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Enhanced Ultra-Short-Term PV Forecasting Using Sky Imagers: Integrating MCR and Cloud Cover Estimation

Author

Listed:
  • Weixiong Wu

    (Mamaya Photovoltaic Branch of Guizhou Beipanjiang Electric Power Co., Ltd., Guiyang 550081, China)

  • Rui Gao

    (Mamaya Photovoltaic Branch of Guizhou Beipanjiang Electric Power Co., Ltd., Guiyang 550081, China)

  • Peng Wu

    (Mamaya Photovoltaic Branch of Guizhou Beipanjiang Electric Power Co., Ltd., Guiyang 550081, China)

  • Chen Yuan

    (Guizhou New Meteorological Technology Co., Ltd., Guiyang 550081, China)

  • Xiaoling Xia

    (Guizhou New Meteorological Technology Co., Ltd., Guiyang 550081, China)

  • Renfeng Liu

    (School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China)

  • Yifei Wang

    (School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China)

Abstract

Accurate photovoltaic (PV) power forecasting is crucial for stable grid integration, particularly under rapidly changing weather conditions. This study presents an ultra-short-term forecasting model that integrates sky imager data and meteorological radar data, achieving significant improvements in forecasting accuracy. By dynamically tracking cloud movement and estimating cloud coverage, the model enhances performance under both clear and cloudy conditions. Over an 8-day evaluation period, the average forecasting accuracy improved from 67.26% to 77.47% (+15%), with MSE reduced by 39.2% (from 481.5 to 292.6), R 2 increased from 0.724 to 0.855, NSE improved from 0.725 to 0.851, and Theil’s U reduced from 0.42 to 0.32. Notable improvements were observed during abrupt weather transitions, particularly on 1 July and 3 July, where the combination of MCR and sky imager data demonstrated superior adaptability. This integrated approach provides a robust foundation for advancing ultra-short-term PV power forecasting.

Suggested Citation

  • Weixiong Wu & Rui Gao & Peng Wu & Chen Yuan & Xiaoling Xia & Renfeng Liu & Yifei Wang, 2024. "Enhanced Ultra-Short-Term PV Forecasting Using Sky Imagers: Integrating MCR and Cloud Cover Estimation," Energies, MDPI, vol. 18(1), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:18:y:2024:i:1:p:28-:d:1553169
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    References listed on IDEAS

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    1. Trigo-González, Mauricio & Cortés-Carmona, Marcelo & Marzo, Aitor & Alonso-Montesinos, Joaquín & Martínez-Durbán, Mercedes & López, Gabriel & Portillo, Carlos & Batlles, Francisco Javier, 2023. "Photovoltaic power electricity generation nowcasting combining sky camera images and learning supervised algorithms in the Southern Spain," Renewable Energy, Elsevier, vol. 206(C), pages 251-262.
    2. Zhu, Nanyang & Wang, Ying & Yuan, Kun & Yan, Jiahao & Li, Yaping & Zhang, Kaifeng, 2024. "GGNet: A novel graph structure for power forecasting in renewable power plants considering temporal lead-lag correlations," Applied Energy, Elsevier, vol. 364(C).
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