A Hybrid Spatio-Temporal Prediction Model for Solar Photovoltaic Generation Using Numerical Weather Data and Satellite Images
Abstract
:1. Introduction
Research Framework
2. Methodology
2.1. Numerical Text Data
2.2. Satellite Image Data
2.2.1. Atmospheric Motion Vector (AMV) Image
2.2.2. Cloud Optical Thickness, Aerosol Optical Depth, and Insolation Image
2.3. Correlation Analysis with the Wind Direction of the Cloud and PM
Algorithm 1. Algorithm of discriminant for movement of clouds and PM by wind direction. | |
Denotes ; ; ; ; . | |
1: | Determination of by identification of in the |
2: | |
3: | Determination of the through |
4: | |
5: | Initialize the time step |
6: | Comparison of the amount of cloud in at and the amount of cloud in the |
7: | Comparison of the amount of cloud in and |
8: | For to |
9: | If |
10: | If |
11: | True |
12: | Else |
13: | False |
14: | Else |
15: | If |
16: | True |
17: | Else |
18: | False |
19: |
3. Forecasting Method of Solar PV Generation
3.1. Prediction of Cloud and PM in ROI
3.2. Proposed Models for the Prediction of Solar PV Generation in ROI
3.2.1. Autoregressive Moving Integrated Average Exogenous input (ARIMAX)
3.2.2. Support Vector Regression (SVR)
3.2.3. Artificial Neural Network (ANN)
3.2.4. Deep Neural Network (DNN)
3.3. Analytic Process for Predicting Solar PV Generation
4. Results and Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Date | Temperature [°C] | Precipitation [mm] | Wind Speed [m/s] | Wind Direction [0–360 degree] | Humidity [%] | Amount of Sunshine [hr] | Irradiance [MJ/m] | Cloudiness [0–10 level] | Visibility [10m] | SO2 [ppm] | CO [μg/m2] | O3 [ppm] | NO2 [ppm] | PM10 [μg/m2] | PM2.5 [μg/m2] | PV [kW] |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 January 2015 09:00:00 | −8.4 | 0 | 6.7 | 340 | 56 | 0.8 | 0.21 | 0 | 2000 | 0.006 | 0.5 | 0.017 | 0.012 | 145 | 33 | 60 |
1 January 2015 10:00:00 | −8.1 | 0 | 6.1 | 226 | 54 | 0.0 | 0.67 | 1 | 2000 | 0.006 | 0.5 | 0.019 | 0.01 | 117 | 34 | 374 |
1 January 2015 11:00:00 | −7.6 | 0 | 6.1 | 340 | 53 | 0.0 | 1.1 | 1 | 2000 | 0.006 | 0.6 | 0.019 | 0.01 | 98 | 33 | 638 |
1 January 2015 12:00:00 | −6.9 | 0 | 6.4 | 340 | 52 | 0.0 | 1.41 | 1 | 2000 | 0.006 | 0.6 | 0.021 | 0.01 | 90 | 30 | 784 |
1 January 2015 12:00:00 | −6.1 | 0 | 6.4 | 340 | 53 | 0.0 | 1.53 | 1 | 2000 | 0.006 | 0.6 | 0.023 | 0.01 | 85 | 27 | 842 |
. | . | . | . | . | . | . | . | . | . | . | . | . | . | . | . | . |
. | . | . | . | . | . | . | . | . | . | . | . | . | . | . | . | . |
. | . | . | . | . | . | . | . | . | . | . | . | . | . | . | . | . |
31 December 2015 13:00:00 | 2.9 | 0.0 | 3.3 | 360 | 74 | 1.0 | 1.38 | 4 | 800 | 0.011 | 1.2 | 0.013 | 0.042 | 78 | 66 | 230 |
31 December 2015 14:00:00 | 3.3 | 0.0 | 3.1 | 360 | 75 | 1.0 | 1.24 | 3 | 800 | 0.011 | 1.2 | 0.023 | 0.032 | 100 | 72 | 310 |
