading Category 1 – Overhang only
Category 2 – Fins only
Category 3 – Combined Fins and overhang
Alternative 1 Alternative 2 Alternative 3 Alternative 4 Alternative 5 Alternative 6 Alternative 7 Alternative 1 Alternative 2 Alternative 3 Alternative 4 Alternative 1 Alternative 2 Alternative 3 Alternative 4
Comments No fins and no overhang on the window 0.10 projection of the overhang 0.20 projection of the overhang 0.40 projection of the overhang 0.60 projection of the overhang 0.80 projection of the overhang 1.00 projection of the overhang 1.50 projection of the overhang 0.10 projection of the overhang 0.20 projection of the overhang 0.30 projection of the overhang 0.40 projection of the overhang 0.10 overhang and 0.10 fins 0.40 overhang and 0.20 fins 0.80 overhang and 0.40 fins 1.50 overhang and 0.60 fins
The results of each simulation were then analyzed by extracting cooling and heating information for each individual space in the building that has windows oriented towards one orientation selected for analysis and by looking more specifically at the solar thermal load created by the window. These simulations were carried out with clear windows as the thermal standard will separately consider the shading characteristics of the glazing (shading coefficient) and the shading from architectural devices. The total thermal cooling load caused by windows in all these spaces was then summed up over the complete cooling season. Then the contribution of the window solar heat gain to the heating of the building in the winter season was established. Each thermal load was then converted into cooling and heating energy required at the input of the heating and cooling equipment by considering appropriate efficiency factors. The resulting architectural shading factor can then be directly related to the energy usage effect of the shading device and the orientation for a clear window of 6mm thick. This analysis provides a series of values representing the impact of the orientation of windows and the shading caused by the architectural shading devices on the building energy requirements. After the initial analysis of the south orientation of the building, the same analysis was performed for the other main orientations of the building to see the combined impact of orientation and external shading devices on the cooling and heating load. Thus eight orientations (N, NE, E, SE, S, SW, W, and NW) were analyzed to determine the architectural shading factor (ASF).
Energy Analysis and Economic Feasibility Study
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The simulations were done only for Beirut on the rationale that the coastal region features the highest population and construction density. So the architectural shading factor should reflect the situation in this part of the country. It is obvious that the economics of fins and overhang will be higher in the coastal regions than in other parts of the country. It will be necessary to take that into consideration in the thermal standard. This will be done through a selection of an effective fenestration ratio (EFRreq) compatible with the economic return of architectural shading devices.
2.5.1
Overhang Analysis
Figure 33 presents the results of the parametric runs made on the overhang. The results are expressed as the architectural shading factor (ASF) which is the ratio of the annual energy usage created by the window solar gain on the orientation selected when the window is equipped with an overhang compared to the annual energy usage of an equivalent window without solar protection. The dimensions of the overhang are expressed as the projection factor (Figure 32). Figure 32 - Overhang Projection Factor Parameters
PFOverhang =
A B
PFOverhang = Projection Factor for overhang (dimensionless) A = Horizontal extension of the overhang from the vertical plane of the wall where the window is located (m) B = Distance between the bottom edge of the window and the bottom edge of the overhang (m)
We can see from Figure 33 that in the base case without overhangs, the solar cooling load and the useful solar heating gain in winter induce an energy requirement about four times lower on the north façade than on a south façade. Southeast, south and south-west façades have quite similar values and making a single recommendation for the architectural shading factor for these three orientations will make sense. Then the east and west orientations are close enough so that a single architectural shading factor could be developed for both of them. Finally, the northeast and northwest orientations can be grouped to determine a single architectural shading factor. Figure 33 - Architectural Shading Factor for Various Overhang Projections
Overhang Effect 1.20
Architectural Shading Factor
1.00
0.80
0.60
0.40
0.20
0.00 N
NE
E
SE
S
SW
W
Orientation Base case Overhang 0.80
Overhang 0.10 Overrhang 1.00
Energy Analysis and Economic Feasibility Study
Overhang 0.20 Overhang 1.50
:: 33 ::
Overhang 0.40
Overhang 0.60
NW
2.5.2
Fins Analysis
Figure 35 presents the results of the parametric runs made for the fins. The results are expressed as a ratio of the annual energy usage caused by the windows on the orientation and with the shading devices selected compared to the annual energy usage of a window on the south orientation. The dimensions of the fins are expressed as the projection factor (figure 34) Figure 34 - Fins Projection Factor Parameters
PF fins =
A B
PFfins = Projection factor for fins (dimensionless) A = Horizontal extension of the fins from the vertical plane of the wall that containing the window (m) B = Distance between the farthest side of the window to the face of fin that is close to the window (m)
Figure 35 presents the impact of fins on the architectural shading coefficient. We can see that fins are less effective than overhangs in reducing the energy usage for a building and this is understandable as they produce their main effect only when solar rays are coming from the left or right. Fins are not effective when the sun approaches the zenith (on south orientation).
Figure 35 - Fins Effect on the Architectural Shading Factor
Fins Effect 1.20
Architectural Shading Factor
1.00
0.80
0.60
0.40
0.20
0.00 N
NE
E
SE
S
SW
W
Orientation Base case
Fins 0.10
Energy Analysis and Economic Feasibility Study
Fins 0.20
:: 34 ::
Fins 0.30
Fins 0.40
NW
2.5.3
Combined Fins and Overhangs
Another series of simulations were conducted to analyze the combined impact of both fins and overhangs. The projections used for the fins and overhang are not the same. The test window used is about twice as wide as its height. To have an equal extension of the fins and overhangs from the wall plane, different projections have to be used. Figure 36 - Combined Effects of Fins and Overhang
Overhang and Fins Combined Effect 1.20
Architectural Shading Factor
1.00
0.80
0.60
0.40
0.20
0.00 N
NE
E
SE
S
SW
W
NW
Orientation Base case
2.6
Overhang 0.20, fins 0.10
Overhang 0.40, fins 0.20
Overhang 0.80, fins 0.40
Overhang 1.50, fins 0.60
Recommended Architectural Shading Factors
Tables 14, 15, 16 and 17 present the recommended values of the architectural shading factor for various configurations of fins, overhangs and for different orientations. These tables will be used directly in the thermal standard to determine the effective fenestration ratio (EFR) of a building. These architectural shading factors can be directly related to the energy requirements for a fenestration with a specific orientation and shading device compared to the energy consumption created by a clear single glass window with the same orientation but without shading devices. Table 17 should be used for windows that have no architectural shading devices i.e. without overhangs and fins. This Table just takes into consideration the orientation of the window to determine its impact on building energy consumption. For instance, an unprotected window facing north will induce 26% of the energy usage that will require a window of the same size, unprotected and facing south. If the orientation is centered between two orientations provided in the Table, the most conservative (higher) architectural shading factor should be used. Table 18 provides the values for the architectural shading factor for windows protected by overhangs only. The projection of each overhang should be calculated individually and applied to the specific window it protects. If the orientation is centered between two orientations provided in the Table, the most conservative (higher) architectural shading factor should be used.
Energy Analysis and Economic Feasibility Study
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Table 19 provides the values for the architectural shading factor for windows protected by fins only. The projection of each fin should be calculated individually and applied to the specific window it protects. If the orientation is centered between two orientations provided in the Table, the most conservative (higher) architectural shading factor should be used. Table 20 provides the values for the architectural shading factor for windows simultaneously protected by fins and overhangs. The Table assumes that the horizontal extension will be the same for fins and overhangs if the window is twice as large as its height. The projection of each protection should be calculated individually and applied to the specific window it protects. For projection factors falling between the values provided in the table, the closest projection factor for the overhang should be used. If the orientation is centered between two orientations provided in the Table, the most conservative (higher) architectural shading factor should be used. Table 17 Architectural Shading Factor for Unprotected Windows PF - Fins or Overhangs PF < 0.05
N 0.26
ASF per Orientation NE,NW E,W 0.47 0.82
S,SE,SW 1.00
Table 18 Architectural Shading Factor for Windows Protected by Overhang Only PF - Overhangs 0.05 ≤ PF < 0.15 0.15 ≤ PF < 0.30 0.30 ≤ PF < 0.50 0.50 ≤ PF < 0.70 0.70 ≤ PF < 0.90 0.90 ≤ PF < 1.25 PF ≥ 1.25
N 0.24 0.23 0.21 0.19 0.18 0.17 0.16
ASF per Orientation NE,NW E,W 0.43 0.74 0.40 0.68 0.34 0.57 0.31 0.49 0.28 0.43 0.26 0.38 0.24 0.31
S,SE,SW 0.89 0.80 0.64 0.54 0.46 0.41 0.34
Table 19 Architectural Shading Factor for Windows Protected by Fins Only PF - Fins 0.05 ≤ PF < 0.15 0.15 ≤ PF < 0.25 0.25 ≤ PF < 0.35 PF ≥ 0.35
N 0.23 0.20 0.19 0.17
ASF per Orientation NE,NW E,W 0.42 0.76 0.38 0.71 0.35 0.67 0.32 0.63
S,SE,SW 0.92 0.85 0.78 0.74
Table 20 Architectural Shading Factor for Windows Protected by Fins and Overhang PF – Fins and Overhangs Overhangs: 0.05 ≤ PF < 0.30 Fins: 0.05 ≤ PF < 0.15 Overhangs: 0.30 ≤ PF < 0.60 Fins: 0.15 ≤ PF < 0.30 Overhangs: 0.60 ≤ PF < 1.05 Fins: 0.30 ≤ PF < 0.50 Overhangs: PF ≥ 1.05 Fins: PF ≥ 0.50
N
ASF per Orientation NE,NW E,W
S,SE,SW
0.20
0.35
0.63
0.72
0.15
0.26
0.47
0.50
0.11
0.17
0.30
0.27
0.08
0.11
0.17
0.13
Energy Analysis and Economic Feasibility Study
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2.7
Selected Combined Simulations for Walls, Roofs and Windows
Selected combined simulations have been realized to explore the simultaneous impact of various energy conservation measures. This is important as the interaction among measures can sometimes be negative (a combined measure may yield less results than the individual measure) or sometimes be positive (the combination provides better savings). For instance, the combination of wall and roof insulation usually leads to a lower energy savings than the individual simulation of savings for the two components because they both have a tendency to reduce the heating season. On the other hand, an example of a beneficial combination would be the combination of reflective windows and architectural shading with wall insulation. As the reflective window and architectural shading will reduce the solar heat gain in winter, the insulation of walls usually becomes more cost effective. The reflective window and architectural shading will also reduce the negative effect of wall insulation observed during the cooling season as the solar load will be reduced during summer and mid-season. Table 21 presents the parameters used for the office and residential buildings. The base case is the same as the one used in the individual analysis. It corresponds to a non-insulated building with single clear glazing. Model 1 combines the insulation of roof, reflective glazing and architectural overhang on the south, southeast and south-west side. This model is optimized for coastal regions. Model 2 is a combination of roof and wall insulation together with a limitation of the solar heat gain in summer by a combination of reflective glazing and overhang. This model is a compromise anticipated for regions with higher heating loads. Table 21 Combined Simulation Parameters Combined Simulations for Offices and Residential Buildings Walls Roofs Windows Overhang
Base Case
Model 1
Model 2
Single, non insulated U=3.432 W/m2 C Non-insulated U=2.556 W/m2C Single 6 mm clear U=6.160 W/m2C
Single, non insulated U=3.432 W/m2 C Insulated 6 cm. EPS U=0.524 W/m2C Single reflective with SC=0.45 U=5.490 W/m2C
Double, insulated with 6 cm EPS U=0.500 W/m2 C Insulated 6 cm EPS U=0.524 W/m2C Single reflective with SC=0.45% U=5.490 W/m2C
No overhang
PF = 0.40 for E,W,S side
PF = 0.40 for E,W,S side
Results for office buildings Figure 37 presents the results of the combined simulations for the five weather stations. We can see that the model 1 building provides savings in all zones except in Cedars where the reduction in solar heat gain results in a net increase (3%) of the total energy usage. For Beirut, the model 1 building provides 17% energy savings. In the other locations (Bayssour, Qartaba and Zahle) the savings range from 0.8% to 3.4% of the annual heating and cooling energy usage. The results confirm what was expected i.e. that the model 1 building is more adapted for the coastal regions. The model 2 building (with wall insulation) performs quite well in all regions. The total savings in Beirut are 28.8% while the savings in Bayssour, Qartaba, Zahle and Cedars vary from 28% to 32%. This is due to the fact that the glazing and overhang for the model 2 building reduce the negative impact of wall insulation during the summer and mid-season which results in larger savings that reflect on the economic analysis. This is an improvement over the individual runs but the impact is not drastic as the combined simulations provide an added reduction of 5.7% of the total building energy consumption over the total of what will be provided by the summation of the individual measure results.
Energy Analysis and Economic Feasibility Study
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Figure 37 - Combined Measure Results in Office Buildings Combined Measures Office Buildings 1,800
1,600
1,400
Energy Usage (GJ)
1,200 Beirut Bayssour Kartaba Zahle Cedars
1,000
800
600
400
200
0 Base
Mod 1
Mod 2
Building Model
Results for residential buildings Figure 38 presents the results of the combined simulations for the residential buildings. As for the office buildings, both models provide savings with the notable exception of Cedars where the model 1 results in an increase of energy consumption. This is due to the large reduction of positive solar heat gain during the heating season caused by the reflective windows included in the model 1 building. We can also see that the model 1 produces a more important energy reduction in Beirut than in Qartaba, Bayssour or Zahle as the reflective windows are more effective in the coastal areas. If we compare the combined results with the results of the individual simulations, we observe that for Beirut, the model 1 building provides a reduction in energy usage of 21.1 % while the model 2 provides a reduction of 30.5 %. In the regions of Bayssour, Qartaba and Zahle, the model 1 building provides 3.7% to 4% savings. The model 2 building in these zones ranges provides savings between 48% and 52%. For the other regions, we also observe that the model 2 building provides more savings than the individual measures as it allows the achievement of a better balance between the solar heat gain and the building losses over the heating and cooling seasons. For the Cedars, this large difference comes from the fact that the combination of measures corrects the negative effect of reflective windows which produce a negative impact if applied alone. Figure 38 - Combined Measure Results Residential Buildings Combined Measures Residential Buildings 1,400
1,200
Energy Usage (GJ)
1,000
Beirut Bayssour Kartaba Zahle Cedars
800
600
400
200
0 Base
Mod 1 Building model
Energy Analysis and Economic Feasibility Study
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Mod 2
3
ECONOMIC ANALYSIS
The Net Present Value of the savings associated with the conservation measures was evaluated (over 20 years) for each of the building types in each climatic zone, changing one parameter at a time. Then a combined simulation was made to see the interaction between the most promising measures. The calculations presented in this section are based on scenario number 4 (preferred scenario). The Results of the economic analysis and the resulting Net Present Value will be presented per climatic zone. The results were examined to determine the optimum level of roof and wall insulation in each climatic zone, and to note the difference in optimum U values according to building types.
3.1
Economic Results of Roof U-value
For the office buildings, the optimum insulation level and the corresponding NPV are presented in Tables 22 and 23. The first table presents the results as predicted by DOE2 but not taking into consideration the user’s behavior (i.e increasing natural ventilation to evacuate excess heat when necessary). The second table is corrected to consider that no additional cooling load will be created due to the roof insulation. The occupants are expected to open the windows to evacuate any extra heat. For the office building, the result is the same for all regions except the High Mountain. Even for the High Mountain region, the difference is small as the effect of insulation on cooling is close to neutral. Table 22 Optimum Roof U-value (with additional cooling load) – Office Buildings Climatic Zone 1- Coastal 2- Western Mid-mountain 3- Inland Plateau 4- High Mountain
Reference City Beirut Qartaba Zahle Cedars
Roof U Value (W/m2.K) 0.524 0.713 0.524 0.415
Typical EP Thickness (cm) 6 4 6 8
Direct Payback (Year) 4.8 3.3 3.2 1.9
NPV (USD)
(USD/m2)
1,232 1,764 2,624 6,250
0.64 0.92 1.37 3.26
Table 23 Optimum Roof U-value (without additional cooling load) – Office Buildings Climatic Zone 1- Coastal 2- Western Mid-mountain 3- Inland Plateau 4- High Mountain
Reference City Beirut Qartaba Zahle Cedars
Roof U Value (W/m2.K) 0.524 0.713 0.524 0.415
Typical EP Thickness (cm) 6 4 6 8
Direct Payback (Year) 4.8 3.3 3.2 1.8
NPV (USD)
(USD/m2)
1,232 1,764 2,624 6,493
0.64 0.92 1.37 3.38
We can observe that the negative air conditioning effect that can result from a better insulation of envelope has no effect on the recommended level of insulation. The NPV increases slightly in the high mountain region when we do not take the air conditioning effect into consideration.
