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Regional Photovoltaic Power Forecasting Using Vector Autoregression Model in South Korea

Author

Listed:
  • A-Hyun Jung

    (Department of Statistics, Dongguk University, Seoul 04620, Korea)

  • Dong-Hyun Lee

    (Department of Statistics, Dongguk University, Seoul 04620, Korea)

  • Jin-Young Kim

    (New and Renewable Energy Resource Map Laboratory, Korea Institute of Energy Research, Daejeon 34129, Korea)

  • Chang Ki Kim

    (New and Renewable Energy Resource Map Laboratory, Korea Institute of Energy Research, Daejeon 34129, Korea)

  • Hyun-Goo Kim

    (New and Renewable Energy Resource Map Laboratory, Korea Institute of Energy Research, Daejeon 34129, Korea)

  • Yung-Seop Lee

    (Department of Statistics, Dongguk University, Seoul 04620, Korea)

Abstract

Renewable energy forecasting is a key for efficient resource use in terms of power generation and safe grid control. In this study, we investigated a short-term statistical forecasting model with 1 to 3 h horizons using photovoltaic operation data from 215 power plants throughout South Korea. A vector autoregression (VAR) model-based regional photovoltaic power forecasting system is proposed for seven clusters of power plants in South Korea. This method showed better predictability than the autoregressive integrated moving average (ARIMA) model. The normalized root-mean-square errors of hourly photovoltaic generation predictions obtained from VAR (ARIMA) were 8.5–10.9% (9.8–13.0%) and 18.5–22.8% (21.3–26.3%) for 1 h and 3 h horizon, respectively, at 215 power plants. The coefficient of determination, R 2 was higher for VAR, at 4–5%, than ARIMA. The VAR model had greater accuracy than ARIMA. This will be useful for economical and efficient grid management.

Suggested Citation

  • A-Hyun Jung & Dong-Hyun Lee & Jin-Young Kim & Chang Ki Kim & Hyun-Goo Kim & Yung-Seop Lee, 2022. "Regional Photovoltaic Power Forecasting Using Vector Autoregression Model in South Korea," Energies, MDPI, vol. 15(21), pages 1-13, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:7853-:d:950951
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    References listed on IDEAS

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