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Regional Solar Irradiance Forecast for Kanto Region by Support Vector Regression Using Forecast of Meso-Ensemble Prediction System

Author

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  • Takahiro Takamatsu

    (National Institute of Advanced Industrial Science and Technology, Fukushima 963-0298, Japan)

  • Hideaki Ohtake

    (National Institute of Advanced Industrial Science and Technology, Fukushima 963-0298, Japan
    Meteorological Research Institute, Ibaraki 305-0052, Japan)

  • Takashi Oozeki

    (National Institute of Advanced Industrial Science and Technology, Fukushima 963-0298, Japan)

  • Tosiyuki Nakaegawa

    (Meteorological Research Institute, Ibaraki 305-0052, Japan)

  • Yuki Honda

    (Japan Meteorological Agency, Tokyo 105-8431, Japan)

  • Masahiro Kazumori

    (Japan Meteorological Agency, Tokyo 105-8431, Japan)

Abstract

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.

Suggested Citation

  • Takahiro Takamatsu & Hideaki Ohtake & Takashi Oozeki & Tosiyuki Nakaegawa & Yuki Honda & Masahiro Kazumori, 2021. "Regional Solar Irradiance Forecast for Kanto Region by Support Vector Regression Using Forecast of Meso-Ensemble Prediction System," Energies, MDPI, vol. 14(11), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3245-:d:567341
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    References listed on IDEAS

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    Cited by:

    1. Dou, Weijing & Wang, Kai & Shan, Shuo & Li, Chenxi & Wang, Yiye & Zhang, Kanjian & Wei, Haikun & Sreeram, Victor, 2024. "Day-ahead Numerical Weather Prediction solar irradiance correction using a clustering method based on weather conditions," Applied Energy, Elsevier, vol. 365(C).
    2. Abdel-Rahman Hedar & Majid Almaraashi & Alaa E. Abdel-Hakim & Mahmoud Abdulrahim, 2021. "Hybrid Machine Learning for Solar Radiation Prediction in Reduced Feature Spaces," Energies, MDPI, vol. 14(23), pages 1-29, November.

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