Energy Analysis and Economic Feasibility Study
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For the residential buildings, the results are summarized in Tables 24 and 25. In this case also, there are some differences between the results for the High Mountain zone but it is also small. A sensitivity analysis was made to vary the efficiency of the cooling system between a 0.32 kW input per kW cooling and an efficiency of 0.37 kW input per kW cooling. The first one would be typical of a good efficiency unit. The last one would be more representative of a low efficiency product. The sensitivity analysis shows that the thickness of insulation does not change with the efficiency (the variation in savings is not enough to make the insulation recommendation move by the minimum increment of 2 cm considered in the analysis). The payback is however shorter and the NPV higher. The results presented herewith are for an efficiency of 0.37 kW input to kW cooling effect. Table 24 Optimum Roof U-value (with additional cooling) – Residential Buildings Climatic Zone 1- Coastal 2- Western Mid-mountain 3- Inland Plateau 4- High Mountain
Reference City Beirut Qartaba Zahle Cedars
Roof U Value (W/m2.K) 0.524 0.524 0.524 0.415
Typical EP Thickness (cm) 6 6 6 8
Direct Payback (Year) 1.3 1.6 1.1 0.9
NPV (USD)
(USD/m2)
3,487 3,470 4,702 7,120
2.50 2.49 3.37 5.11
Table 25 Optimum Roof U-value (without additional cooling) – Residential Buildings Climatic Zone 1- Coastal 2- Western Mid-mountain 3- Inland Plateau 4- High Mountain
3.2
Reference City Beirut Qartaba Zahle Cedars
Roof U Value 2 (W/m .K) 0.524 0.524 0.524 0.415
Typical EP Thickness (cm) 6 6 6 8
Direct Payback (Year) 1.3 1.6 1.1 0.9
NPV (USD)
(USD/m2)
3,487 3,470 4,702 7,260
2.50 2.49 3.37 5.21
Economic Results of Wall U-value
For the office buildings, the optimum insulation levels and the corresponding NPV are presented in Tables 26 and 27. The first Table presents the results as predicted by DOE2 but not taking into consideration the users’ behavior (i.e increasing natural ventilation to evacuate excess heat when necessary). The second table is corrected to consider that no additional cooling load will be created due to the wall insulation since the occupants are assumed to open the windows to evacuate any extra heat. These simulation results are for the wall insulation alone and show that there is no economic return to insulate walls in the coastal climate of Lebanon. Some insulation can be justified economically for the other regions. The set of results of presented in Table 27 are considered more representative of the reality and thus will be retained for setting the thermal standard requirements. This result also considers an efficiency of 0.37 kW input to kW cooling. Table 26 Optimum U-value for Walls (with additional cooling) – Office Buildings Climatic Zone 1- Coastal 2- Western Mid-mountain 3- Inland Plateau 4- High Mountain
Reference City Beirut Qartaba Zahle Cedars
Wall U Value (W/m2.K) NA 1.011 0.669 0.500
Typical EP Thickness (cm)
Direct Payback (Year)
(USD)
(USD/m2)
2 4 6
7.3 4.7 2.2
637 4,503 16,444
0.33 2.35 8.56
NPV
Table 27 Optimum U-value for Walls (without additional cooling) – Office Buildings Climatic Zone 1- Coastal 2- Western Mid-mountain 3- Inland Plateau 4- High Mountain
Reference City Beirut Qartaba Zahle Cedars
Wall U Value (W/m2.K) NA 0.669 0.669 0.399
Energy Analysis and Economic Feasibility Study
Typical EP Thickness (cm)
Direct Payback (Year)
(USD)
(USD/m2)
4 4 8
6.7 4.0 2.2
1,464 6,206 18,783
0.76 3.23 9.78
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NPV
Tables 28 and 29 present the results for residential buildings. The results show a higher requirement for the thermal insulation compared to the office buildings because of the longer operating hours. One difference is that there is a requirement for Beirut in both tables. The other regions have the same requirement for the scenario with and without added AC. The results of Table 29 will be used as a guideline to set the requirements of the thermal standard. Table 28 Optimum U-value for Walls (with additional cooling) – Residential Buildings Climatic Zone 1- Coastal 2- Western Mid-mountain 3- Inland Plateau 4- High Mountain
Reference City Beirut Qartaba Zahle Cedars
Wall U Value (W/m2.K) 0.669 0.500 0.500 0.399
Typical EP Thickness (cm) 4 6 6 8
Direct Payback (Year) 2.8 2.0 1.0 0.8
NPV (USD)
(USD/m2)
4,983 10,919 18,877 30,442
3.57 7.83 13.54 21.84
Table 29 Optimum U-value for Walls (without additional cooling) – Residential Buildings Climatic Zone 1- Coastal 2- Western Mid-mountain 3- Inland Plateau 4- High Mountain
3.3
Reference City Beirut Qartaba Zahle Cedars
Wall U Value (W/m2.K) 0.669 0.669 0.500 0.399
Typical EP Thickness (cm) 4 6 6 8
Direct Payback (Year) 2.8 2.0 1.0 0.7
NPV (USD)
(USD/m2)
4,983 10,919 18,877 32,975
3.57 7.83 13.54 23.65
Economic Results of Window U-value and Shading Coefficient
For the office buildings, the optimum U-values for glazing and the corresponding NPV are presented in Table 30. In this case, all windows with a positive NPV are presented instead of just the ones providing maximum NPV. With respect to windows, the decision of what to take into consideration is more complex than for roof and wall U-value. Windows are considered under two separate requirements: first, the requirement for the thermal transmittance (U-value) and second the requirement for the shading characteristic when calculating the effective fenestration ratio. If we want to first tackle U-value requirement, we can compare in the table 30 the three following options with clear glass where just the U value is a factor in the economic analysis. • • •
Window type 4 : Single glass with low-e coating (U = 4,27 W/m2.K) Window type 5: Double glass (U = 2,739 W/m2.K) Window type 8: Double clear glass with low-e coating (U = 1,956 W/m2.K)
For the Coastal region, none of these three types of windows are cost effective options. So there should be no U-value requirement for windows in this climate. For Qartaba and Zahle, just the Single glass low-e is an option which suggests that the requirement should be put near the 4.27 W/m2.K value. For the high mountain region, the double window is a cost effective option and even if its NPV is less than the single glass with low-e coating, it is still acceptable as the positive NPV suggests. It would be appropriate to put a more stringent requirement on Cedars than on Zahle and Qartaba so a thermal conductance of about 2.8 W/m2.K should be an appropriate requirement. Referring to the residential results presented in Table 31, we observe that more types of windows are providing an economic return in this scenario. As for the U-value requirement for the different regions, they will be similar to the office buildings with no requirement in the Beirut area; a U-value equivalent to a low-e single glazing in the western mid-mountainous and inland climatic zones, and a requirement equivalent to a clear double glazing in the high mountain zones. We can notice that several glazing also have a negative payback meaning that they have an immediate return on investment. In these cases, the incremental cost of the selected glazing is covered by the reduction in size and cost of the mechanical equipment used to provide heating and cooling.
Energy Analysis and Economic Feasibility Study
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Table 30 Optimum Performance of Windows (with additional cooling) – Office buildings Climatic Zone 1- Coastal
2- Western Midmountain
3- Inland Plateau
4- High Mountain
Window Composition
Tint
Reflective
Single (6 mm) Single (6 mm) Single (6 mm) Double (6/12/6 mm) Double (6/12/6 mm) Single (6mm) Single (6mm) Single (6mm) Single (6mm) Double (6/12/6 mm) Double (6/12/6 mm) Single (6 mm) Single (6 mm) Single (6 mm) Single (6 mm) Double (6/12/6 mm) Double (6/12/6 mm) Single (6 mm) Double (6/12/6 mm) Double (6/12/6 mm) Double (6/12/6 mm)
Bronze Clear Clear Bronze Clear Bronze Clear Clear Clear Bronze Clear Bronze Clear Clear Clear Bronze Clear Clear Clear Bronze Clear
--Tin-Oxide Titanium --Stainless H --Tin-Oxide Titanium ----Stainless H --Tin-Oxide Titanium ----Stainless H ---------
Characteristics Low-E Filling ----------------e = 0.2 ----------e = 0.2 ----e = 0.2 ----e = 0.2
------Air Air --------Air Air --------Air Air --Air Air Air
U value (W/m2.K) 6.163 6.124 5.500 2.739 2.495 6.163 6.124 5.500 4.270 2.739 2.495 6.163 6.124 5.500 4.270 2.739 2.495 4.270 2.739 2.739 1.956
SC (%) 0.71 0.58 0.45 0.57 0.26 0.71 0.58 0.45 0.84 0.57 0.26 0.71 0.58 0.45 0.84 0.57 0.26 0.84 0.81 0.57 0.78
Payback
NPV
(Years)
(USD)
0.71 1.32 1.62 6.95 4.80 1.09 2.46 2.61 6.63 8.01 6.92 -0.52 -0.06 0.45 2.30 4.17 3.70 2.21 6.51 7.42 7.19
9637 13903 17126 2594 12067 5820 6344 8793 896 506 3246 9207 12080 16560 6366 10074 15685 8408 3100 1479 2475
Table 31 Optimum Performance of Windows (without additional cooling) –Residential buildings Climatic Zone 1- Coastal
2- Western Midmountain
3- Inland Plateau
4- High Mountain
Window Composition
Tint
Reflective
Characteristics Low-E Filling
Single (6 mm) Single (6 mm) Single (6 mm) Single (6 mm) Double (6/12/6 mm) Double (6/12/6 mm) Single (6 mm) Single (6 mm) Single (6 mm) Single (6 mm) Double (6/12/6 mm) Double (6/12/6 mm) Single (6 mm) Single (6 mm) Single (6 mm) Single (6 mm) Double (6/12/6 mm) Double (6/12/6 mm) Double (6/12/6 mm) Single (6 mm) Single (6 mm) Double (6/12/6 mm) Double (6/12/6 mm) Double (6/12/6 mm) Double (6/12/6 mm)
Bronze Clear Clear Clear Bronze Clear Bronze Clear Clear Clear Bronze Clear Bronze Clear Clear Clear Bronze Clear Clear Clear Clear Clear Bronze Clear Clear
--Tin-Oxide Titanium ----Stainless H --Tin-Oxide Titanium ----Stainless H --Tin-Oxide Titanium ----Stainless H --Titanium ------Stainless H ---
------e = 0.2 ----------e = 0.2 ----------e = 0.2 ----e = 0.2 --e = 0.2 ------e = 0.2
Energy Analysis and Economic Feasibility Study
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--------Air Air --------Air Air --------Air Air Air ----Air Air Air Air
U value (W/m2.K) 6.163 6.124 5.500 4.270 2.739 2.495 6.163 6.124 5.500 4.270 2.739 2.495 6.163 6.124 5.500 4.270 2.739 2.495 1.956 5.500 4.270 2.739 2.739 2.495 1.956
SC (%) 0.71 0.58 0.45 0.84 0.57 0.26 0.71 0.58 0.45 0.84 0.57 0.26 0.71 0.58 0.45 0.84 0.57 0.26 0.78 0.45 0.84 0.81 0.57 0.26 0.78
Payback
NPV
(Years)
(USD)
0.0 0.4 0.6 5.0 4.2 2.9 -0.1 0.8 0.8 3.4 4.7 3.9 -2.1 -2.4 -1.6 0.8 2.2 1.2 8.3 4.7 1.0 4.0 3.9 5.0 0.0
10413 15417 19253 1350 7275 17561 6815 7924 11245 2261 5456 9668 8387 11405 15951 6450 12024 19034 70 965 9134 7034 7235 5443 7489
3.4
Economic Results of Architectural Shading (Fins and Overhangs)
For fins and overhangs, the economic return will vary according to the location and orientation of the window. The optimum economic result of the various considered projections were analyzed for different orientations. The Results of this economic analysis will not be used directly to put mandatory requirement for fins and overhangs in the thermal standard as it will be too limitative of the architectural expression to force a specific type and form of architectural shading. The architects have other options to control the solar heat gain. For instance a combination of window area, orientation and glazing shading coefficient can be added to the architectural shading to provide an optimum design. So the results of this section will be analyzed later in combination with the glass shading coefficient to determine a reasonable effective fenestration ratio (EFRreq).
Results for fins and overhangs – Office buildings Tables 32, 33, 34 and 35 present the results for each of the four climatic zones considered. The Tables provide information about the optimum economic level or the upper limit of the parametric analysis which is a projection factor of 1.50 for overhangs and 0.60 for fins. The NPV is also mentioned near the optimum projection level. We can observe from these figures that the fins and overhangs measures are very profitable in the coastal region. In fact, on several orientations, the most cost effective option was the upper limit of the range of parametric options. In the coastal zone, some shading on the north side is cost effective even if it just reduces the diffuse solar component and the direct component from north east in the early morning and north west in the late afternoon. In the western mid-mountain and inland zone, the projection for overhang is about half the one for the coastal zone and the NPV is two to three times lower. This is understandable as the solar heat gain is more beneficial in this region during the winter time. In the high mountain zone, where the solar heat gain is beneficial most of the year, only a minimum of overhang shading makes sense on the eastern, southern and western orientation. This minimum overhang will just slightly reduce the solar heat gain component when the sun is high over the horizon in summer but will let most of the direct solar heat gain component get into the building in winter period.
Table 32 Optimum Projection and Net Present Value – Office Building, Climatic Zone 1
Overhang Projection NPV (USD) Payback Fins Projection NPV (USD) Payback Combined Projection NPV (USD) Payback
Orientation SE S
N
NE
E
0.40 511 4.5
1.00 2311 3.3
1.50 6979 2.0
1.50 9491 1.6
0.20 642 4.0
0.40 1404 3.9
0.40 1476 3.8
0.40 0.20 891 4.8
0.80 0.40 3400 3.5
1.50 0.60 7845 2.9
Energy Analysis and Economic Feasibility Study
SW
W
NW
1.50 9006 1.6
1.50 9400 1.3
1.50 6507 1.7
0.80 2102 2.7
0.60 2472 3.6
0.60 2768 3.4
0.40 2259 2.8
0.20 1129 2.9
0.20 905 3.2
1.50 0.60 11221 2.2
1.50 0.60 10722 2.3
1.50 0.60 8496 2.0
1.50 0.60 7016 2.8
0.80 0.40 2751 3.7
:: 43 ::
Table 33 Optimum Projection and Net Present Value – Office Building, Climatic Zone 2
Overhang Projection NPV (USD) Payback Fins Projection NPV (USD) Payback Combined Projection
Orientation SE S
N
NE
E
---
0.40 1067 2.9
1.00 3646 2.4
0.80 4672 1.7
0.10 23 7.7
0.20 398 5.0
0.20 199 6.3
0.40 0.20 1282 3.9
0.80 0.40 2853 3.8
---
NPV (USD) Payback
SW
W
NW
0.60 3722 1.6
0.80 4047 1.3
0.80 3119 1.6
0.40 991 2.2
0.20 993 3.1
0.20 944 3.3
0.20 814 3.3
0.20 185 6.3
0.10 109 6.0
0.80 0.40 4790 2.8
0.40 0.20 3847 2.0
0.40 0.20 721 1.5
0.40 0.20 2278 2.2
0.40 0.20 1003 4.0
SW
W
NW
Table 34 Optimum Projection and Net Present Value – Office Building, Climatic Zone 3
Overhang Projection NPV (USD) Payback Fins Projection NPV (USD) Payback Combined Projection NPV (USD) Payback
Orientation SE S
N
NE
E
1.00 1991 4.0
1.00 8377 2.8
1.50 15450 2.4
1.00 12557 2.1
0.80 5462 2.0
1.00 12500 2.1
1.50 15200 2.4
1.00 8300 2.8
0.20 855 4.4
0.60 4151 3.2
0.60 4961 2.6
0.60 5341 2.2
0.20 1401 2.0
---
---
---
0.40 0.20 2388 3.8
0.80 0.40 9730 3.0
1.50 0.60 16181 2.5
0.80 0.40 14030 2.3
0.80 0.40 5870 2.1
0.80 0.40 14000 2.3
1.50 0.60 16000 2.3
1.50 0.60 16000 2.6
SW
W
NW
Table 35 Optimum Projection and Net Present Value – Office Building, Climatic Zone 4
Overhang Projection NPV (USD) Payback Fins Projection NPV (USD) Payback Combined Projection NPV (USD) Payback
Orientation SE S
N
NE
E
---
---
0.40 369 4.9
0.20 334 3.5
0.10 275 1.6
0.20 421 1.0
0.20 191 2.4
---
---
---
---
---
---
---
---
---
---
---
---
---
---
---
---
---
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Results for fins and overhangs – Residential buildings Tables 36, 37, 38 and 39 present the results for each of the four climatic zones considered. We can observe a similarity with the office results for the coastal zone. The highest limit in the parametric analysis was often the most economic. It reflects the context of a high solar load in a region where the cooling season is long and heating season short. The maximum value for fins in residential buildings has been put to 60% as it more closely reflects the form factor of windows for this category of buildings. In the coastal zone, some shading on the north side is cost effective even if it just reduces the diffuse solar component and the direct component from north east in the early morning and north west in the late afternoon (the NPV for fins is higher than for overhang). In the western mid-mountain and inland zone, the optimum projection for overhang is about two third the one for the coastal zone so it is slightly more cost effective than for office buildings. In the high mountain zone where the solar heat gain is beneficial most of the year, only a minimum of overhang shading makes sense on the eastern, southern and western orientation. This minimum overhang will just slightly reduce the solar heat gain component when the sun is high over the horizon in summer but will let most of the direct solar heat gain component get into the building in the winter period. In the case of the high-mountain zone, we observe also that the shading optimal levels are a bit higher than for office buildings. Several NPV are just near the border (0-value) so the table is not symmetrical due to the fact that the weather file represents real weather and the peak days on each orientation are not necessarily the same when the building is rotated.
