Solar Irradiance Forecasting Using Multi-layer Cloud Tracking and ...
Solar Irradiance Forecasting Using Multi-layer Cloud Tracking and Numerical Weather Prediction Jin Xu, Shinjae Yoo, Dantong Yu, Dong Huang, John Heiser, Paul Kalb
Solar Energy Abundant, clean, and secure renewable resource Intermittent nature of solar irradiance challenges the regulation and maintenance of grids Clouds cause fluctuations in the photovoltaic (PV) output
2
Motivation: Climatology on the East Coast
3
Motivation: Climatology on the East Coast
Reliable 1-15 minute short term irradiance forecasting is essential for grid operators to control ramp events. 3
Short-term Forecasting Workflow Feature Engineering
Cloud Tracking •
Image preprocessing
•
Data calibration
•
Cloud cover classification
•
Feature extraction
•
Correlation analysis with irradiance
•
Feature selection
•
Multi-layer cloud detection
•
Segment identification
•
Cloud tracking
8
Prediction •
Linear & non-linear modeling
•
Model and feature set evaluation and cross validation
Sky Imaging Based Prediction Short term ground-based sensor data collection
High spatial and temporal resolution Able to capture local cloud movement
Pyranometer (irradiance sensor)
TSI (Total Sky Imager)
TSI instrument
Sample TSI image
4
Solar Irradiance Fluctuations
TSI image sequence
Ground irradiance 5
Limitations of Current Methods For Sky Image Processing Thin, high altitude clouds are difficult to detect Unable to differentiate between multiple cloud layers
Block and pixel based tracking are noise prone Accurate forecasts are limited to 5 minutes
6
Novel Solar Irradiance Forecasting Framework Multi-layer cloud detection and tracking • Cloud type classification • Multi-layer detection • Segment identification
Numerical Weather Prediction (NWP) incorporation • Extend the forecasting horizon to 15 minutes
7
Novel Cloud Tracking Pipeline
9
Novel Cloud Tracking Pipeline
Original image
9
Novel Cloud Tracking Pipeline
Image undistortion Shadowband dispatch
10
Novel Cloud Tracking Pipeline
Cloud Cover
11
Novel Cloud Tracking Pipeline
12
Novel Cloud Tracking Pipeline
12
Novel Cloud Tracking Pipeline
Cloud segmentation
13
Novel Cloud Tracking Pipeline
Normalized Cross Correlation Algorithm
17
Novel Cloud Tracking Pipeline
t-1
t
Example cloud segments at time t-1 and t and corresponding predicted motion vectors
14
Method Comparison
Segment based cloud tracking with multi-layer detection
Block based tracking
15
Multi-layer Detection and Segment Identification
20
Feature Engineering: TSI Image Features
16
Feature Engineering: NWP Features Category
17
1
Rain
2
Light Rain
3
Overcast
4
Mostly Cloudy
5
Partly Cloudy
6
Scattered Cloud
7
Fog
8
Clear
9
Sunny
Full Feature Set TSI Feature Set circumsolar RBR, motion vector length/count/sum, cloud cover mean/variance, blue channel max/min, shadowband brightness.
Weather Feature Set category, temperature, humidity, pressure, wind direction, and wind speed.
22
Forecast Models Persistent Model (PM) Linear Regression (LR) • One TSI feature (LR_RBR,GHI) • All features (LR_all)
Support Vector Regression (SVR) • Linear kernel (SVRlnr_all)
• Radial Basis Function kernel (SVRRBF_all)
18
Forecast Models Persistent Model (PM) Linear Regression (LR) with only one TSI feature (LR_RBR,GHI)
with all features (LR_all)
23
Forecast Models Support Vector Regression (SVR) using linear kernel (SVR_lnr)
using Radial Basis Function kernel (SVR_RBF)
24
1 to 15 Minute Forecasting Using Different Models
19
1 to 15 Minute Forecasting Using Different Feature Sets
20
1 to 15 Minute Forecasting Using Different Cloud Tracking methods
29
Example Forecasts on a Cloudy Day
Forecasting of 1-min averaged GHI for 1, 5, 10 and 15 min ahead using persistent model and SVR-RBF, between 10:00 am and 14:00 pm on June 6th, 2013. 30
Summary Novel TSI image processing pipeline • Differentiate low, thick clouds from high, thin
clouds • Tracking a complete cloud
Significant improvement on short term (1-15 minute) solar irradiance forecasting model • Incorporate TSI image features & NWP features • Average of 21% improvement over baseline
model
21
Existing GHI forecast methods Statistical based models Persistent model (PM), benchmark, which directly uses the present irradiance as the prediction. ARMA, ANN, etc., use historical GHI data to train models to predict future irradiances.
