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Statistical Upscaling Prediction Method of Photovoltaic Cluster Power Considering the Influence of Sand and Dust Weather

Author

Listed:
  • Hu Yang

    (College of Science, Beijing Forestry University, Beijing 100083, China)

  • Chang Liu

    (College of Science, Beijing Forestry University, Beijing 100083, China)

  • Zhao Wang

    (National Key Laboratory of Renewable Energy Grid-Integration, China Electric Power Research Institute, Beijing 100192, China)

  • Hongqing Wang

    (College of Science, Beijing Forestry University, Beijing 100083, China)

Abstract

Photovoltaic (PV) clusters in deserts such as the Gobi and other regions are frequently affected by sand and dust, which causes great deviation in power prediction and seriously threatens the safe operation of new power systems. For this reason, this paper proposes a short-term cluster PV power prediction method based on statistical upscaling, considering the effect of sand and dust. Firstly, the sand and dust events are identified, and then time series generative adversarial networks (TimeGANs) are used to solve the problem of small sample scarcity in sand and dust and construct a power correction model for sand and dust scenes. Secondly, for different weather scenes, a combination of conventional prediction and correction prediction is used to solve the problem of large differences in the predictability of a single model. Finally, a statistical upscaling method is utilized to calculate the cluster prediction power to solve the prediction difficulties of large-scale newly installed PV field stations. Through a case study and comparison with other models and methods, the cluster prediction method established in this paper effectively improves the prediction accuracy of the power of large-scale PV clusters affected by sand and dust, with the RMSE reduced by 8.28%.

Suggested Citation

  • Hu Yang & Chang Liu & Zhao Wang & Hongqing Wang, 2025. "Statistical Upscaling Prediction Method of Photovoltaic Cluster Power Considering the Influence of Sand and Dust Weather," Energies, MDPI, vol. 18(4), pages 1-20, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:4:p:784-:d:1586149
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    References listed on IDEAS

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    1. Weiliang Liu & Changliang Liu & Yongjun Lin & Liangyu Ma & Feng Xiong & Jintuo Li, 2018. "Ultra-Short-Term Forecast of Photovoltaic Output Power under Fog and Haze Weather," Energies, MDPI, vol. 11(3), pages 1-22, February.
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