Table 36 Optimum Projection and Net Present Value – Residential Building, Climatic Zone 1
Overhang Projection NPV (USD) Payback Fins Projection NPV (USD) Payback Combined Projection NPV (USD) Payback
Orientation SE S
N
NE
E
SW
W
NW
0.60 671 4.8
1.00 2838 2.9
1.50 8969 1.6
1.50 11687 1.3
1.50 11524 1.2
1.50 13064 0.3
1.50 11576 immediate
1.00 4005 0.7
0.40 1481 3.7
0.60 3759 2.7
0.60 3991 2.6
0.60 6125 1.8
0.60 6964 1.5
0.60 6874 0.9
0.60 4322 2.2
0.60 4625 1.4
0.40 0.40 2070 3.9
0.60 0.60 6283 2.5
0.60 0.60 10341 1.6
0.60 0.60 14988 1.1
0.60 0.60 15874 1.0
0.60 0.60 13425 0.1
0.60 0.60 12208 0.5
0.60 0.60 8136 0.7
Table 37 Optimum Projection and Net Present Value – Residential Building, Climatic Zone 2
Overhang Projection NPV (USD) Payback Fins Projection NPV (USD) Payback Combined Projection NPV (USD) Payback
Orientation SE S
N
NE
E
0.40 435 4.2
0.60 1996 2.3
1.50 5981 2.1
1.00 5783 1.6
0.60 4669 1.1
0.20 1124 1.8
0.60 2118 3.7
0.40 977 4.5
0.60 2889 3.1
0.20 0.20 1413 2.2
0.60 0.60 3891 3.3
0.60 0.60 5248 2.6
0.60 0.60 7528 2.0
Energy Analysis and Economic Feasibility Study
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SW
W
NW
0.80 7092
1.50 8341
0.80 3286
immediate
immediate
immediate
0.40 2055 2.9
0.60 3399 2.0
0.60 1401 4.3
0.60 3029 1.8
0.40 0.40 5468 1.9
0.60 0.60 4241 0.3
0.60 0.60 7033 0.8
0.60 0.60 5820 0.9
Table 38 Optimum Projection and Net Present Value – Residential Building, Climatic Zone 3
Overhang Projection NPV (USD) Payback Fins Projection NPV (USD) Payback Combined Projection NPV (USD) Payback
Orientation SE S
N
NE
E
SW
W
NW
0.60 537 3.8
1.00 2270 2.3
1.50 7175 1.2
1.50 9350 1.0
1.50 9220 1.0
1.50 10451 0.3
1.50 9261 0.3
1.00 3204 0.5
0.40 1185 2.9
0.60 3007 2.1
0.60 3192 2.1
0.60 4900 1.5
0.60 5571 1.2
0.60 5499 0.7
0.60 3457 1.8
0.60 3700 1.1
0.40 0.40 1656 3.1
0.60 0.60 5027 2.0
0.60 0.60 8273 1.3
0.60 0.60 11990 0.9
0.60 0.60 12699 0.8
0.60 0.60 10740 0.0
0.60 0.60 9767 0.4
0.60 0.60 6508 0.6
Table 39 Optimum Projection and Net Present Value – Residential Building, Climatic Zone 4
Overhang Projection NPV (USD) Payback Fins Projection NPV (USD) Payback Combined Projection
NE
E
---
0.40 493 4.2
1.00 1798 0.0
0.40 968 2.3
0.20 581 1.2
0.20 45 7.7
---
0.10 6 8.2
---
0.20 0.20 308 5.9
0.20 0.20 30 8.0
0.20 0.20 401 5.1
0.20 0.10 98 6.1
0.10 4 8.2
---
NPV (USD) Payback
3.5
Orientation SE S
N
SW
W
NW
0.60 1953
1.00 3990 0.0
0.60 1407
immediate
0.40 387 0.0
---
immediate
---
0.40 433 0.0
0.40 0.40 1010 0.0
0.40 0.40 1508 0.0
Economic Results of Selected Combined Measures
The economic analysis of the combined energy conservation measures was realized to see if the combination of some measures could lead to better savings than individual measures.
Office building results Table 40 presents the result for the combined analysis for the office building. The combination of selected measures always provides a positive NPV. This means that as a group, the measures provide a good return on investment but it does not mean that all measures constituting the group have an equal saving. If we examine more closely the result for the Coastal zone, building model 2, we find a NPV of US$9,026 that seems interesting at first glance. However, if we compare with the results of individual measures we can see that the windows and overhangs are the ones that are highly positive with US$11,953 of savings, while the wall insulation is negative. The roof insulation also allows some savings while the wall insulation does not. So the combination of measures in a coastal climate does not seem to bring much advantage, especially the combination of wall and roof insulation.
Energy Analysis and Economic Feasibility Study
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If we make an analysis for the high mountain zone, the results are quite different than the coastal zone. First, we notice that the NPV of the combined measures is US$17,364. This is higher than the sum of individual measures which is US$9,026. Furthermore, if we analyze just the difference for model 1 and 2 that correspond to the wall insulation only, we now find that it has a much higher NPV than when calculated individually. The NPV of the wall measures implemented after the roof and windows optimization allows an additional US$17,024. In the inland region, the total of all individual measures was US$11,300. The total for the combined measures is US$14,392 for model 1 so the combination better performs than the individual measures. When we analyze in detail the difference between model 1 and 2 building, it is possible to isolate the NPV for the wall measure only. In this case, the model 2 NPV is larger than the model 1 NPV so the wall measure has a positive NPV of US$ 8,078.
Table 40 Combination Measures Economic Analysis – Office Buildings NPV (USD)
1 - Coastal
2 - Western Mid Mountain
3 - Inland Plateau
4 - High Mountain
19,942
9,069
14,392
340
9,026
11,004
22,470
17,364
Base Case Wall: Single, non insulated, U=3.432 W/m2 C Roof: Non-insulated, U=2.556 W/m2C Window: Single 6 mm clear, U=6.160 W/m2C Overhang: No overhang
Model 1 Wall: Single, non insulated, U=3.432 W/m2C Roof: Insulated 6 cm. EPS, U=0.524 W/m2C Window: Single reflective, SC=0.45, U=5.490 W/m2C Overhang: PF = 0.40 for E,W,S side
Model 2 Wall: Double, insulated 6 cm EPS, U=0.500 W/m2C Roof: Insulated, 6 cm EPS, U=0.524 W/m2C Window: Single reflective, SC=0.45%, U=5.490 W/m2C Overhang: PF = 0.40 for E,W,S side
Residential building results For the residential buildings all combined results provide a positive NPV but the detailed analysis led to the conclusion that the difference in result is small and does not affect substantially the recommendation. The analysis for the coastal zone and the western mid-mountain showed a negative result for the wall alone (difference between the NPV of model 1 and 2). For the inland plateau and high mountain, the two models perform well and the wall insulation is economically attractive. Table 41 presents the result for the combined analysis for residential buildings. Table 41 Combined Measures Economic Analysis - Residential Buildings NPV (USD)
1 - Coastal
2 - Western Mid Mountain
3 - Inland Plateau
4 - High Mountain
26,124
13,871
16,098
3,482
25,695
11,007
31,497
16,855
Base Case Wall: Single, non insulated, U=3.432 W/m2 C Roof: Non-insulated, U=2.556 W/m2C Window: Single 6 mm clear, U=6.160 W/m2C Overhang: No overhang
Model 1 Wall: Single, non insulated, U=3.432 W/m2C Roof: Insulated 6 cm. EPS, U=0.524 W/m2C Window: Single reflective, SC=0.45, U=5.490 W/m2C Overhang: PF = 0.40 for E,W,S side
Model 2 Wall: Double, insulated 6 cm EPS, U=0.500 W/m2C Roof: Insulated, 6 cm EPS, U=0.524 W/m2C Window: Single reflective, SC=0.45%, U=5.490 W/m2C Overhang: PF = 0.40 for E,W,S side
Energy Analysis and Economic Feasibility Study
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3.6
Sensitivity Analysis
This section presents a sensitivity analysis from building to building and for different economic scenarios. Ranges of optimum insulation levels and glazing characteristics for office buildings in each climatic zone are presented in Table 42. The ranges shown are the ones obtained from the nine scenarios used in the sensitivity analysis and demonstrate how the economic parameters used affect the recommendation. It is important to note that the maximum value of the NPV varies from scenario to scenario, as one would expect: highest NPV for high fuel price and low discount factor, and lowest NPV for low fuel price and high discount factor. The results shown are for the economic analysis without the impact of insulation on additional air conditioning. For the roof insulation, we can see that the sensitivity analysis produces small changes in the level of insulation recommended (equivalent to 2 cm to 8cm thickness of expanded polystyrene). For the wall, all scenarios in the Coastal region prove that insulation is not economical for these regions. In zones 2 and 3, the sensitivity of the recommendation to the economic parameter variation is high with a range from no insulation at all to 6 cm equivalent of polystyrene (0.500 U value). For the glazing, we can see that the economic analysis is not quite sensitive to a variation in the economic parameters. For zones 1 and 2, the economic analysis result for the optimum glazing indicate the same type of window, the single clear window with reflective coating to reduce solar heat gain. In Cedars also, one type of glazing is the optimum selection whatever the scenario used. In this case, it corresponds to the single glazing with low-e coating (good for large heating load). In the Inland zone, the optimum selection varies from 0.45 to 0.26 in shading coefficient and includes some single and double glazing window which explains the variation in thermal transmittance values obtained. Table 42 Range in Optimum Shell Properties to Maximize NPV (for all 9 scenarios) – Office Climate Zone
Roof U value W/m2.K
Wall U value W/m2.K
1 – Coastal
0.713 (2 cm. EPS) to 0.524 (6 cm. EPS)
---
2- Western midmountain 3- Inland Plateau
0.713 (2 cm EPS) to 0.524 (6 cm EPS)
No insulation to 0.669 (4 cm EPS).
0.413 (4 cm EPS) to 0.415 (8 cm EPS)
0.669 (4 cm. EPS) to 0.500 (6 cm EPS).
4- High Mountain
0.524 (6 cm EPS) to 0.415 (8 cm EPS)
0.524 (6 cm EPS) to 0.415 (8 cm EPS)
Glass U value W/m2.K 5.5 for all scenarios
Glass Shading Coefficient 0.45 for all scenarios
5.5 for all scenarios 5.5 To 2.5 4.3 For All
0.45 for all scenarios 0.45 To 0.26 0.84 For All
Table 43 presents the results of the sensitivity analysis for the residential building. The roof results show a variation in the same range as for the office building. For the wall, the range is quite variable with a 2 cm to 4 cm of equivalent polystyrene insulation in Beirut and a gradual increase in other zones. From the glazing in zones 1 and 2, only two types of glazing are providing the optimum economic return. One of the glazing types is a single glass with reflective coating and the second is a double glazing with also a reflective coating. In this case, it is more likely that it was not the thermal transmittance characteristic of the double glazing that makes it a good choice but rather the very low shading coefficient associated with this glazing. Table 43 Range in Optimum Shell Properties to Maximize NPV (for all 9 scenarios) – Residential Climate Zone 1 – Coastal 2- Western midmountain 3- Inland Plateau 4- High Mountain
Roof U value W/m2.K
Wall U value W/m2.K
0.524 (6 cm EPS) All scenarios
0.669 (4 cm EPS) all scenarios
0.524 (6 cm EPS) to 0.415 (8 cm EPS)
0.669 (4 cm EPS) to 0.500 (6 cm EPS).
0.524 (6 cm EPS) to 0.415 (8 cm EPS) 0.415 (8 cm EPS) All scenarios
0.500 (6 cm EPS) To 0.399 (8 cm EPS). 0.399 (8 cm EPS) All scenarios
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Glass U value W/m2.K 5.5 To 2.5
Glass Shading Coefficient 0.45 To 0.26
5.5 To 2.5 2.5 for all scenarios 4.27 To 2.0
0.45 To 0.26 0.26 for all scenarios 0.84 To 0.57
4
RECOMMENDATIONS FOR THE THERMAL STANDARD
Results of the economic analysis based on an average scenario (Scenario #4) indicate that certain additions in roof and wall insulating values and reduction of the coefficient of heat transfer for windows would result in economic benefits to the building owner over the 20 year evaluation period. The economic analysis also revealed that the selection of windows with a low shading coefficient and the installation of architectural shading devices (fins and overhang) can be beneficial in the warmer part of Lebanon. The economic analysis will allow the determination of the appropriate effective fenestration ratio requirement (EFRreq) which includes various fenestration characteristics like orientation, glazing shading coefficient and architectural shading factor. This section summarizes the results presented in the economic analysis section and discusses further the establishment of the effective fenestration ratio (ERFreq) based on the economic analysis results.
4.1
Maximum U-value for Roofs and Walls
The analysis of cases studied for Scenario 4 suggest that optimum insulation levels for roofs and walls should be as presented in Section 3.1 for the roof. For the wall, the requirement should be set at the level presented in Section 3.2 except in Beirut because the cumulative effect of wall insulation will be small if the roof is already insulated in this zone with lower heating requirement. For that region, it will be required that a double cavity wall without insulation be the minimum requirement for wall construction. In the case of office buildings, this recommended level takes also into consideration that we should avoid large difference in requirements between zones. Additional consideration in the selection of the regulatory level is to provide some margin for change in construction methods. With the addition of this margin, the promoters will not be in obligation to increase the insulation j level just because they have deviated from the base case construction scenarios used to establish the level in this analysis. The level recommended in the table below takes this margin into account. Finally, the required wall level in zones 3 and 4 has been adjusted to a lower requirement than what was found in section 3.2 due to the fact that the walls have a lower economic return than roofs and as such would have a lower requirement. The increment of 2 cm used in the analysis was not fine enough to find the real optimum level of insulation (probably between 4 cm and 6 cm for zone 3 and between 6 cm and 8 cm for zone 4). In order to keep with the rationale of a minimum requirement for this voluntary standard, the lower boundary of insulation has been retained. A last deviation from the wall recommendations in section 3.2 is applied for the Western mid-mountain region where the recommendation leads to 6 cm of insulation, but by a too close margin with the 4 cm alternative. The difference in terms of NPV is just 63 USD over a 20 year period and the difference in payback is just 2.0 years instead of 2.03. Considering this small difference in result and the important incremental cost that is added by considering 6 cm insulation, then a conservative level at 4 cm equivalent of polystyrene insulation was retained. The resulting values are consolidated in Table 44, along with the equivalent thickness of expanded polystyrene (R=0.25). These are the recommended levels of roof and wall U-values for the Thermal Standard for Buildings for the various climatic zones of Lebanon. The recommendations are presented for the two categories of buildings. Category 1 is for residential buildings while category 2 is for the other types of buildings.