31 December 2015 15:00:00 | 3.1 | 0.0 | 3.4 | 340 | 77 | 1.0 | 0.93 | 3 | 800 | 0.011 | 1.2 | 0.024 | 0.034 | 87 | 67 | 439 |
31 December 2015 16:00:00 | 3.3 | 0.0 | 3.2 | 340 | 77 | 1.0 | 0.67 | 2 | 900 | 0.009 | 1.2 | 0.024 | 0.035 | 90 | 68 | 303 |
31 December 2015 17:00:00 | 2.9 | 0.0 | 2.1 | 320 | 78 | 1.0 | 0.26 | 0 | 700 | 0.009 | 1.2 | 0.017 | 0.047 | 83 | 65 | 95 |
Channel | Center Wavelength (μm) | Wavelength Band (μm) | Spatial Resolution (km) |
---|---|---|---|
Visible | 0.67 | 0.55~0.8 | 1 |
Shortwave Infrared | 3.7 | 3.5~4.0 | 4 |
Water vapor | 6.7 | 6.5~7.0 | 4 |
Infrared 1 | 10.8 | 10.3~11.3 | 4 |
Infrared 2 | 12.0 | 11.5~12.5 | 4 |
Date | Wind Direction | Clear | Partly Cloudy | Mostly Cloudy | Cloudy |
---|---|---|---|---|---|
18 January 2015 11:00:00 | W | 0 | 0 | 0 | 0 |
18 January 2015 12:00:00 | W | 1608 | 114 | 6 | 0 |
18 January 2015 13:00:00 | SW | 1147 | 935 | 31 | 0 |
18 January 2015 14:00:00 | W | 0 | 0 | 0 | 0 |
2015-08-02 08:00:00 | SW | 0 | 0 | 0 | 0 |
2015-08-02 09:00:00 | W | 175 | 636 | 165 | 6 |
2015-08-02 10:00:00 | W | 238 | 332 | 222 | 35 |
2015-08-02 11:00:00 | W | 52 | 591 | 364 | 0 |
Date | Wind Direction | Good | Moderate | Unhealthy | Very Unhealthy |
---|---|---|---|---|---|
18 January 2015 11:00:00 | W | 27 | 131 | 119 | 57 |
18 January 2015 12:00:00 | W | 0 | 88 | 204 | 78 |
18 January 2015 13:00:00 | SW | 0 | 4 | 30 | 20 |
18 January 2015 14:00:00 | W | 0 | 6 | 0 | 0 |
2 August 2015 08:00:00 | SW | 6 | 144 | 12 | 18 |
2 August 2015 09:00:00 | W | 0 | 12 | 12 | 9 |
2 August 2015 10:00:00 | W | 0 | 9 | 62 | 39 |
2 August 2015 11:00:00 | W | 0 | 0 | 0 | 18 |
Cloud | Clear | Partly Cloudy | Mostly Cloudy | Cloudy |
---|---|---|---|---|
Accuracy (%) | 75.068 | 75.793 | 84.044 | 91.296 |
PM | Good | Moderate | Unhealthy | Very Unhealthy |
---|---|---|---|---|
Accuracy (%) | 87.489 | 85.585 | 89.665 | 93.382 |
Number of Hidden Layer | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
---|---|---|---|---|---|---|---|
Number of Nodes | 180 | 0.4 | 100 | 0.4 | 100 | 0.4 | 1 |
Activation Function | tanh | Drop out | Relu | Drop Out | Sigmoid | Drop out | Sigmoid |
Group 1 (Numerical Text Weather Data) | Group 2 (Satellite Images) | Group 3 (Mixed, G1 + G2) | |
---|---|---|---|
Common Parameters | Month, Day, Time, PV (previous data) | ||
Case 1 (Cloud) | Temperature, Precipitation, Wind Speed, Wind Direction, Humidity, Amount of Sunshine, Irradiance, Cloudiness, Visibility | Wind Speed, Wind Direction, Clear, Partly cloudy, Mostly cloudy, Cloudy, Irradiance | Temperature, Precipitation, Wind Speed, Wind Direction, Humidity, Amount of Sunshine, Irradiance, Clear, Partly cloudy, Mostly cloudy, Cloudy, Visibility |