Physics based models Numerical Weather Prediction (NWP), utilize meteorological observations and measurements. Cloud imagery based techniques (Deterministic) -- satellite based -- ground based (Total Sky Imager, Whole Sky Imager, etc.) 32
Full Feature Set
33
Linear Chain CRF
G = (V; F;E)
34
1 to 5 Minute Forecasting Using Different Models
18
1 to 5 Minute Forecasting Using Different Feature Sets
MAE for different numbers of states of CRF and HMM
Solar Energy Abundant, clean, and secure renewable resource Intermittent nature of solar irradiance challenges the regulation and maintenance of grids Clouds cause fluctuations in the photovoltaic (PV) output
2
Motivation: Climatology on the East Coast
3
Motivation: Climatology on the East Coast
Reliable 1-15 minute short term irradiance forecasting is essential for grid operators to control ramp events. 3
Short-term Forecasting Workflow Feature Engineering
Cloud Tracking •
Image preprocessing
•
Data calibration
•
Cloud cover classification
•
Feature extraction
•
Correlation analysis with irradiance
•
Feature selection
•
Multi-layer cloud detection
•
Segment identification
•
Cloud tracking
8
Prediction •
Linear & non-linear modeling
•
Model and feature set evaluation and cross validation
Sky Imaging Based Prediction Short term ground-based sensor data collection
High spatial and temporal resolution Able to capture local cloud movement
Pyranometer (irradiance sensor)
TSI (Total Sky Imager)
TSI instrument
Sample TSI image
4
Solar Irradiance Fluctuations
TSI image sequence
Ground irradiance 5
Limitations of Current Methods For Sky Image Processing Thin, high altitude clouds are difficult to detect Unable to differentiate between multiple cloud layers
Block and pixel based tracking are noise prone Accurate forecasts are limited to 5 minutes
6
Novel Solar Irradiance Forecasting Framework Multi-layer cloud detection and tracking • Cloud type classification • Multi-layer detection • Segment identification
Numerical Weather Prediction (NWP) incorporation • Extend the forecasting horizon to 15 minutes
7
Novel Cloud Tracking Pipeline
9
Novel Cloud Tracking Pipeline
Original image
9
Novel Cloud Tracking Pipeline
Image undistortion Shadowband dispatch
10
Novel Cloud Tracking Pipeline
Cloud Cover
11
Novel Cloud Tracking Pipeline
12
Novel Cloud Tracking Pipeline
12
Novel Cloud Tracking Pipeline
Cloud segmentation
13
Novel Cloud Tracking Pipeline
Normalized Cross Correlation Algorithm
17
Novel Cloud Tracking Pipeline
t-1
t
Example cloud segments at time t-1 and t and corresponding predicted motion vectors
14
Method Comparison
Segment based cloud tracking with multi-layer detection
Block based tracking
15
Multi-layer Detection and Segment Identification
20
Feature Engineering: TSI Image Features
16
Feature Engineering: NWP Features Category
17
1
Rain
2
Light Rain
3
Overcast
4
Mostly Cloudy
5
Partly Cloudy
6
Scattered Cloud
7
Fog
8
Clear
9
Sunny
Full Feature Set TSI Feature Set circumsolar RBR, motion vector length/count/sum, cloud cover mean/variance, blue channel max/min, shadowband brightness.
Weather Feature Set category, temperature, humidity, pressure, wind direction, and wind speed.
22
Forecast Models Persistent Model (PM) Linear Regression (LR) • One TSI feature (LR_RBR,GHI) • All features (LR_all)
Support Vector Regression (SVR) • Linear kernel (SVRlnr_all)
• Radial Basis Function kernel (SVRRBF_all)
18
Forecast Models Persistent Model (PM) Linear Regression (LR) with only one TSI feature (LR_RBR,GHI)
with all features (LR_all)
23
Forecast Models Support Vector Regression (SVR) using linear kernel (SVR_lnr)
using Radial Basis Function kernel (SVR_RBF)
24
1 to 15 Minute Forecasting Using Different Models
19
1 to 15 Minute Forecasting Using Different Feature Sets
20
1 to 15 Minute Forecasting Using Different Cloud Tracking methods
29
Example Forecasts on a Cloudy Day
Forecasting of 1-min averaged GHI for 1, 5, 10 and 15 min ahead using persistent model and SVR-RBF, between 10:00 am and 14:00 pm on June 6th, 2013. 30
Summary Novel TSI image processing pipeline • Differentiate low, thick clouds from high, thin
clouds • Tracking a complete cloud
Significant improvement on short term (1-15 minute) solar irradiance forecasting model • Incorporate TSI image features & NWP features • Average of 21% improvement over baseline
model
21
Existing GHI forecast methods Statistical based models Persistent model (PM), benchmark, which directly uses the present irradiance as the prediction. ARMA, ANN, etc., use historical GHI data to train models to predict future irradiances.
Physics based models Numerical Weather Prediction (NWP), utilize meteorological observations and measurements. Cloud imagery based techniques (Deterministic) -- satellite based -- ground based (Total Sky Imager, Whole Sky Imager, etc.) 32
Full Feature Set
33
Linear Chain CRF
G = (V; F;E)
34
1 to 5 Minute Forecasting Using Different Models
18
1 to 5 Minute Forecasting Using Different Feature Sets
MAE for different numbers of states of CRF and HMM