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Table 44 Optimum Roof and Wall Insulation levels by Climate Zone Climate zone
1 - Coastal
2 – Western midMountain 3 – Inland Plateau 4 – High Mountain
Building Type 1
U Value (W/m2.K) 0.57
Roof Equivalent Polystyrene (cm) 6
U Value (W/m2.K) 2.10
2
0.57
6
2.10
1 2 1 2 1 2
0.57 0.57 0.57 0.57 0.44 0.44
6 6 6 6 8 8
0.77 0.77 0.77 0.77 0.55 0.55
Wall Equivalent Polystyrene (cm) Double cavity wall No insulation Double cavity wall No insulation 4 4 4 4 6 6
Once insulation is added to a masonry building, one has to look at thermal bridging that occurs across the joints between walls and floors and projections such as balconies. No evaluation was made at this stage of the various options to insulate these additional areas of heat gains and losses, but future analysis should look at the issue of thermal bridges before the Thermal Standard becomes mandatory in the target year 2010.
4.2
Maximum U-value for Windows
Table 45 summarizes the recommendations from the economic analysis for the selection of the glazing Uvalue in Lebanon. This section just considers the heat transfer characteristic of the glass. The shading coefficient will not be directly subject to an absolute requirement but will rather be incorporated into the effective fenestration ratio requirement (EFRreq) were the orientation and architectural shading devices will also be taken into consideration. Table 45 Glazing thermal transmittance requirement Climate Zone
1 - Coastal 2 – Western mid-Mountainous 3 – Inland Plateau 4 – High Mountainous
4.3
Building Type 1 2 1 2 1 2 1 2
Window U Value (W/m2.K) 6.2 6.2 4.3 4.3 4.3 4.3 2.8 2.8
Typical Characteristics Single glass Single glass Single glass, low-e Single glass, low-e Single glass, low-e Single glass, low-e Double glazing, clear, low-e Double glazing, clear, low-e
Maximum Effective Fenestration Ratio
This section discusses the selection of the proposed effective fenestration ratios (EFRreq) in the thermal standard. This is an important requirement as it complements the U-value requirements. The objective of the EFRreq is to limit the solar load to a reasonable range for the two categories of buildings. Determining the reasonable EFRreq requires a simultaneous analysis of various parameters that the building designer may act upon in order to reduce the solar heat gain of the proposed building. These are: • • •
the orientation of the building, the glass shading coefficient, the architectural shading factor (fins and overhang)
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Each of these factors has already been analyzed in the previous sections for its technical and economical interests. The conclusion for the fenestration ratio, the glass shading coefficient and the architectural shading factors are summarized in the following:
Fenestration Ratio Windows facing west, south and east are surfaces that can allow solar heat gain in summer and contribute to increasing the air conditioning load. They can also allow solar heat in winter and help offset some of the space heating energy requirements. Because of the higher U-value of glass compared to walls, windows have a higher heat loss per unit area than walls, particularly in cold weather, during cloudy days and at night. Therefore, we can say that in general increasing the window to wall ratio will result in a higher energy requirement for both space heating and cooling. This was confirmed in all Visual-DOE3 runs where the effect of increasing the WWR from the base case also increased the energy usage. So, from a strict point of view of thermal energy, the percentage of fenestration should be kept to a reasonable level for higher efficiency. However, this cannot be a practical requirement in a thermal standard as it may impact building design and day-lighting. The approach that will be used instead is to put a limit on the fenestration ratio in the prescriptive path. This will result in the need to compensate a high fenestration ratio by other improvements in the building envelope to insure that the building will not use more energy than a building using the maximum allowed WWR. Since windows are desirable from the point of view of natural lighting, natural ventilation (for opening windows), occupant general visual comfort and aesthetics, it is necessary to allow reasonable WWRs, as long as this does not adversely affect the thermal energy requirements of the building. Options to offset heat losses/gains incurred by greater window areas can include specifying better U-values for walls and roofs, addition of external horizontal or vertical window shading devices and lower U-value window configurations (two panes or better frame).
Glazing Shading Coefficient The results of the NPV analysis indicate that in all climates, there are some types of tinted or reflective windows that are cost effective. This result may seem contradictory at first glance as the global energy requirement for a building actually increases in regions like mid-mountain and inland when reflective windows are installed. However, when the detailed economic analysis results in the cooling and heating season are performed using the relative price of energy for cooling and heating, reflective windows are cost effective in most climates because the cooling energy cost saved is higher than the heating energy cost. The only exception would be in the high mountain zone where only one tinted double glass window was providing a positive NPV but a closer examination of the NPV results reveal that a clear double window outperforms the tinted window in this region. So the positive NPV was solely due to the lower coefficient of heat transfer of the double glazing and not to its shading coefficient.
Architectural Shading Factor The architectural shading factor is an interesting measure that should be considered in all climates and can be combined with glass shading coefficient, orientation and fenestration ratio. In the coastal zone, the economic analysis revealed that the higher the architectural shading factor, the better, even to the point that the economical size of fins and overhang may not be consistent with building aesthetic or natural lighting objectives. Thus the architectural shading factor must not be selected in the thermal standard based on the economic return only. For the western mid-mountain and inland region, the architectural shading factor also makes sense but the economically optimal size of fins and overhang is more in a reasonable range (architecturally speaking) and could be applied to reduce the effective fenestration ratio of buildings in most designs.
Energy Analysis and Economic Feasibility Study
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For the high-mountain region, solar heat gain is beneficial most of the year so the only cost effective alternative here are moderate overhangs and fins that will just limit the solar heat gain during summer and will still let most of the solar heat gain in winter get in. Based on the above information, to the study proceeded to calculating the effective fenestration ratio of different building shapes with combinations of cost effective measures to establish a reasonable level requirement in the thermal standard should be. In the thermal standard, the following relationship will be used to define the effective fenestration ratio of the building by considering the individual impact of each window (vertical window or skylight) in the building. EFR = Σ (Awi x SCwi x ASFwi) / Σ Av + 2 Σ (Asi x SCsi) / Σ Ah EFR
Awi SCwi ASFwi Av Asi SCsi Ah
(3)
= The effective fenestration ratio of the building considering the combined impact of glass shading coefficient, orientation and architectural shading factor on the solar heat gain and on the resulting heating and cooling requirement for the building. = Area of a specific individual window (m2) = Shading coefficient of the individual window = Architectural shading factor of the individual window = Area of all vertical surfaces (opaque walls + windows) (m2) = Area of the individual skylight (m2) = Shading coefficient of the individual skylight = Area of all horizontal surfaces (roofs + skylights) (m2)
In the above equation, the area of individual windows Awi and skylights Asi can be determined from a simple examination of the building drawings. The shading coefficient SCwi and SCsi can be determined by the optical characteristics of the selected windows and skylights. These are typically provided by the manufacturer. The architectural shading factor ASFwi, is a function of the physical (architectural) protections used for the windows and the orientation of the window considered. Furthermore, in the above equation, a weighting factor of 2 is applied to all skylights as their impact on cooling load is much higher than vertical windows. The calculated EFR for the proposed building will then be compared to the effective fenestration ratio requirement (EFRreq) proposed in the thermal standard to determine if the proposed building complies with the thermal standard. In order to determine the appropriate EFRreq levels for Lebanon, different typical buildings and scenarios were considered. By direct examination of the results, a judgment could be applied to what level or EFR should be required for the purpose of the thermal standard. The typical buildings considered have two different shapes and orientations for a total of four building variations. Then, each of these four buildings were tested with five different scenarios of window to wall ratios and for different glazing shading coefficient and architectural shading factors for a total of 20 calculations for each climate zone.
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4.3.1
EFRreq Analysis for Office Buildings
Table 46 presents the EFRreq variations considered for the office building analysis . Table 46 Variations of Buildings for the EFRreq Analysis Alternative for Buildings Building form factor and orientation
Building window to wall ratio and shading
20 m x 40 m x 5 stories, main façade toward south 20 m x 40 m x 5 stories, main façade toward east 20 m x 60 m x 5 stories, main façade toward south 20 m x 60 m x 5 stories, main façade toward east 20% WWR and no shading 25% WWR and selected SC and overhang 25% WWR and selected SC and overhang 30% WWR and selected SC and overhang Maximum WWR and limits for SC and overhang
Table 47 presents the five scenarios for window shading. The first one that corresponds to 20% WWR is a building with single clear glass (SC = 0.95) and no overhang or fins. This scenario was set up just to see if a building with a minimum window area will automatically comply to the EFRreq even if the designer does nothing to limit the solar radiation. The three next scenarios for 25%, 30% and 35% WWR consider a selected SC and overhang value. These values were selected by inspection of the economic returns of glazing types and overhangs and fins. The retained value is generally lower than the alternative that provides the maximum economic return for the following reasons: - Shading coefficient: We can observe from the economic analysis that it is possible to economically justify windows with a very low shading coefficient. These windows are generally highly reflective and thus impose a quite important restriction on the architectural concept of the building (mirror like building façade) and on the daylight quality. Going for a thermal standard that is too strictly tied to the economic value of the glass type will have a negative impact on the freedom of architectural expression. It is also important to consider the fact that highly reflective windows do not provide a good natural lighting level inside buildings. This will have the negative effect of increasing the artificial lighting usage and consequently the energy requirement for it. - Overhangs: We can observe from the economic analysis that the measure for fins and overhangs is very economical, particularly in a coastal climate. The projection that would be recommended from a strict economical point of view will be 1.50 which means 1.5 times the window height. If such a high level is put as a requirement in the thermal standard, it will impose again a severe limitation to architectural expression. For aesthetic and practical reasons, it is recommended that the overhangs and fins used to recommend the EFR required be selected to a level that makes practical sense. According to these considerations, a “selected” level of shading coefficient and overhang projection was selected for the calculation of the EFR. This level of SC and projection for overhangs will be used to test the case study building with 25%, 30%, and 35% WWR. From the above scenarios, a decision was made about the level of EFR that should become the requirement in the thermal standard. Once the EFR was selected, a last case study of building scenario was calculated to determine if the proposed EFRreq could be achieved by a building with a very high percentage of fenestration and using all cost effective measures (i.e. using the minimum possible shading coefficient and the maximum overhang). This last simulation reveals the absolute practical limitation that can be put on a building with the proposed EFRreq. Table 47 presents the selected and limit values of SC and overhang projection factors used in the case study.
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Table 47 Selected and Limit Values of SC and projection factor for overhang Selected Overhang Projection factor
Climatic Zone SC
SC
Limit Overhang Projection factor
Zone 1 - Coastal Zone 2 - Western Mid-mountain Zone 3 - Inland Plateau
0.6 0.7 0.7
0.60 0.40 0.60
0.26 0.26 0.26
1.50 0.80 1.50
Zone 4 - High Mountain
0.95
0.20
0.57
0.20
The results of the simulations are summarized in Table 48. In this table, we can see one section for each climatic zone considered and one line in each table section for the four variations of building form and orientation. Then, each section presents the case studies for five different scenarios of WWR, SC and overhang according to Table 47. Table 48 Case Study Analysis to Determine the WWRreq – Office Buildings Climatic zone
Façade Width (m)
Façade Length (m)
20 20 20 20
40 40 60 60
Façade Width (m)
Façade Length (m)
20 20 20 20
40 40 60 60
Façade Width (m)
Façade Length (m)
20 20 20 20
40 40 60 60
Climatic zone
Façade Width (m)
Façade Length (m)
Zone 4 - High mountainous
20 20 20 20
40 40 60 60
Zone 1 - Coastal
Climatic zone
Zone 2 - Western mid-mountain
Climatic zone
Zone 3 – Inland plateau
Orientation Main façade S E S E Orientation Main façade S E S E Orientation Main façade S E S E Orientation Main façade S E S E
WWR 20% SC=.95 NO PF 13.2% 14.4% 17.2% 19.6% WWR 20% SC=.95 NO PF 13.2% 14.4% 17.2% 19.6% WWR 20% SC=.95 NO PF 13.2% 14.4% 17.2% 19.6% WWR 20% SC=.95 NO PF 13.2% 14.4% 17.2% 19.6%
WWR 25% SC=.6 0.6 PF Overhang 6.1% 6.7% 7.9% 9.2% WWR 25% SC = .7 0.4 PF Overhang 8.3% 9.1% 10.7% 12.4% WWR 25% SC = .7 0.6 PF Overhang 7.1% 7.8% 9.3% 10.7% WWR 25% SC = 0,95 0.2 PF Overhang 13.5% 14.8% 17.6% 20.2%
Case studies WWR 30% SC=.6 O.6 PF Overhang 7.3% 8.1% 9.5% 11.0% Case studies WWR 30% SC = .7 0.4 PF Overhang 9.9% 10.9% 12.9% 14.9% Case studies WWR 30% SC = .7 0.6 PF Overhang 8.5% 9.4% 11.1% 12.8% Case studies WWR 30% SC = 0,95 0.2 PF Overhang 16.2% 17.8% 21.1% 24.3%
WWR 35% SC=.6 0.6 PF Overhang 8.5% 9.4% 11.1% 12.9%
WWR = 98% SC = .26 1.5 PF Overhang 6.8% 7.4% 8.9% 10.0%
WWR 35% SC = .7 0.4 PF Overhang 11.6% 12.8% 15.0% 17.4%
WWR = 93% SC = 0.26 0.8 PF Overhang 8.6% 9.5% 11.2% 13.0%
WWR 35% SC = .7 0.6 PF Overhang 10.0% 11.0% 13.0% 15.0%
WWR = 100% SC = .26 1.5 PF Overhang 7.0% 7.5% 9.1% 10.2%
WWR 35% SC = 0,95 0.2 PF Overhang 18.9% 20.8% 24.6% 28.3%
WWR = 39% SC = 0,57 0.2 PF Overhang 13.0% 14.2% 16.9% 18.9%
By inspection, the value for the scenario at 30% WWR with the selected SC and overhang projection and for the 20 m x 60 m building oriented south was chosen as the one that should represent the required EFR in each zone. For instance, in Beirut, the EFRreq would be 9.5% that can be rounded to 10%. If we look at the other scenarios, we can see that, at this level of requirement, all buildings with a low WWR (20%) would not comply unless they install some sort of shading devices or by using glass with lower SC. The case study for the building that has 30% WWR (the maximum allowed WWR in the prescriptive approach) and the slightly disadvantaged shape (large façade toward south) would be complying if it uses the “selected” SC and overhang projection that represents a selection.
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The last column in table 48 presents what could be the maximum possible WWR of a building if the designer decides to apply measures that offer the best protection against sun. We can also see from the table than in almost all regions except Cedars, the designer would still have the possibility to go close to 100% fenestration if he selects the best glazing and longest overhang while installing cost effective measures. For Cedars, the practical limit would be a WWR of 39% because the minimum SC that make economic sense is 0.57 while in others regions it goes as low as 0.26. As a result of this analysis, the EFRreq levels presented in Table 49 will be recommended for office buildings. Table 49 Thermal Standard Requirement for WWRreq – Office Buildings Climatic Zone Zone 1 - Coastal Zone 2 - Western Mid-Mountain Zone 3 - Inland Plateau Zone 4 - High Mountain
4.3.2
EFRreq 10% 13% 11% 21%
EFRreq Analysis for Residential Buildings
For residential buildings, a process similar to the one described above for office buildings was applied. Similarly to office buildings, a “selected” and “limit” value of SC and overhang was selected considering the economic analysis of windows and the architectural shading device and practical constraint that the EFRreq could put in the construction market in Lebanon. Table 50 presents the selected and limit values of SC and overhang used in the case study. Table 50 Variations of Buildings for the EFRreq Analysis Alternatives for Buildings 20 m x 40 m x 5 stories, main façade toward south 20 m x 40 m x 5 stories, main façade toward east 20 m x 60 m x 5 stories, main façade toward south 20 m x 60 m x 5 stories, main façade toward east 15% WWR and no shading 18,5% WWR and selected SC and overhang 22% WWR and selected SC and overhang 26% WWR and selected SC and overhang
Building form factor and orientation
Building window to wall ratio and shading
Maximum WWR and limits for SC and overhang
Table 51 Selected and Limit Values of SC and projection factor for overhang Climatic Zone
SC
Limit Overhang Projection Factor
Zone 1 - Coastal Zone 2 - Western Mid-mountain Zone 3 - Inland Plateau
0.95 0.95 0.95
0.60 0.40 0.60
0.26 0.26 0.26
1.50 0.80 1.50
Zone 4 - High Mountain
0.95
0.20
0.57
0.20
SC
Selected Overhang Projection Factor
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Table 52 presents the result of the analysis for the four climatic zones and the various case studies. In the case of residential buildings, we notice that the requirement for windows will be lower due to the base hypothesis used for the typical window to wall ratio in current construction. This lower initial WWR means that in all regions, the requirement proposed will put a practical economic limit on the size of the window that can be designed in the building. This maximum economic ratio varies from 68% to 83% in the coastal, western mid-mountain and lower inland and inland plateau. For the high mountain region, the maximum economic EFR will be around 28% which is the most stringent requirement that will be put in the standard. It may be advisable to slightly increase the EFRreq for Cedars residential buildings to avoid requesting from building owners uneconomical choice of glazing and overhang for their buildings. A level of 18% instead of the 14% suggested by the analysis should be sufficient to allow some more flexibility to designers.
