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A Two-Step Approach to Solar Power Generation Prediction Based on Weather Data Using Machine Learning

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  • Seul-Gi Kim

    (Department of Industrial & Management Systems Engineering, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin-si, Gyenggi-do 17104, Korea)

  • Jae-Yoon Jung

    (Department of Industrial & Management Systems Engineering, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin-si, Gyenggi-do 17104, Korea)

  • Min Kyu Sim

    (Department of Industrial & Management Systems Engineering, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin-si, Gyenggi-do 17104, Korea)

Abstract

Photovoltaic systems have become an important source of renewable energy generation. Because solar power generation is intrinsically highly dependent on weather fluctuations, predicting power generation using weather information has several economic benefits, including reliable operation planning and proactive power trading. This study builds a model that predicts the amounts of solar power generation using weather information provided by weather agencies. This study proposes a two-step modeling process that connects unannounced weather variables with announced weather forecasts. The empirical results show that this approach improves a base approach by wide margins, regardless of types of applied machine learning algorithms. The results also show that the random forest regression algorithm performs the best for this problem, achieving an R-squared value of 70.5% in the test data. The intermediate modeling process creates four variables, which are ranked with high importance in the post-analysis. The constructed model performs realistic one-day ahead predictions.

Suggested Citation

  • Seul-Gi Kim & Jae-Yoon Jung & Min Kyu Sim, 2019. "A Two-Step Approach to Solar Power Generation Prediction Based on Weather Data Using Machine Learning," Sustainability, MDPI, vol. 11(5), pages 1-16, March.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:5:p:1501-:d:213233
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    References listed on IDEAS

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    11. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).
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