Regional Solar Irradiance Forecast for Kanto Region by Support Vector Regression Using Forecast of Meso-Ensemble Prediction System
From the perspective of stable operation of the power transmission system, the transmission system operators (TSO) needs to procure reserve adjustment power at the stage of the previous day based on solar power forecast information from global horizontal irradiance (GHI). Because the reserve adjustm...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/11/3245 |
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| author | Takahiro Takamatsu Hideaki Ohtake Takashi Oozeki Tosiyuki Nakaegawa Yuki Honda Masahiro Kazumori |
| author_facet | Takahiro Takamatsu Hideaki Ohtake Takashi Oozeki Tosiyuki Nakaegawa Yuki Honda Masahiro Kazumori |
| author_sort | Takahiro Takamatsu |
| collection | DOAJ |
| description | From the perspective of stable operation of the power transmission system, the transmission system operators (TSO) needs to procure reserve adjustment power at the stage of the previous day based on solar power forecast information from global horizontal irradiance (GHI). Because the reserve adjustment power is determined based on information on major outliers in past forecasts, reducing the maximum forecast error in addition to improving the average forecast accuracy is extremely important from the perspective of grid operation. In the past, researchers have proposed various methods combining the numerical weather prediction (NWP) and machine learning techniques for the one day-ahead solar power forecasting, but the accuracy of NWP has been a bottleneck issue. In recent years, the development of the ensemble prediction system (EPS) forecasts based on probabilistic approaches has been promoted to improve the accuracy of NWP, and in Japan, EPS forecasts in the mesoscale domain, called mesoscale ensemble prediction system (MEPS), have been distributed by the Japan Meteorological Agency (JMA). The use of EPS as a machine learning model is expected to improve the maximum forecast error, as well as the accuracy, since the predictor can utilize various weather scenarios as information. The purpose of this study is to examine the effect of EPS on the GHI prediction and the structure of the machine learning model that can effectively use EPS. In this study, we constructed the support vector regression (SVR)-based predictors with multiple network configurations using MEPS as input and evaluated the forecast error of the Kanto region GHI by each model. Through the comparison of the prediction results, it was shown that the machine learning model can achieve average accuracy improvement while reducing the maximum prediction error by MEPS, and knowledge was obtained on how to effectively provide EPS information to the predictor. In addition, machine learning was found to be useful in improving the systematic error of MEPS. |
| first_indexed | 2024-03-10T10:48:07Z |
| format | Article |
| id | doaj.art-bfb56754043241eea945c7881179b571 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T10:48:07Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-bfb56754043241eea945c7881179b5712023-11-21T22:29:22ZengMDPI AGEnergies1996-10732021-06-011411324510.3390/en14113245Regional Solar Irradiance Forecast for Kanto Region by Support Vector Regression Using Forecast of Meso-Ensemble Prediction SystemTakahiro Takamatsu0Hideaki Ohtake1Takashi Oozeki2Tosiyuki Nakaegawa3Yuki Honda4Masahiro Kazumori5National Institute of Advanced Industrial Science and Technology, Fukushima 963-0298, JapanNational Institute of Advanced Industrial Science and Technology, Fukushima 963-0298, JapanNational Institute of Advanced Industrial Science and Technology, Fukushima 963-0298, JapanMeteorological Research Institute, Ibaraki 305-0052, JapanJapan Meteorological Agency, Tokyo 105-8431, JapanJapan Meteorological Agency, Tokyo 105-8431, JapanFrom the perspective of stable operation of the power transmission system, the transmission system operators (TSO) needs to procure reserve adjustment power at the stage of the previous day based on solar power forecast information from global horizontal irradiance (GHI). Because the reserve adjustment power is determined based on information on major outliers in past forecasts, reducing the maximum forecast error in addition to improving the average forecast accuracy is extremely important from the perspective of grid operation. In the past, researchers have proposed various methods combining the numerical weather prediction (NWP) and machine learning techniques for the one day-ahead solar power forecasting, but the accuracy of NWP has been a bottleneck issue. In recent years, the development of the ensemble prediction system (EPS) forecasts based on probabilistic approaches has been promoted to improve the accuracy of NWP, and in Japan, EPS forecasts in the mesoscale domain, called mesoscale ensemble prediction system (MEPS), have been distributed by the Japan Meteorological Agency (JMA). The use of EPS as a machine learning model is expected to improve the maximum forecast error, as well as the accuracy, since the predictor can utilize various weather scenarios as information. The purpose of this study is to examine the effect of EPS on the GHI prediction and the structure of the machine learning model that can effectively use EPS. In this study, we constructed the support vector regression (SVR)-based predictors with multiple network configurations using MEPS as input and evaluated the forecast error of the Kanto region GHI by each model. Through the comparison of the prediction results, it was shown that the machine learning model can achieve average accuracy improvement while reducing the maximum prediction error by MEPS, and knowledge was obtained on how to effectively provide EPS information to the predictor. In addition, machine learning was found to be useful in improving the systematic error of MEPS.https://www.mdpi.com/1996-1073/14/11/3245meso-ensemble prediction systemsupport vector regressionsolar irradiance forecastensemble learning |
| spellingShingle | Takahiro Takamatsu Hideaki Ohtake Takashi Oozeki Tosiyuki Nakaegawa Yuki Honda Masahiro Kazumori Regional Solar Irradiance Forecast for Kanto Region by Support Vector Regression Using Forecast of Meso-Ensemble Prediction System Energies meso-ensemble prediction system support vector regression solar irradiance forecast ensemble learning |
| title | Regional Solar Irradiance Forecast for Kanto Region by Support Vector Regression Using Forecast of Meso-Ensemble Prediction System |
| title_full | Regional Solar Irradiance Forecast for Kanto Region by Support Vector Regression Using Forecast of Meso-Ensemble Prediction System |
| title_fullStr | Regional Solar Irradiance Forecast for Kanto Region by Support Vector Regression Using Forecast of Meso-Ensemble Prediction System |
| title_full_unstemmed | Regional Solar Irradiance Forecast for Kanto Region by Support Vector Regression Using Forecast of Meso-Ensemble Prediction System |
| title_short | Regional Solar Irradiance Forecast for Kanto Region by Support Vector Regression Using Forecast of Meso-Ensemble Prediction System |
| title_sort | regional solar irradiance forecast for kanto region by support vector regression using forecast of meso ensemble prediction system |
| topic | meso-ensemble prediction system support vector regression solar irradiance forecast ensemble learning |
| url | https://www.mdpi.com/1996-1073/14/11/3245 |
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