Table 52 Case Study Analysis to Determine the WWR req – Residential Buildings Climatic zone
Façade Width (m)
Façade Length (m)
20 20 20 20
40 40 60 60
Façade Width (m)
Façade Length (m)
20 20 20 20
40 40 60 60
Façade Width (m)
Façade Length (m)
20 20 20 20
40 40 60 60
Climatic zone
Façade Width (m)
Façade Length (m)
Zone 4 - High mountainous
20 20 20 20
40 40 60 60
Zone 1 - Coastal
Climatic zone
Zone 2 - Western mid-mountain
Climatic zone
Zone 3 – Inland plateau
Orientation Main façade S E S E Orientation Main façade S E S E Orientation Main façade S E S E Orientation Main façade S E S E
9.9% 10.8% 12.9% 14.7%
WWR 18,5% SC = .95 0.6 PF Overhang 7.1% 7.9% 9.3% 10.8%
WWR 15% SC=.95 No PF 9.9% 10.8% 12.9% 14.7%
WWR 18,5% SC = 0.95 0.4 PF Overhang 8.3% 9.2% 10.8% 12.5%
WWR 15% SC=.95 No PF 9.9% 10.8% 12.9% 14.7%
WWR 18,5% SC = 0.95 0.6 PF Overhang 7.2% 7.9% 9.3% 10.8%
WWR 15% SC=.95 No PF 9.9% 10.8% 12.9% 14.7%
WWR 18,5% SC = 0.95 0.2 PF Overhang 10.0% 11.0% 13.0% 15.0%
WWR 15% SC=.95 No PF
Case studies WWR 22% SC = .95 0.60 PF Overhang 8.5% 9.4% 11.0% 12.8% Case studies WWR 22% SC = 0.95 0.4 PF Overhang 9.9% 10.9% 12.8% 14.9% Case studies WWR 22% SC = 0.95 0.6 PF Overhang 8.5% 9.4% 11.1% 12.8% Case studies WWR 22% SC = 0.95 0.2 PF Overhang 11.9% 13.1% 15.5% 17.8%
The resulting recommended EFRreq levels for residential buildings are shown in Table 53. Table 53 Thermal Standard Requirement for EFR req – Residential Buildings Climatic Zone
EFRreq
Zone 1 - Coastal Zone 2 - Western Mid-mountain Zone 3 - Inland Plateau
11% 13% 11%
Zone 4 - High Mountain
16%
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WWR 26% SC =.95 0.6 PF Overhang 10.0% 11.1% 13.0% 15.1%
WWR = 68% SC = .95 1.50 PF Overhang 17.3% 18.7% 22.7% 25.4%
WWR 26% SC = 0.95 0.4 PF Overhang 11.7% 12.9% 15.1% 17.6%
WWR = 71% SC = 0.95 0.8 PF Overhang 24.0% 26.5% 31.2% 36.2%
WWR 26% SC = 0.95 0.6 PF Overhang 10.1% 11.1% 13.1% 15.1%
WWR = 83% SC = 0.95 1.5 PF Overhang 21.2% 22.8% 27.7% 30.9%
WWR 26% SC = 0.95 0.2 PF Overhang 14.1% 15.4% 18.3% 21.0%
WWR = 28% SC = 0.95 0.2 PF Overhang 15.1% 16.6% 19.7% 22.6%
5
IMPACT ASSESSMENT
The Thermal Standard for Buildings in Lebanon is anticipated to become mandatory in the target year 2010. This chapter presents a forecast of the impact of the application of the thermal standard on the macroeconomic level. This impact is based on an estimation of the area of residential buildings and office buildings which will be constructed on a 20 year horizon between the period 2010 and 2029.
5.1
Projected Economic Growth Rate
The assumptions about economic growth from 2005 to the end of the study period will be considered as follows: Table 54 Economic Growth Rate Economic growth rate Medium growth
5.2
Years 2005-2009 1.0%
Years 2010-2029 3.0%
Projected Population Growth Rate
Assumptions about the number of residential buildings meeting the thermal standards on a 20-year horizon will consider the following: • •
Demographic growth of the resident population. Family sizes assuming that each family will need one residential unit.
The projected population growth and the related family size are taken from the official projections used for the Schéma D’Aménagement du Territoire Libanais (SDATL), and are presented in Table 55 and Table 56.
Table 55 Projected Population Growth Year Mohafazat Beirut Mt-Liban North Beqaa South Nabatieh Total
2000 401,174 1,514,914 826,314 545,058 485,039 280,032 4,052,531
2010 424,121 1,668,223 984,156 623,372 579,820 326,344 4,606,036
2020 433,069 1,782,217 1,153,656 705,372 676,505 372,738 5,123,557
2030 428,891 1,851,129 1,327,677 781,265 769,717 414,719 5,573,398
Annual Growth Rate 20002030 0.21% 0.63% 1.49% 1.13% 1.45% 1.23% 0.96%
Source: Schema D’Amenagement du Territoire Libanais (SDATL), Rapport de la Phase 1, projections demographiques, Conseil du Développement et de la Reconstruction (CDR).
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Table 56 Projected Number of Households Mohafazat Family size Beirut Total number Family size Mt-Liban Total number Family size North Total number Family size Beqaa Total number Family size South Total number Family size Nabatieh Total number Family size Total Total number
2000 4.23 94,748 4.42 342,770 5.47 150,987 4.93 110,490 4.94 98,223 4.35 64,441 4.74 861,659
2010 4.02 105,460 4.16 400,832 5.38 182,832 4.72 132,169 4.74 122,392 4.17 78,281 4.56 1,021,966
2020 3.82 113,374 3.92 454,738 5.29 217,900 4.51 156,423 4.54 148,852 4.00 93,198 4.38 1,184,485
2030 3.63 118,212 3.69 501,568 5.21 254,957 4.31 181,210 4.36 176,538 3.84 108,088 4.20 1,340,573
Source: Schéma D’Aménagement du Territoire Libanais (SDATL), Rapport de la Phase 1, projections démographiques, Conseil du Développement et de la Reconstruction (CDR).
5.3 5.3.1
Projected Building Growth Projected Built-up Area of Residential Buildings
The projected number of residential units, on a 20-year horizon, was taken from the projections of the Schéma D’Aménagement du Territoire Libanais (SDATL), and is presented in Table 57. Table 57 Projected Number of Residences Mohafazat 2000 2010 2020 2030 Primaire 103,024 114,670 123,276 128,536 Secondaire 2,436 2,595 2,764 2,944 Beirut Vacant 13,771 11,818 10,141 8,702 Total 118,130 125,065 132,408 140,182 Primaire 345,537 404,067 458,408 505,617 Secondaire 28,336 30,725 33,316 36,126 Mt-Liban Vacant 102,827 90,695 79,995 70,557 Total 475,185 517,087 562,683 612,300 Primaire 147,856 179,041 213,382 249,670 Secondaire 10,511 12,142 14,026 16,202 Nord Vacant 28,457 26,677 25,008 23,443 Total 187,016 216,293 250,154 289,315 Primaire 94,359 112,873 133,586 154,754 Secondaire 8,218 9,310 10,548 11,950 Beqaa Vacant 21,845 20,149 18,585 17,142 Total 124,763 141,974 161,559 183,846 Primaire 89,249 111,209 135,252 160,408 Secondaire 7,284 9,420 12,183 15,757 Sud Vacant 20,762 19,931 19,133 18,367 Total 117,100 138,687 164,253 194,532 Primaire 53,511 65,004 77,391 89,756 Secondaire 8,253 8,447 8,647 8,851 Nabatieh Vacant 13,893 12,729 11,662 10,686 Total 75,787 85,618 96,725 109,272 Primaire 833,536 986,864 1,141,295 1,288,741 Secondaire 65,038 72,639 81,484 91,830 Total Vacant 201,555 181,999 164,524 148,897 Total 1,097,981 1,224,724 1,367,782 1,529,447 Source: Schéma D’Aménagement du Territoire Libanais (SDATL), Rapport de la Phase 1, projections démographiques, Conseil du Développement et de la Reconstruction (CDR).
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In addition to the building growth, another parameter that could be considered is the demolition and replacement of buildings that are over 75 years of age. This parameter contributes to an increase in the number of new buildings but will not be considered in this analysis. In order to translate the number of residential units constructed during the study period to square meters, an assumption was used for the surface area of the residential unit as an average of 140 m2 per residential unit. Table 58 presents the annual and total number of primary residential building units constructed during the study period and the resulting built-up area in m2. Table 58 Forecast of the Residential Building Area that will comply with the Thermal Standard Year 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029
Housing Number (units) 15,648 15,600 15,441 15,398 15,447 15,062 14,849 14,848 14,717 14,722 14,549 14,424 14,440 14,359 14,336 14,153 14,013 14,026 13,965 14,004
Housing Area (m2) 2,190,788 2,184,019 2,161,705 2,155,746 2,162,598 2,108,687 2,078,897 2,078,699 2,060,363 2,061,130 2,036,791 2,019,407 2,021,612 2,010,290 2,007,031 1,981,354 1,961,849 1,963,599 1,955,089 1,960,563
294,002
41,160,216
Total (2010 to 2029 - 20 years)
5.3.2
Projected Built up Area of Office Buildings
The projected growth of offices should normally follow the economic growth. However, since the 1996 survey of the Central Administration of Statistics (CAS) revealed a large number of empty offices, projections should therefore take into account the fact that there might be a gap between economic growth and the growth in the number of new offices. According to the 1996 survey of the CAS, the number of vacant office units reached an average of 31% on the national level. Considering the high level of vacancy, the following assumptions were made about the number and the occupancy of offices built according to the thermal standards: • •
The number of new office units up to 2010 will be considered negligible, and the 3% growth rate will be applied from 2010 onwards. It will be considered that all new offices constructed after 2010 will meet the thermal standard. The average surface area per office unit will be considered as 25 m2.
Table 59 presents the projection of the growth rate and occupancy of office space in the history period and for the forecast period. The resulting building area in m2 per climatic zone constructed each year is also identified in this Table. We can see from this Table that it is expected that the thermal standard will apply to a projected total constructed built-up area of 1,548,928 m2.
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Table 59 Forecast of the Office Building Area that will comply with the Thermal Standard Year
Economic growth
Working offices
(%) 4.0 2.2 1.2 0.4 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029
(units)
New offices rate (%)
New offices Annual (units)
Total number of offices (units)
(units)
188,162 195,688 199,993 202,393 203,203 203,203 203,203 203,203 205,235 207,287 209,360 211,454 213,569 219,976 226,575 233,372 240,373 247,584 255,012 262,662 270,542 278,658 287,018 295,629 304,498 313,633 323,042 332,733 342,715 352,996 363,586 374,494 385,729 397,301
0 2 1.1 0.6 0.2 0 0 0 0 0 0 0 0 0 0 0 0 0 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 3.0 3.0 3.0 3.0 3.0 3.0 3.0
0 3,763 2,153 1,200 405 0 0 0 0 0 0 0 0 0 0 0 0 0 3,714 3,825 3,940 4,058 4,180 4,305 4,434 4,567 4,704 4,846 4,846 4,846 4,846 4,846 4,846 4,846
301,853 305,616 307,769 308,969 309,374 309,374 309,374 309,374 309,374 309,374 309,374 309,374 309,374 309,374 309,374 309,374 309,374 309,374 313,087 316,913 320,852 324,911 329,090 333,396 337,830 342,398 347,102 351,948 351,948 351,948 351,948 351,948 351,948 351,948
113,691 109,928 107,775 106,575 106,170 106,170 106,170 106,170 104,138 102,086 100,013 97,919 95,804 89,397 82,798 76,001 69,000 61,789 58,075 54,250 50,310 46,252 42,072 37,767 33,333 28,766 24,062 24,062 24,062 24,062 24,062 24,062 24,062 24,062
Total (2010 to 2029 - 20 years)
5.4
Empty offices
79,170
2
Area (m ) of office the thermal Zone 1 Zone 2 50% 20% 0 0 47,038 18,815 26,913 10,765 15,000 6,000 5,063 2,025 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 46,425 18,570 47,813 19,125 49,250 19,700 50,725 20,290 52,250 20,900 53,813 21,525 55,425 22,170 57,088 22,835 58,800 23,520 60,575 24,230 60,575 24,230 60,575 24,230 60,575 24,230 60,575 24,230 60,575 24,230 60,575 24,230 895,614
complying standard Zone 3 20% 0 18,815 10,765 6,000 2,025 0 0 0 0 0 0 0 0 0 0 0 0 0 18,570 19,125 19,700 20,290 20,900 21,525 22,170 22,835 23,520 24,230 24,230 24,230 24,230 24,230 24,230 24,230
358,245 358,245 1,791,228 m2
with Zone 4 10% 0 9,408 5,383 3,000 1,013 0 0 0 0 0 0 0 0 0 0 0 0 0 9,285 9,563 9,850 10,145 10,450 10,763 11,085 11,418 11,760 12,115 12,115 12,115 12,115 12,115 12,115 12,115 179,124
Projected Reduction in Energy Consumption
The potential impact of the LTSB on energy consumption in Lebanon can be estimated once the following numbers are derived: Di
= Difference in annual specific energy consumption (GJ/m2 of floor area) that each category (i) of building upgraded to the corresponding recommended level in the LTSB in each climatic zone (k) will consume (compared to a base case building of the same configuration)
Aik1
= Floor area (m2) by building category (i) of new buildings constructed in climatic zone (k) in year 1 since the implementation of the TSBL.
P1
= Potential energy savings at the end of year 1 can then be calculated using the following formula:
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Equation 4 – Potential energy saving for the first year summed over all building classes and over all climatic regions built in the first year.
P1 =
∑{∑ D × A
ik1 }
i
k
(Equation 4)
i
P2
= Potential energy savings at end of year 2
Aik2
= Floor area of new buildings by building class (i) built during year 2
Equation 5: potential energy savings for the second year
P2 = P1 +
∑ {∑ D × A
ik 2 }
i
k
(Equation 5)
i
P2 is summed over all building classes and over all climatic regions built in the second year. And so on for future years
5.4.1
Impact of Office Buildings
The results of the Economic Analysis of various improvements in thermal transmission levels of walls, windows and roofs indicate that there would be substantial savings in requiring new buildings and building expansions to comply with the optimum levels of thermal insulation. The savings per m2 of floor area between base office buildings and similar buildings complying with the thermal standard were calculated from model studies. By subtracting the energy budget per unit of floor area between the base building and the compliant building, we obtain the net annual savings per unit of floor area. The results of these subtractions are presented on the next table. Table 60 Annual Base Case Office Building Energy per m2 and Savings for Heating and Cooling
1 - Coastal 2 - Western Mid-mountain 3 - Inland Plateau 4 - High Mountain
Base Case Building Energy Usage GJ GJ/m2 437 0.228
Percentage Energy Savings % 9.8%
Energy Savings with the Thermal Standard Total Heating Cooling GJ GJ/m2 GJ GJ/m2 GJ GJ/m2 43 0.022 16.7 0.0087 26.0 0.0135
540
0.281
22.4%
121
0.063
119.0
0.0620
2.5
0.0013
794
0.414
46.5%
369
0.172
368.9
0.1921
6.1
0.0032
1242
0.647
56.7%
704
0.367
686.0
0.3573
1.8
0.0009
We can observe in the previous results that the Coastal region is the one where the potential is the least important. This is understandable as this is the zone where there is a lower set of measures for the improvement of thermal transmittance that are cost effective. The main characteristic of buildings that can be improved in this case is the solar radiation reduction but there are practical limits that have to be considered in this case to avoid putting too much restriction on the architectural expression. The overall result for the Coastal zone is a lower potential. For Cedars, we have the coldest climate and the one where the largest improvement in thermal transmittance is possible. Thus a very high potential of improvement exists which translates into a 56.7% percentage of improvement. The two others regions fall in between. We can see that the Inland Plateau zone, which has larger temperature amplitudes, has a higher potential than the Western mid-mountain.
