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Predicting photovoltaic power production using high-uncertainty weather forecasts

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  • Polasek, Tomas
  • Čadík, Martin

Abstract

A growing interest in renewable power increases its impact on the energy grid, posing significant challenges to reliability, stability, and planning. Weather-based prediction methods help relieve these issues. However, their real-world accuracy is limited by weather forecast errors. To help resolve this limitation, we introduce the SolarPredictor model. Publicly available weather forecasts are used to predict solar power production by a target photovoltaic power plant. To achieve high prediction accuracy, the model is trained on genuine weather forecasts, including errors and mispredictions. Further, we introduce the SolarDB dataset, comprising one year of power production data for 16 power plants. The dataset includes hourly weather forecasts with seven days of history, allowing our model to anticipate errors in the meteorological features. The prediction accuracy is evaluated on a wide range of weather forecast ages, accurately reflecting real-world performance. The SolarPredictor model is compared against 17 techniques, reaching an average RRMSE of 6.15 for 1-day, 8.54 for 3-day, and 8.89 for 7-day predictions on the SolarDB dataset. Finally, we analyze the effects of weather forecast uncertainty on prediction accuracy, showing there is at least a 23% performance gap compared to using zero-error weather. Data and additional resources are available at cphoto.fit.vutbr.cz/solar.

Suggested Citation

  • Polasek, Tomas & Čadík, Martin, 2023. "Predicting photovoltaic power production using high-uncertainty weather forecasts," Applied Energy, Elsevier, vol. 339(C).
    • Handle: RePEc:eee:appene:v:339:y:2023:i:c:s0306261923003537
    DOI: 10.1016/j.apenergy.2023.120989
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    2. Wang, Yongli & Wang, Huan & Meng, Xiao & Dong, Huanran & Chen, Xin & Xiang, Hao & Xing, Juntai, 2023. "Considering the dual endogenous-exogenous uncertainty integrated energy multiple load short-term forecast," Energy, Elsevier, vol. 285(C).
    3. Pavol Belany & Peter Hrabovsky & Zuzana Florkova, 2024. "Probability Calculation for Utilization of Photovoltaic Energy in Electric Vehicle Charging Stations," Energies, MDPI, vol. 17(5), pages 1-34, February.
    4. Taorong Jia & Guoqing Yang & Lixiao Yao, 2024. "The Low-Carbon Path of Active Distribution Networks: A Two-Stage Model from Day-Ahead Reconfiguration to Real-Time Optimization," Energies, MDPI, vol. 17(19), pages 1-20, October.
    5. Hu, Zehuan & Gao, Yuan & Ji, Siyu & Mae, Masayuki & Imaizumi, Taiji, 2024. "Improved multistep ahead photovoltaic power prediction model based on LSTM and self-attention with weather forecast data," Applied Energy, Elsevier, vol. 359(C).

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