Case 2 (PM) | Temperature, Precipitation, Wind Speed, Wind Direction, Humidity, Amount of Sunshine, Irradiance, SO2, CO, O3, NO2, PM10, PM2.5, Visibility | Wind Speed, Wind Direction, PM_Good, PM_Moderate, PM_Unhealthy, PM_Very Unhealthy, Irradiance | Temperature, Precipitation, Wind Speed, Wind Direction, Humidity, Amount of Sunshine, Irradiance, PM_Good, PM_Moderate, PM_Unhealthy, PM_Very Unhealthy, Visibility |
Case 3 (Cloud + PM) | Temperature, Precipitation, Wind Speed, Wind Direction, Humidity, Amount of Sunshine, Irradiance, SO2, CO, O3, NO2, PM10, PM2.5, Cloudiness, Visibility | Wind Speed, Wind Direction, Clear, Partly cloudy, Mostly cloudy, Cloudy, PM_Good, PM_Moderate, PM_Unhealthy, PM_Very Unhealthy, Irradiance | Temperature, Precipitation, Wind Speed, Wind Direction, Humidity, Amount of Sunshine, Irradiance, Clear, Partly cloudy, Mostly cloudy, Cloudy, PM_Good, PM_Moderate, PM_Unhealthy, PM_Very Unhealthy, Visibility |
Output Parameter | PV (One hour ahead) |
Calibration Type | Index | Acceptable Value |
---|---|---|
Monthly | MBE month | ± 5% |
Cv (RMSE) month | 15% | |
Hourly | MBE hour | ±10% |
Cv (RMSE) hour | 30% |
Group | Error | ARIMIX | SVR_RBF | SVR_Linear | SVR_Poly | ANN | DNN |
---|---|---|---|---|---|---|---|
Group 1 (Numerical Text Data) | MAE | 81.261 | 90.686 | 81.112 | 102.638 | 122.285 | 72.554 |
RMSE | 101.768 | 111.289 | 101.63 | 128.766 | 149.354 | 98.519 | |
SMAPE | 17.845 | 17.114 | 17.91 | 21.16 | 19.706 | 14.197 | |
MBE | 0.017 | 0.394 | 0.526 | 0.504 | 23.958 | 0.956 | |
Cv | 22.593 | 24.706 | 22.562 | 28.587 | 33.157 | 21.872 | |
Group 2 (Satellite Images) | MAE | 79.186 | 101.178 | 81.147 | 180.107 | 92.738 | 73.496 |
RMSE | 101.712 | 124.462 | 103.107 | 232.476 | 117.635 | 95.934 | |
SMAPE | 15.635 | 18.178 | 16.51 | 28.447 | 16.786 | 14.484 | |
MBE | 0.093 | 0.052 | 0.455 | 12.666 | 13.171 | 3.142 | |
Cv | 22.58 | 27.631 | 22.89 | 51.61 | 26.115 | 21.298 | |
Group 3 (Mixed, G1 + G2) | MAE | 78.143 | 88.868 | 78.884 | 85.339 | 92.323 | 67.531 |
RMSE | 101.496 | 109.402 | 100.88 | 116.526 | 114.274 | 93.642 | |
SMAPE | 17.792 | 16.841 | 17.613 | 16.58 | 17.857 | 15.039 | |
MBE | 0.06 | 0.592 | 0.14 | 3.89 | 12.21 | 6.013 | |
Cv | 22.532 | 24.288 | 22.396 | 25.869 | 25.369 | 20.789 |
Group | Error | ARIMIX | SVR_RBF | SVR_Linear | SVR_Poly | ANN | DNN |
---|---|---|---|---|---|---|---|
Group 1 (Numerical Text Data) | MAE | 79.753 | 94.812 | 83.152 | 76.808 | 86.44 | 75.432 |
RMSE | 100.344 | 115.05 | 104.319 | 97.71 | 106.326 | 95.944 | |
SMAPE | 17.377 | 17.417 | 18 | 16.241 | 19.124 | 14.439 | |
MBE | 0.265 | 1.8 | 1.301 | 1.585 | 10.807 | 3.661 | |
Cv | 22.277 | 25.541 | 23.159 | 21.692 | 23.605 | 21.3 | |
Group 2 (Satellite Images) | MAE | 78.33 | 96.5 | 78.103 | 190.973 | 82.34 | 75.727 |