Energy Analysis and Economic Feasibility Study
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When the figures above are applied to the projected built-up area of which will be constructed in the future and which will comply with the thermal standard, we can find the following projection (Table 61) of energy savings caused by the thermal standard application. Table 61 Projected Cumulative office built-up area (2010-2029)
Yearly 2 (m ) 0 0 0 0 46,425 47,813 49,250 50,725 52,250 53,813 55,425 57,088 58,800 60,575 60,575 60,575 60,575 60,575 60,575 60,575
Zone 1 50% Cumulative 2 (m ) 0 0 0 0 46,425 94,238 143,488 194,213 246,463 300,276 355,701 412,789 471,589 532,164 592,739 653,314 713,889 774,464 835,039 895,614
895,614
7,262,405
Year
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2
Total (m )
Area of offices complying with the thermal standard Zone 2 Zone 3 20% 20% Yearly Cumulative Yearly Cumulative 2 2 2 2 (m ) (m ) (m ) (m ) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 18,570 18,570 18,570 18,570 19,125 37,695 19,125 37,695 19,700 57,395 19,700 57,395 20,290 77,685 20,290 77,685 20,900 98,585 20,900 98,585 21,525 120,110 21,525 120,110 22,170 142,280 22,170 142,280 22,835 165,115 22,835 165,115 23,520 188,635 23,520 188,635 24,230 212,865 24,230 212,865 24,230 237,095 24,230 237,095 24,230 261,325 24,230 261,325 24,230 285,555 24,230 285,555 24,230 309,785 24,230 309,785 24,230 334,015 24,230 334,015 24,230 358,245 24,230 358,245
Yearly 2 (m ) 0 0 0 0 9,285 9,563 9,850 10,145 10,450 10,763 11,085 11,418 11,760 12,115 12,115 12,115 12,115 12,115 12,115 12,115
Zone 4 10% Cumulative 2 (m ) 0 0 0 0 9,285 18,848 28,698 38,843 49,293 60,056 71,141 82,559 94,319 106,434 118,549 130,664 142,779 154,894 167,009 179,124
358,245
179,124
1,452,495
2,904,955
358,245
2,904,955
Table 62 Projected Energy Savings from Office Buildings (2010-2029) Climatic Zone 1 - Coastal
Distribution of projected built up area (%)
Cumulative m2 per zone 2010-2029
50%
7,262,405
2 - Western 20% 2,904,955 Mid-mountain 3 - Inland 20% 2,904,955 Plateau 4 - High 10% 1,452,495 Mountain Projected energy savings by type (GJ) Total projected energy savings (GJ)
Energy Analysis and Economic Feasibility Study
Heating Savings
Cooling Savings
GJ/m2
GJ
GJ/m2
GJ
Projected Energy Savings per zone (GJ)
0.0087
63,183
0.0135
98,042
161,225
0.0620
180,107
0.0013
3,776
183,883
0.1921
558,041
0.0032
9,295
567,336
0.3573
518,976
0.0009
1,307
520,283
1,320,307
112,420 1,432,727
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5.4.2
Impact for Residential Buildings
Table 63 Annual Base Case Residential Building Energy per m2 and Savings for Heating and Cooling Climatic Zone
Base Case Bldg Energy Use GJ GJ/m2
Energy Savings %
Energy Savings with the Thermal Standard Total Heating Cooling GJ GJ/m2 GJ GJ/m2 GJ GJ/m2
1 - Coastal 2 - Western Mid-mountain 3 - Inland Plateau 4 - High Mountain
341
0.245
12.8
44
0.031
8.8
0.0063
35.0
0.0251
501
0.359
42.8
215
0.154
202.0
0.1449
12.5
0.0089
683
0.490
44.7
305
0.219
276.9
0.1986
28.3
0.0203
1283
0.921
58.0
745
0.534
743.2
0.5331
1.7
0.0012
Table 64 Projected cumulative residential built up area (2010-2029) Year
Area of residential buildings complying with the thermal standard Yearly (m2) Cumulative (m2) 2,190,788 2,190,788 2,184,019 4,374,806 2,161,705 6,536,511 2,155,746 8,692,257 2,162,598 10,854,855 2,108,687 12,963,542 2,078,897 15,042,439 2,078,699 17,121,138 2,060,363 19,181,501 2,061,130 21,242,631 2,036,791 23,279,421 2,019,407 25,298,829 2,021,612 27,320,441 2,010,290 29,330,731 2,007,031 31,337,762 1,981,354 33,319,116 1,961,849 35,280,965 1,963,599 37,244,565 1,955,089 39,199,653 1,960,563 41,160,216 41,160,216 440,972,169
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 Total (m2)
Table 65 Projected Energy Savings from Residential Buildings (2010-2029) Climatic Zone 1 - Coastal
Distribution of projected built up area (%)
Cumulative m2 per zone 2010-2029
50%
220,486,084
2 - Western 20% 88,194,433 Mid-mountain 3 - Inland 20% 88,194,433 Plateau 4 - High 10% 44,097,217 Mountain Projected energy savings by type (GJ) Total projected energy savings (GJ)
Energy Analysis and Economic Feasibility Study
Heating Savings
Cooling Savings
GJ/m2
GJ
GJ/m2
GJ
Projected Energy Savings per zone (GJ)
0.0063
1,389,062
0.0251
5,534,200
6,923,262
0.1449
12,779,373
0.0089
784,930
13,564,303
0.1986
17,515,414
0.0203
1,790,347
19,305,761
0.5331
23,508,226
0.0012
52,916
23,561,142
55,192,075
8,162,393 63,354,468
:: 63 ::
5.5
Summary of the Energy Saving Results
Over a 20 year period (2010-2029), the thermal standard for Buildings in Lebanon can generate a reduction in energy use at building input consisting of around 56 million GJ of avoided heating energy and around 8 million GJ of avoided cooling energy, as summarized in Table 66. Table 66 Summary of the Energy Savings at Building Input Building Category
Heating Savings (GJ) 55,192,075 1,320,307 56,512,382
Residential Offices (Non-Residential) Total
Cooling Savings (GJ) 8,162,393 112,420 8,274,813
Energy savings (GJ) 63,354,468 1,432,727 64,787,195
The environmental benefits include the avoidance of around 6 million tons of CO2, as summarized in Table 67. Table 67 projected avoided CO2 emissions (2010-2029) Energy Type
Energy in GJ
Energy in Mtoe
Cooling Energy
Electricity
8,274,813 GJ
0.18 Mtoe
Million tons of CO2 emissions 1.75
Heating Energy
Electricity Diesel Oil/Gas Wood
5,651,238 GJ 45,209,906 GJ 5,651,238 GJ
0.12 Mtoe 1.00 Mtoe 0.13 Mtoe
1.16 3.34 0.62
Total 64,787,195 GJ 1.43 Mtoe 6.87 Note: The CO2 emissions related to electricity have been calculated using the current electricity supply mix which is fuel based and which results in the emissions of 780g of CO2 for every KWh of electricity.
To be noted that the associated economic savings will vary in magnitude depending on the price of fuel and diesel oil. Average estimations indicate savings in the range of 500 million USD. These savings come from cost effective measures and further highlight the positive impacts of the application of the thermal standard for Buildings in Lebanon.
Energy Analysis and Economic Feasibility Study
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ANNEXES
Energy Analysis and Economic Feasibility Study
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ANNEX 1.
DESCRIPTION OF THE RESIDENTIAL BUILDING
This Annex provides a description of the typical residential building used in the simulations. The units are given for convenience in SI and IP units as the simulations on DOE 2 was run in IP formats.
Overall Dimensions Building type: Residential building Shape: Rectangular with important window to wall ratio (WWR) on the South façade Dimension: 18.3 m x 15.2 m Number of floor: 5 floors, 3.3 meters (10.83 ft) height each. Construction •
Roof
There are two options for entering roof characteristics in Visual DOE3. The first one is the quick wall option where just the U value is specified. This option does not take wall mass into consideration and the calculation assumes steady state heat transfer. The second approach is more precise and the input defines each layer of materials including its position in the roof and the mass characteristics. The detailed option has been used in the simulations to take into consideration the mass effect that can be important in mild climate like Lebanon. The R value and U factor for the roof, excluding the interior and exterior air films are shown in the following table. Table A-1 1 Roof Thermal Conductance Layer Material Thickness RSI value (mm) (m2 ºC/W) 1 Tile 30 0.0882 2 Sand 100 0.1176 3 Weatherization 10 0.0588 4 Concrete 200 0.1129 5 Plaster 10 0.0139 Total 0.3913 U value (W/m2 ºC) 2.56 •
Exterior walls
The wall was entered in Visual DOE3 as a wall with complete layers and mass similar to the approach used for the roof. U value calculation for the exterior wall, excluding the interior and exterior air film is shown on the following table. Table A-1 2 Wall Thermal Conductance Material Thickness RSI value (mm) (m2 ºC/W) 1 Plaster 10 0.0139 2 Hollow blocks 150 0.2636 3 Plaster 10 0.0139 Total 0.2914 U value (W/m2 ºC) 3.432 Layer
•
Windows
Clear single pane window 6 mm thick. With a U factor of 6.16 W/m2 C (1.087 BTU/h.ft2.F) and a shading coefficient of 0.95. This model corresponds to window type 1001 in the DOE2.1E window library.
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The building façade has three dimensions for windows, namely: − Type n°1: Window and frame of 1.2 meters x 1.5 meters (3.94 ft x 4.92 ft) − Type n°2: Window and frame of 2.2 meters x 1.5 meters ( 7.22 ft x 4.92 ft) − Type n°3: Window and frame of 2.2 meters x 2.5 meters ( 7.22 ft x 8.2 ft) The number of windows per façade is as follows: − South Façade: Four windows of type n°3 per floor − North Façade : Four windows of type n°2 − East and West Façade: One window of type n°1 and one of type n°2 There is no external shading for the window on the base case. The window frame is defined as “Thermally unbroken aluminum” with a thickness of 1.5 cm (0.05 ft). The window to wall ratio is 20.5%. •
Intermediate floor
The U values for the internal floor are as follow:
Layer 1 2 3 4 5 6 U value
•
Table A-1 3 Internal Floor Thermal Conductance Material Thickness R value (mm) (m2 ºC/W) Tiles 30 0.0882 Mortar 10 0.0139 Sand 60 0.0694 Weatherization 80 0.0444 Concrete 120 0.2164 Plaster 10 0.0139 Total 0.4462 (W/m2 ºC) 2.24
Internal partition
Internal partitions are constituted of a 10 cm (4 in) concrete hollow blocks with plastering on both side. The U factor for the partition is as follows:
Layer 1 2 3 U value
•
Table A-1 4 Conductance of Internal Partition Material Thickness R value (mm) (m2 ºC/W) Plaster 10 0.0139 Hollow blocks 100 0.1646 Plaster 10 0.0139 Total 0.192 (W/m2 ºC) 5.197
Slab on ground
The slab on ground is located in the basement that is an unconditioned space. It is simulated with a 30 cm (12 inches) concrete layer then 100 mm (4 in.) earth then a mat of insulation to limit the exchange to more realistic value that take into consideration the thermal mass of the earth below the slab. This approach is consistent with the recommendation of Lawrence Berkeley laboratory, the designer of the DOE 2 software engine for simulating slabs on ground.
Energy Analysis and Economic Feasibility Study
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Zones The building has two thermal zones per floor and divided in two apartments; the East and West apartments. Both apartments have windows on the south and north façade and two windows on the east or the west façade depending of the apartment considered. The staircase is located inside the building and is non conditioned area. Schedules and Set-points •
Occupancies, lighting and equipment schedules 2 − Lighting density: 16.14 W/m for load calculation. − Light to space fraction: 1 2 − Equipment power density (EPD) = 10.7 W/m for load calculation. 2 − Occupant density = 25.6 m /person − Zone type = conditioned for both apartments, unconditioned for the staircase and the parking (basement) − Occupancy type = Residential − Infiltration = 0.333 ACH
The occupancy is defined as follows: Weekday: Midnight to 6 a.m. 6 a.m. to noon Noon to 1 p.m. 1 p.m. to 6 p.m. 7p.m. to midnight
= = = = =
100% 65% 100% 65% 100%
Friday and Saturday:
Midnight to 6 a.m. = 6 a.m. to 2 p.m. = 2 p.m. to 3 p.m. = 3 p.m. to 7 p.m. = 7 p.m. to midnight =
100 % 65% 100 % 65% 100%
Sunday
Midnight to 7 a.m. = 7 a.m. to noon = Noon to midnight =
100% 65% 100%
• •
Heated at 21.1°C (70°F). Cooled at temperature 23.8°C (75°F).
Heating schedule Cooling schedule
• Fan schedule The fan schedule is on 24h all day. The fan schedule has no incidence on building heating or cooling energy as the fan energy will not be included in the report where the cooling and heating energy requirement will be extracted from Visual DOE3. Outside air schedule on ventilation system is on 24 h all day. • Domestic hot water schedule The domestic hot water schedule and consumption was put at zero to have no thermal load associated to this usage as we are interested on the energy impact of the envelope only. • System Packaged air conditioned system for cooling. A sensitivity analysis was made to vary the efficiency of the cooling system between a 0.32 kW input per kW cooling and an efficiency of 0.37 kW input per kW cooling. The heating is a hydronic hot water type with boiler efficiency of 0.8.
Energy Analysis and Economic Feasibility Study
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ANNEX 2.
DESCRIPTION OF THE COMMERCIAL BUILDING
This section provides a description of the typical building used in simulations. Units are given for convenience in SI and IP as simulations on DOE 2 has been run in IP format. Overall Dimensions Building type: Office building Shape: Rectangular with the largest side facing south and north Dimension: South and north façade 24 meters (78.7 ft), east and west façades 16 meters (52.5 ft). Number of floors: 5 floors, 3.3 meters (10.83 ft) height each. Construction •
Roof
The detailed option of Visual DOE3 has been used in simulations to take into consideration the mass effect that can be important in mild climate like Lebanon. The R value and U factor for the roof, excluding interior and exterior air films is shown in the following table. Table A-2 1 Roof Thermal Conductance Layer Material Thickness RSI value (mm) (m2 ºC/W) 1 Tile 30 0.0882 2 Sand 100 0.1176 3 Weatherization 10 0.0588 4 Concrete 200 0.1129 5 Plaster 10 0.0139 Total 0.3913 U value (W/m2 ºC) 2.56 •
Exterior wall
The wall was entered in Visual DOE3 as a wall with complete layers and mass similar to the approach used for the roof. U value calculation for the exterior wall, excluding interior and exterior air films is shown in the following Table. Table A-2 2 Wall Thermal Conductance Material Thickness RSI value (mm) (m2 ºC/W) 1 Plaster 10 0.0139 2 Hollow block 150 0.2636 3 Plaster 10 0.0139 Total 0.2914 U value (W/m2 ºC) 3.432 Layer
•
Window
Clear single pane window of 6 mm thick with a U factor of 6.16 W/m2 C (1.087BTU/h.ft2.F) and a shading coefficient of 0.95. This model corresponds to window type 1001 in the DOE2.1E window library. The building façade has two dimensions for windows, namely: − Type n°1: window and frame of 1.4 meter x 2.9 meters (4.5 ft x 9.47 ft) − Type n°2: window and frame of 1.4 meter x 2.4 meters (4.5 ft x 8 ft)
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The number of windows per façade is as follows: − South and north façade: five windows of type n°1 per floor − East and west façade: four windows of type n°2 per floor There is no external shading for windows on the base case. The window frame is defined as “thermally unbroken aluminum” with a thickness of 1.5 cm (0.05 ft). The window to wall ratio is 25.4%. •
Intermediate floor
The U value for the internal floor is as follows: Table A-2 3 Internal Floor Thermal Conductance Layer Material Thickness R value (mm) (m2 ºC/W) 1 Tile 30 0.0882 2 Mortar 10 0.0139 3 Sand 60 0.0694 4 Weatherization 80 0.0444 5 Concrete 120 0.2164 6 Plaster 10 0.0139 Total 0.4462 U value (W/m2 ºC) 2.24 •
Internal partition
Internal partitions are constituted of a 10 cm (4 in) concrete hollow blocks with plastering on both sides. The U factor for the partition is as follows: Table A-2 4 Partition Thermal Conductance Layer Material Thickness R value (mm) (m2 ºC/W) 1 Plaster 10 0.0139 2 Hollow block 100 0.1646 3 Plaster 10 0.0139 Total 0.192 U value (W/m2 ºC) 5.197 •
Slab on ground
The slab on ground is located in the basement, which is an unconditioned space. It is simulated with a 30 cm (12 inches) concrete layer then 100 mm (4 in.) earth then a mat of insulation to limit the exchange to more realistic value that take into consideration the thermal mass of the earth below the slab. This approach is consistent with the recommendation of Lawrence Berkeley laboratory, designer of the DOE 2 software engine for simulating slabs on ground. Zones The building has five thermal zones per floor and they are divided in four orientations plus an internal zone per floor. There is also a basement that is considered as a parking and is not conditioned. All other spaces above ground are conditioned. Schedules and Set-points • Occupancy, lighting and equipment schedules 2 • Lighting density: 16.14 W/m design • Light to space fraction: 1 • Equipment power density (EPD) = 10.76 W/m2 design 2 • Occupant density = 16 m /person • Occupancy type = office • Infiltration = 0.333 ACH
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The same schedule is used to define occupancy, lighting operation and equipment (outlet for computers, faxes, printers, etc.) in zones. Weekdays:
Saturdays:
Sundays
12 midnight to 7 a.m. 7 a.m. to 8 a.m. 8 a.m. to 12 noon 12 noon to 1 p.m. 1 p.m. to 5 p.m. 5 p.m. to 7 p.m. 7 p.m. to 12 midnight 12 midnight to 7 a.m. 7 a.m. to 1 p.m. 1 p.m. to 12 p.m. Off
= = = = = = = = = =
0% 40% 90% 80% 90% 20% 0% 0% 90% 0%
• Heating schedule The building is heated at 21.1°C (70°F) from 6 a.m. to 7 p.m. weekdays and 7 a.m. to 1 p.m. Saturday. The rest of the time, the temperature is allowed to float down to a setback minimum temperature of 12.8 C (55°F). The system starts at 6 a.m. to insure that at 8 a.m. the temperature is close to comfort level. • Cooling schedule The temperature set-point is 23.3°C (74°F) from 6 a.m. to 7 p.m. on weekday and 7 a.m. to 1 p.m. on Saturday. The rest of the time, the temperature is allowed to float to a set-up maximum temperature of 37.2°C (99°F). The system is scheduled to start 2 hours before building occupancy to insure comfort during occupied time. • Fan schedule The fan schedule is on 24h all days. It does not have incidence on building heating or cooling energy as the energy used by the fan will not be included in the report where the cooling and heating energy requirement will be extracted from DOE2. •
Outside air schedule on ventilation system Weekday: 7 a.m. to 7 p.m. Saturday: 8 a.m. to 1 p.m. Sunday: off
•
Infiltration schedule:
0.33 air change per hour.
• Domestic hot water schedule The domestic hot water schedule and its associated consumption were put at zero to have no thermal load associated to this usage in order to not affect the results of the study that is focused on the thermal envelope. • System Packaged air conditioned system for cooling with an efficiency of 0.32 kW input/kW thermal. Air volume and fresh air are constant. The heating is a hydronic hot water type with a boiler efficiency of 0.8. The system was automatically sized once by DOE2 to see the recommended air volume in steady state conditions. However, as the system will be used with variable set-points, an automatic sizing is not adequate as the resulting system will be too small to rapidly bring the interior temperature in the comfort range following a period of set-up or set-back temperature in the night or during the weekend. An adjustment of air volume was then manually made to take into account the extra capacity required for rapidly bringing the interior temperature to comfort level. This added capacity is needed since for a normal operation the systems temperature set-points are set to start the system only two hours before the occupation time to reach a comfort level. The adjustment of the ventilation system was made by specifying a minimum air flow per square feet for the system.
Energy Analysis and Economic Feasibility Study
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ANNEX 3.
HYPOTHESES OF ENERGY PRICE FORECAST
This annex provides information on how the energy forecast hypothesis were selected for the study. Crude oil cost forecasts hypothesis For the purpose of the study, the crude oil price forecast has been considered for a 20 year period (2005-2024). The following three studies were used as references for the selection of the forecast values: a)
International Energy Outlook 2004 (IEO 2004): the yearly outlook published by the American Department of Energy. Its conclusions are summarized in Table A-3 1 and provides expected prices for i crude oil per barrel for three different scenarios. Table A-3 2 provides information about a previous release of the long-term forecast issued in 2000. This information will be useful to analyze the variation between the two forecasts. Table A-3 1 Prices of Oil in 2002 USD per Barrel Forecasts 2005 2010 2015 2020 Reference Case 24.17 25.07 26.02 27.00 High Price Case 34.27 34.23 34.63 35.03 Low Price Case 16.98 16.98 16.98 16.98 Note: IEO 2004 projections are for average landed imports to the United States.
Table A-3 2 Prices of Oil in 2000 USD per Barrel (Reference) Forecasts 2005 2010 2015 2020 Reference Case 22.73 23.36 24 24.68 High Price Case 29.56 30.01 30.44 30.58 Low Price Case 17.41 17.64 17.64 17.64 Note: IEO 2000 projections are for average landed imports to the United States. The difference in price between the 2000 and 2004 forecast for the reference case over a 20 years period is a 6.5% increase. This percentage will be later used in the calculation for forecasting future price of electricity. b)
World Energy Outlook 2000 (WEO 2000): The yearly outlook published by the International Energy ii Agency (IEA) . This study provides forecasts in 2000 USD constant value at around $20.4/barrel until iii 2010 followed by a constant and linear increase till 2020 at USD 27.83 /barrel . As this analysis is older, it was not retained as the basis for this study.
c)
A study published in 2000 by the “Conseil d’Analyse Économique”, the official consulting office of the French Prime Minister, adopts the figure of USD 24,00 /barrel in constant year 2000 USD value, as the most suitable for OPEP producers on a 20-year horizon. This figure, which is close enough to what is found in Table A-3 3, is thus in general agreement with the 2000 projection of the US agency.
All three studies reveal the difficulty of making forecasts over long periods. Political instability in the region can have dramatic impacts on future oil prices. The level of volatility of oil price in 2004 and 2005 combined with the forecasted shortage of traditional fossil fuel (with variable scenarios) energy somewhere in the future and fuelled by the growth of economies in transition and in development add some more uncertainties to these forecasts. The recent surge in price in 2005 for crude oil at USD 50 per barrel shows that the official projections have difficulty taking into account the complexity of the market. Also, official projections from countries that are net importers of energy will obviously not project in the future large increases in cost as this could be perceived by producers as an indication of the willingness to pay of these countries and will be rather an unwise move in an official government publication. Nevertheless, the trend speaks for itself as we see the world energy usage growing rapidly, fuelled by the development of large economies like China. It is interesting to look at the recent short term forecast releases by the International Energy Agency (USA) that projects for the period 2005 to 2006 an average price between USD 40 and 45 /barrel. It also predicted that volatility in the market during peak consumption season could bring the market price to over 45 USD/barrel. Energy Analysis and Economic Feasibility Study
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On the other hand, the increase in world energy demand is still strong and increased by 2.4% in 2004. The report from the US agency for the base case scenario is entirely based on the hypothesis that OPEP will be willing to control the price in the market by regulating the production in order to keep the oil price in a range of USD 22 to 28 per barrel. But in a context of declining availability of fossil fuel global resources, introduction of new types of energy sources at higher production cost and the strategy for oil producers to get the maximum value from their remaining reserve, the base hypothesis seems a bit optimistic or merely reflecting the wishes of the largest importers of oil for the future price of this resource. Nevertheless, to be on the conservative side in the study for the thermal standard, the base case from the US agency was selected as the most probable scenario and the basis to develop our three scenarios for electricity cost. Prices are an average cost for the next 20 year period (2004 to 2024) in constant 2003 US dollars (not taking into consideration the general rate of inflation). IEA’s projections have been kept for the Lebanon thermal standard analysis. Table A-3 3 Crude Oil 20 Year Forecast Assumptions Alternative Low Base High
Price (USD 2002/Barrel) 20.452 25.565 30.678
Remarks - 20% from the base case + 20% from the base case
Diesel Oil Cost Forecasts Diesel price to distributor hypothesis After establishment of the hypothesis for crude oil price, the assumption for price of domestic oil used for heating in residential dwellings was determined. To determine a representative diesel cost in Lebanon, a review of the local price policy was conducted. After which the base case cost of EN590 for local distributors was set at USD 256.2 /kl. Domestic diesel oil prices for consumers After determining the cost of diesel oil to distributors, public authorities still need to determine the final cost at the retail outlet and oil station for consumers. This is done by adding the following components to the distributor price: a) Product taxes that are generally applied with the notable exception of domestic diesel oil. b) The maximum benefit of distribution companies that is currently fixed at 7,000 LP/kl. c) The transportation cost from the storage tanks of importers to oil stations that are fixed at a maximum of 8,000 LP/kl. d) The maximum benefit of station owners is fixed at 20,000 LP/kl. e) Since all maximum benefits and intermediary costs are fixed by the government, the maximum selling price to the customer is easy to evaluate. This allows the determination of a maximum VAT that will be paid by importers to the government on behalf of all other actors of the distribution chain. The VAT amount is currently fixed at 45,000LP/kl. With a maximum selling price to distributors of 415,000LP/kl, the final price for a liter of EN590 to the consumer reaches 495,000 LP. This corresponds to 9,900 LP/20 liters or 49.5 LP/l.
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This calculation supposes that all actors in the distribution chain will take the maximum allowed benefit which is not always the case but it can be taken as a general rule. Therefore, the following assumptions will be used: a) b) c) d) e)
A price of USD 256.2 /kl for local distributors. Product taxes unchanged at 0%, as diesel oil is mainly used by industries. VAT is not changed. Other various components of the tariff structure unchanged. US dollar rate exchange is maintained at 1,500 LP per USD.
In these conditions, the selling price in constant USD 2002 for the Lebanese consumer would be: 256,320 + (7,000+8,000+20,000+45,000)/1,500 = USD 309.6 /kl, i.e. USD 0.3 /liter. In that context, considering the base price of USD 0.3 per liter and variations of +/-20% as per IAE to determine a high and low scenario, we can forecast the following prices of EN590 for Lebanese consumers as follow:
Scenario Low Base High
Table A-3 4 Diesel Oil 20 Years Forecast Assumptions Price Remarks (USD 2002/litre) 0.24 20% lower than base case 0.30 0.36 20% higher than base case
Cost of electricity The forecasts for the cost of electricity to consumers is based on the current structure of the power tariff in Lebanon and on a study conducted in 1999 by “Électricité de France” (EDF) for “Électricité du Liban” (EDL). Table A-3 5 presents an overview of the main tariffs applied by EDL. Other sector rates are shown for comparison.
End User Building Sector
Hotels and Large Buildings Small Industries Agriculture Sector Public Sector
Table A-3 5 EDL Tariff Structure Monthly Price (LP/kWh) Consumption (kWh) < 100 35 100-300 55 300-400 80 400-500 120 >500 200 150 approx. Flat 115 Flat 115 Flat 140
Price (US cents/kWh) 2.3 3.6 5.3 8.0 13.2 10.0 7.6 7.6 9.3
Cost of electricity for residential, small offices and schools Residential, office, and school buildings subscribe to EDL at the low voltage distribution level and thus follow the low voltage rate structure. Table A-3 5 shows that when consumption exceeds 500 kWh, the highest rate is imposed i.e. 200 LP/kWh.
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The following sections will discuss the marginal rate corresponding to different categories of customers. •
Residential, small offices and private schools
The assumption retained for the determination of the marginal electricity cost to consumers in this category is that residences, small offices and private schools consume more than 500 kWh for base needs (lighting, refrigerators, water heating, electronic equipment and other appliances). Electric consumption for cooling and heating is considered a secondary usage added to base needs. A survey about the spending on energy purchases for families in the Beirut area showed that all income brackets except for the two poorest exceed 414.000 LP/year or an average of 34.500 LP/month which is the threshold to be consuming the last kWh in the most expensive bracket. Given the fact that basic needs such as lighting, food refrigeration, water heating and washing have to be satisfied at first, it is logical to estimate the cost of electricity for air conditioning in residential houses at 200 LP/kWh. Therefore, the study considered the following scenarios for the price of the kWh for residential buildings, small offices and private schools: a)
b)
c) d)
Low case: things remain unchanged for government’s policy of subsidizing electricity. However, the current rate structure is adjusted to reflect the increase in long-term projection of the crude oil cost over the next 20 years. The difference between the 2000 IEA’s projection (when the study on which the rate analysis is based) and the 2004 IEA’s projection was consulted to determine this adjustment. The proposed adjustment will be 6.5% of the oil price in the market. With this hypothesis, the Lebanese government still pays for all losses incurred by EDL during current and previous years. Therefore, the base scenario will consider that residential houses, small offices and public schools pay 200 LP/kWh plus 6.5% or 213 LP/kWh. VAT (10%) must be added for residential houses only, for a total of 234,300 LP/kWh. High case: the international oil price is adjusted by 6.5% to reflect the change in long-term trend concerning the international oil price between 2000 and 2004. The rate structure is changed by the government so there is a break even for EDL. Bill recovery is complete (no arrears in payment from customers). The energy rate increase required is 13% so that EDL can break-even. The new rate for the highest category of users would be: 200 x (1.00+0.13) = 226,000 LP/kWh. When VAT is added, this raises the customer cost for energy to 248,600 LP/kWh. Base case: this base case scenario is between the low and high scenario. So the electricity cost would be 219,500 LP/kWh before VAT and 241,450 LP/kWh with VAT. As far as VAT is concerned, the study considered that the present rate of 10% would remain unchanged for the coming years.
The three following scenarios for electricity price in residential buildings, small offices and private schools were consequently adopted as shown in Table A-3 6.
Scenario Low Case Base Case High Case
Table A-3 6 Cost of Electricity in LP and US$ per kWh Residential Houses Offices and Others (LP or USD/kWh) (LP or USD/kWh) 234,300 LP – USD 0.1562 213,000 LP – USD 0.1420 241,450 LP – USD 0.1609 219,500 LP – USD 0.1463 248,600 LP – USD 0.1657 226,000 LP – USD 0.1506
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ANNEX 4.
METHODOLOGY OF ECONOMIC ANALYSIS
The method One purpose of the present study is to show the impact of additional investments in thermal improvement measures on energy consumption and operating cost of buildings. The method used will consist of a comparison between two life cycle cost scenarios. The difference in life cycle cost for a base alternative and an improved alternative is equal to the net present value (NPV) of the proposed improvement. The two life cycle costs that will be compared are as follows: a)
The life cycle cost for building envelope and heating and cooling operation cost if buildings are kept unchanged in the future (LCC1 – base case). The life cycle cost of for the improved building (LCC2). This life cycle cost will include additional cost for construction and reduced energy expenses during operation.
b)
The difference between the LCC1 and LCC2 is equivalent to the net present value of the investment realized to improve the building thermal envelope. The incremental cost becomes the difference in investment and the difference in operating cost between the two scenarios defines the stream of energy savings. The future stream of money is discounted in the LCC’s analysis by an amount representing the minimum acceptable rate of return (MARR) of the investment realized. A NPV of zero means that the investment projected just meets the criteria of return of the investment projected. So a NPV of zero means that the investor should decide to invest in the proposed project. A positive NPV means that the investment provides a return above the expectation of the investor. The discount rate for the cash flows of the two alternatives is the same because both projects carry the same amount of risk. As we are using an average cost for energy over 20 years, the formula for discounting of future flow of money can be analytically solved. The following formula is used to calculate the NPV:
n ( 1+ d ) − 1 Vd = A × n d ×(1+d ) Where: NPV = Net present value of the investment I = Incremental cost of the investment in thermal envelope improvement S = Energy savings d = Discount rate N = Number of years in the study period Each of the components used in the formula is discussed below: •
Incremental cost or investment
This is evaluated for each measure as the difference in material and construction cost for the base case and the improved thermal envelope. The avoided cost for heating and cooling equipment is also taken into consideration in the analysis.