RMSE | 102.158 | 118.679 | 99.672 | 255.323 | 105.741 | 103.094 | |
SMAPE | 16.683 | 17.757 | 17.017 | 28.403 | 15.489 | 15.202 | |
MBE | 0.106 | 1.665 | 0.009 | 18.586 | 3.51 | 0.303 | |
Cv | 22.679 | 26.347 | 22.127 | 56.682 | 23.475 | 22.887 | |
Group 3 (Mixed, G1 + G2) | MAE | 78.895 | 90.663 | 80.282 | 77.882 | 93.974 | 71.295 |
RMSE | 102.432 | 110.424 | 102.413 | 104.554 | 115.703 | 96.355 | |
SMAPE | 17.631 | 16.957 | 18.229 | 15.217 | 17.829 | 14.257 | |
MBE | 0.195 | 0.493 | 0.482 | 1.207 | 12.481 | 2.156 | |
Cv | 22.74 | 24.514 | 22.736 | 23.211 | 25.686 | 21.391 |
Group | Error | ARIMIX | SVR_RBF | SVR_Linear | SVR_Poly | ANN | DNN |
---|---|---|---|---|---|---|---|
Group 1 (Numerical Text Data) | MAE | 196.129 | 90.096 | 81.154 | 79.128 | 95.97 | 79 |
RMSE | 235.048 | 111.22 | 102.268 | 100.929 | 119.607 | 104.059 | |
SMAPE | 31.626 | 17.041 | 17.596 | 16.49 | 20.808 | 15.123 | |
MBE | 11.816 | 2.076 | 0.171 | 1.219 | 6.619 | 8.376 | |
Cv | 52.181 | 24.691 | 22.704 | 22.406 | 26.553 | 23.101 | |
Group 2 (Satellite Images) | MAE | 78.444 | 93.155 | 81.448 | 126.299 | 82.911 | 77.713 |
RMSE | 100.831 | 115.8223 | 102.696 | 169.343 | 107.843 | 102.005 | |
SMAPE | 15.783 | 17.539 | 17.04 | 21.657 | 16.08 | 14.815 | |
MBE | 0.146 | 0.883 | 0.744 | 4.915 | 8.321 | 5.343 | |
Cv | 22.385 | 25.713 | 22.799 | 37.604 | 23.941 | 22.646 | |
Group 3 (Mixed, G1 + G2) | MAE | 77.554 | 87.587 | 80.123 | 83.163 | 101.233 | 71.532 |
RMSE | 101.943 | 108.229 | 102.889 | 107.278 | 122.508 | 92.938 | |
SMAPE | 17.669 | 16.66 | 17.91 | 17.373 | 18.776 | 14.107 | |
MBE | 0.342 | 0.498 | 0.039 | 0.134 | 16.572 | 4.986 | |
Cv | 22.632 | 24.027 | 22.842 | 23.816 | 27.197 | 20.633 |
Case 1 (Cloud Only) | Case 2 (PM Only) | Case 3 (Using Together) | |
---|---|---|---|
Group 1 (KMA) | |||
Group 2 (Satellite) | |||
Group 3 (Mix) |
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Kim, B.; Suh, D. A Hybrid Spatio-Temporal Prediction Model for Solar Photovoltaic Generation Using Numerical Weather Data and Satellite Images. Remote Sens. 2020, 12, 3706. https://doi.org/10.3390/rs12223706
Kim B, Suh D. A Hybrid Spatio-Temporal Prediction Model for Solar Photovoltaic Generation Using Numerical Weather Data and Satellite Images. Remote Sensing. 2020; 12(22):3706. https://doi.org/10.3390/rs12223706
Chicago/Turabian StyleKim, Bowoo, and Dongjun Suh. 2020. "A Hybrid Spatio-Temporal Prediction Model for Solar Photovoltaic Generation Using Numerical Weather Data and Satellite Images" Remote Sensing 12, no. 22: 3706. https://doi.org/10.3390/rs12223706
APA StyleKim, B., & Suh, D. (2020). A Hybrid Spatio-Temporal Prediction Model for Solar Photovoltaic Generation Using Numerical Weather Data and Satellite Images. Remote Sensing, 12(22), 3706. https://doi.org/10.3390/rs12223706