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•
Study period
In our case, the study period was fixed at 20 years. Despite the fact that buildings have life durations of more than 20 years, this period is long enough as: a) Discounted cash flows become negligible after 20 years, b) Forecasts on energy prices over a very long period are difficult to make. •
Energy savings
Heating expenses will be based on the cost forecast for fuel as it is the more conservative (less expensive) choice for heating building structure. Cooling expenses will be based on the cost forecast of electricity as all cooling devices work with electricity (except absorption machines or internal combustion engine driven chillers that are not widely used in Lebanon). •
Discount rate
The discount rate is an important element in project economic evaluations. Its determination is always difficult and, most often, the proposed values can be challenged as it depends of the perception of various categories of investors on what could be the minimum expected return of a project to trigger a decision for investment. The study considered the discount rates that reflect interest rates proposed by the local banking sector for investments with the same level of risk. In the case of investments with no commercial risks, such as energy conservation, to the comparable investment is in treasury bonds of the Lebanese government. Three investor discount rates are presented below and will be used to determine the low, base and high alternatives for discount rates. a) Interest rates on treasury bonds of the Lebanese government in US$ The issuing of treasury bonds used as reference for this study took place in May and August 2002. Their duration was five years and they had an interest rate of 10.5%. The duration of another bond issued during the same period was 15 years, which is close to the 20-year horizon of our study. It has an interest rate of 11.625%. Therefore the study considered a discount rate of 12% for investments by local people on a 20-year horizon as a base scenario for discount rate. This choice can also be justified by the fact that local banks currently ask a minimum of 11-12% on personal loans in USD. b) Interest rates on treasury bonds of the Lebanese government in LP The reference issuing of treasury bonds in LP took place on February 2, 2003. Durations were 3 and 6 months and bonds had interest rates of 6.96% and 8.18% respectively. Before that, a bond issue with two-year duration and 9.38% rate took place on December 23, 2002. Therefore, given the previous considerations, it seems logical to take a discount rate of 8% for investments in LP on a 20-year horizon by local people. This will be the low scenario for discount rate. c) Discount rates for investors with a broader choice of investments iv Country risk premiums for long-term investments in countries rated B- are currently around 12.6% . For 10 to 30-year long investments in the United States, current premiums for risk are around 4.5%. This means that, for foreign investors, the discount rate for an investment in Lebanon would be: 4.5% + 12.6% = 17.1%. For investments in Lebanese pounds, this discount rate should be adjusted with the differential in inflation rates. The consultants will consider that inflation in both the United States and Lebanon will be at very low and similar percentages. In such conditions, the discount rates for investments in US$ and in LP should be the same. For the 20-year period of the study, it seems realistic to count the total discount rate for Lebanon including country risk premium at around 18%. This value will also be the discount rate used for the cash flows related to investments in energy conservation measures. This will be considered the high scenario.
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ANNEX 5.
SCENARIOS FOR ENERGY AND DISCOUNT RATE VARIATIONS
In order to assess the sensitivity of the economic analysis of measures given variation in energy price and discount rate, nine scenarios were prepared to see the range of economics results that will be achieved with each thermal improvement measure. The sensitivity analysis is therefore based on combinations of various oil prices and discount rates. By combining three scenarios for energy price and three scenarios for discount rate, we come up with nine different scenarios for the economic analysis. Table A-5 1 Presentation of the Nine Scenarios Parameter 1 – Energy Price (at right) Low Base High Alternative Alternative Alternative Domestic diesel oil (EN 590) price ($/liter) 0.24 0.30 0.36 Electricity price for residential houses ($/kWh) 0.1562 0.1609 0.1657 Electricity price for offices and other buildings ($/kWh) 0.1420 0.14633 0.1506 Parameter 2 – Discount Rate (below) Resulting Combined Scenarios Low alternative for discount rate at 8% Scenario 3 Scenario 1 Scenario 2 Base alternative for discount rate at 12% Scenario 6 Scenario 4 Scenario 5 High alternative for discount rate at 16% Scenario 9 Scenario 7 Scenario 8 Scenario 4 is considered as the preferred scenario and will be used to determine the requirements of the thermal standard. The other scenario will be used to perform sensitivity analysis to see the range of variation caused by the variation in both main parameters used for the NPV calculation and what is the resulting optimum level of prescription for the thermal standard in each case. Scenario 3 is also interesting as it can be considered as a scenario that represents the investment position of a typical customer if EDL maintains a policy of subsidized energy. Scenario 8 is also interesting as it can be considered as the country level economic analysis where EDL wants to maintain a break even position on its electricity sales in the market and the investment is compared to other international investors’ requirement for discount rate. The combination of parameters for the nine scenarios is summarized in the table below. Table A-5 2 Parameters That Will be Applied in the Nine Scenarios
Residential Buildings
Office Buildings and all Non-Residential
Scenario
Discount Rate (%)
Diesel Oil Price (USD/liter)
Electricity Price (USD/kWh)
1 2 3 4 (Preferred) 5 6 7 8 9 1 2 3 4 (Preferred) 5 6 7 8 9
8 8 8 12 12 12 16 16 16 8 8 8 12 12 12 16 16 16
0.30 0.36 0.24 0.30 0.36 0.24 0.30 0.36 0.24 0.30 0.36 0.24 0.30 0.36 0.24 0.30 0.36 0.24
0.1609 0.1657 0.1562 0.1609 0.1657 0.1562 0.1609 0.1657 0.1562 0.1463 0.1506 0.1420 0.14633 0.1506 0.1420 0.1463 0.1506 0.1420
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ANNEX 6.
INCREMENTAL CONSTRUCTION COSTS
Formulas below were used to determine the incremental cost for every change in the thermal characteristics of roof, wall, window and architectural shading devices. Prices in the base case scenario are referenced as P. 1) 2) 3) 4)
Roof insulation: 2 2 Incremental cost per unit area of building = (Incremental cost per m of roof x roof surface area in m ) Wall insulation: 2 2 (Incremental cost per m of wall x wall surface area in m ) Window improvement (U value or SC): 2 2 (Incremental cost per m of window x window area in m ) Fins and overhang cost: 3 (Incremental cost per m of concrete needed to build these architectural shading devices x fins and 3 overhang volume in m )
The following prices were used for the determination of the incremental cost. a)
Cost of insulating material and installation
A range of insulating materials exists on the market with different technical characteristics and prices. The 2 economic analysis was done considering the use of expanded polystyrene at USD 0.70 /cm/m . The cost for installation is estimated at USD 2/m2 whatever the thickness. b) Cost for double wall construction with insulation The preferred approach for the insulation of walls in Lebanon would be to install the insulation between two layers of walls. This approach is well suited to the actual construction market and would allow implementing better thermal insulation with a minimum disruption and market transformation requirement. Wall is typically composed of hollow concrete blocks covered with interior and exterior painted cement plaster. Some cladding could also be used. The following costs were considered in the analysis: • One of the two following options for wall thickness: 2 − Hollow blocks 20 cm thick = USD 8.5 /m (labor + material) 2 − Hollow blocks 15 cm thick = USD 6.5 /m (labor + material) • Interior and external plaster = USD 7 /m2 (2x USD 3.5 /m2) • Internal painting = USD 3 /m2 • One of the three following options for finishing − Exterior painting = USD 5 /m2 − Natural stone cladding = USD 22 /m2 − Granite cladding = USD 49 /m2 2 Therefore, the total incremental cost will vary between USD 21.50 and 23.50 /m for external painting; USD 2 2 38.50 and 40.50 /m for natural stone; and USD 65.50 and 67.50 /m for granite finishing.
For the purpose of a thermal standard, the incremental cost will be determined only by the hollow blocks and insulation cost, as the external cladding or the finishing is entirely at the discretion of the promoter. The incremental price that will be used for walls with insulation will be as follows:
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Simulation
Base Case Alternative n° 1 Alternative n° 2 Alternative n° 3 Alternative n° 4
Table A-6 1 Double Wall with Insulation Incremental Cost Incremental Cost Composition Baseline Incremental Total USD/m2 USD/m2 USD/m2 Single wall without insulation: 20mm. light weight 15.5 0 15.5 cement, 200 mm blocks, 20mm light weight cement. Double wall and insulation: 2 cm. light weight cement, 15 cm hollow concrete blocks, 2 cm. polystyrene 11.9 27.4 insulation, 10 cm hollow concrete blocks. Alternative n°1 but with 4 cm. polystyrene insulation 13.3 28.8 Alternative n°1 but with 6 cm polystyrene insulation 14.7 30.2 Alternative n°1 but with 8 cm polystyrene insulation 16.1 31.6
c) Incremental cost for roof insulation The incremental cost for the roof will be based on insulation and installation costs only as the rest of the roof composition will remain the same. Table A-6 2 Incremental Cost for Roof Insulation Composition
Incremental cost Per m2 USD/m2
Base case
20 cm undried agregate, 10mm built-up roofing and air films, 10 cm. of sand, 3.2 cm. tile,
0.00
Alternative n° 1
Base case with a layer of 2 cm polysterene insulation
3.40
Alternative n° 2
Base case with a layer of 4 cm polysterene insulation
4.80
Alternative n° 3
Base case with a layer of 6 cm polysterene insulation
6.20
Alternative n° 4
Base case with a layer of 8 cm polysterene insulation
7.60
Simulation
d) Cost for window replacement The following incremental cost will be used for window improvements: Table A-6 3 Cost for Window Replacement Composition
Characteristics
Option Base 1 2 3 4 5 6 7 8 9
Single (6 mm) Single (6 mm) Single (6 mm) Single (6 mm) Single (6 mm) Double (6mm, 12mm gap, Double (6mm, 12mm gap, Double (6mm, 12mm gap, Double (6mm, 12mm gap, Double (6mm, 12mm gap,
6mm) 6mm) 6mm) 6mm) 6mm)
Tint
Reflective
Low-E
Filling
Clear Bronze Clear Clear Clear Clear Bronze Clear Clear Clear
----Tin-Oxyde Titanium ------Stainless H --0
--------e = 0.2 ------e = 0.2 e = 0.2
----------Air Air Air Air Argon
Base USD/m2 50
Incremental cost Increment Total USD/m2 USD/m2 4 54 15 65 20 70 50 100 50 100 78 128 85 135 160 210 250 300
f) Cost for fins and overhang A uniform price of USD 110 per cubic meter of concrete needed to build the fins and overhang has been considered. This price includes formwork, reinforced concrete and work.
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ANNEX 7.
AVOIDED COST ON HVAC SYSTEMS
The improvement of the thermal envelope of buildings will allow reducing the capacity of heating and cooling equipment installed in these buildings. A superficial market survey was conducted to assess the cost of these equipment in the market and to derive average avoided cost for these systems. The avoided cost will not include the labor as very often it will remain the same for the installation of the heating system. For instance, the same number of convectors will probably be needed but smaller units will be installed. So the material cost will decrease but the installation will be quite similar. Avoided cost of Heating equipment Heating avoided cost will vary according to the type of heating system used in the building. For electrical heated building, the avoided cost will only be the reduction in electric convector (for baseboard heating systems). For hydronic systems, savings will be the summation of the reduction in size for the boilers and the hot water convector. Two ranges of price reduction will be considered, one for the residential sector and the other one for the rest of the market. Table A-7 1 shows the approximate price for boilers and burners in the market. A range was established by surveying some equipment vendors and average values were selected from this Table as typical of the market value. They are presented in Table A-7 2. The cost provided from the survey is in Euro but the final avoided cost per kW will be converted in US$ to be consistent with other figures used in the study. Table A-7 1 Typical Price Range for Boiler Units Component Capacity (kW) Price Range (Euro) Boilers and Burners 21 kw 651 – 1,812 28 kw 718 – 1,949 34 kw 777 – 2,102 40 kw 910 – 2,306 47 kw 980 – 2,461 58 kw 1,050 – 2,746 70 kw 1,694 – 2,917 85 kw 1,834 – 3,367 105 kw 2,044 – 4,264 Table A-7 2 Typical Price of Boilers Used in the Study Component Capacity Average Type (kW) Price ( ) Boilers + 26 kW 1,330 Cast iron boiler Burners (fuel) 50 kW 1,701 78 kW 2,160 105 kW 2,772 The incremental cost for boilers in the residential sector has been retained as the cost per kW for the difference between a 26 kW unit and a 50 kW unit. Increment (boiler – residential) = (1701 – 1330 ) / (50 kW – 26 kW) = 15.5 /kW Incremental (boiler – others) = (2160 - 1701 )/(78 kW – 50 kW) = 22.7 /kW Table A-7 3 shows the typical price of the market for electric and hot water radiators. A typical avoided cost per kW has been determined by comparing the 1,000 W units to the 1,500 W units. The same price will be applied in all the building types as they are both popular power. The incremental cost for electricity and hot water is then calculated as: Increment (electric) = (90 – 80 ) / (1.0 kW – 0.75 kW) = 40 /kW Incremental (hydronic) = (63 - 49 )/(78 kW – 50 kW) = 56 /kW Energy Analysis and Economic Feasibility Study
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Table A-7 3 Cost for Heating Convectors Capacity Price Notes Electric Convector
Hot Water Convector
500 w 750 w 1,000 w 1,500 w 500 w 750 w 1,000 w 1,500 w
50 80 90 105 35 49 63 91
Cast radiator
iron
Based on the avoided cost for boilers and convectors, we can calculate the total avoided cost for electricity and for hydronic systems. Electricity avoided heating cost = 40 /kW Hydronic – residential avoided heating cost = 56 /kW + 15.5 /kW = 71.5 /kW Hydronic – other buildings avoided heating cost = 56 /kW + 22.7 /kW = 78.7 /kW Then, a blended cost for electricity and fossil systems were calculated to obtain an average cost weighted by the penetration of each type of heating system used in the market. The establishment of the exact proportion of electricity and hydronic systems in the market was not possible as no studies have been made on this subject. However, the following estimates were made to represent the current blend of heating systems and sources: • Wood : 23.9% • Electric heating : 16.4% • Gas/diesel fossil fuel: 59.7% Based on these values, the resulting weighted avoided cost was considered. For the wood system, no reduction in installations was considered. Residential – heating = 49.22 /kW (US$63.97 /kW) Other buildings – heating = 53.53 /kW (US$69.56 /kW) Avoided Cost of Cooling system Table A-7 4 provides the price range observed in the Lebanese market for some types of air conditioning equipment. Table A-7 5 provides prices for lesser used heat pumps and large chillers. The establishment of the avoided cost for the residential system will be done by comparing the price of the 1.5 tons and 1.0 ton units. Average price for 1.0 ton = (300 + 750) / 2 = US$525 Average price for 1.5 tons = (450 + 1250) / 2 = US$850 The difference will thus be US$325 for a difference of 0.5 ton. Considering that the cooling unit has an input power of 0.32 kW/kW thermal cooling then, the avoided cost will equal USD 577.55 per kW input to the cooling unit. The establishment of the avoided cost for the other types of buildings will be based on the comparison of a 5 ton unit and a 4 ton unit. Average price for a 4 ton = (1,650 + 2,550) / 2 = USD 2,100 Average price for a 5 ton = (1,850 + 2,750) / 2 = USD 2,300 The difference will be USD 200 for a 1 ton capacity difference. Considering that the cooling unit has an input power of 0.32 kW/kW thermal cooling then, the avoided cost will equal USD 177.71 per kW input to the cooling unit.
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Table A-7 4 Price Range for Air Conditioning Equipment Capacity Price Range (USD) Split Unit 0.75 T 250 – 650 1.0 T 300 – 750 1.5 T 450 – 1,250 2.0 T 500 – 1,450 Central Unit 2.5 T 750 – 1,650 3.0 T 1,450 – 2,350 4.0 T 1,650 – 2,550 5.0 T 1,850 – 2,750 Table A-7 5 Price for Heat Pump and Chillers Wall Split Unit 0.75 TR USD 490 Heat pump 1 TR USD 525 1.5 TR USD 775 2 TR USD 890 Water Chiller 50 TR 40,000 Cooling only 75 TR 55,000 100 TR 65,000 150 TR 75,000 Above equipment prices are subject to an additional 10% VAT
i
IEO 2004, Table 9, page 42. WEO 2000, p.39 iii These prices are average IEA’s crude oil import prices. iv Reuters financial news agency or www.bondsonline.com , among others ii [3]
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