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Accurate nowcasting of cloud cover at solar photovoltaic plants using geostationary satellite images

Author

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  • Pan Xia

    (Sun Yat-sen University and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai))

  • Lu Zhang

    (National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration)

  • Min Min

    (Sun Yat-sen University and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai))

  • Jun Li

    (National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration)

  • Yun Wang

    (China General Nuclear Power Group (CGN) Wind Energy Co Ltd)

  • Yu Yu

    (National Meteorological Information Centre, China Meteorological Administration)

  • Shengjie Jia

    (Beijing Keytec Technology Co., Ltd.)

Abstract

Accurate nowcasting for cloud fraction is still intractable challenge for stable solar photovoltaic electricity generation. By combining continuous radiance images measured by geostationary satellite and an advanced recurrent neural network, we develop a nowcasting algorithm for predicting cloud fraction at the leading time of 0–4 h at photovoltaic plants. Based on this algorithm, a cyclically updated prediction system is also established and tested at five photovoltaic plants and several stations with cloud fraction observations in China. The results demonstrate that the cloud fraction nowcasting is efficient, high quality and adaptable. Particularly, it shows an excellent forecast performance within the first 2-hour leading time, with an average correlation coefficient close to 0.8 between the predicted clear sky ratio and actual power generation at photovoltaic plants. Our findings highlight the benefits and potential of this technique to improve the competitiveness of solar photovoltaic energy in electricity market.

Suggested Citation

  • Pan Xia & Lu Zhang & Min Min & Jun Li & Yun Wang & Yu Yu & Shengjie Jia, 2024. "Accurate nowcasting of cloud cover at solar photovoltaic plants using geostationary satellite images," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-023-44666-1
    DOI: 10.1038/s41467-023-44666-1
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    References listed on IDEAS

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    Cited by:

    1. Shi, Chaojun & Su, Zibo & Zhang, Ke & Xie, Xiongbin & Zhang, Xiaoyun, 2024. "CloudSwinNet: A hybrid CNN-transformer framework for ground-based cloud images fine-grained segmentation," Energy, Elsevier, vol. 309(C).
    2. Kim, Jimin & Obregon, Josue & Park, Hoonseok & Jung, Jae-Yoon, 2024. "Multi-step photovoltaic power forecasting using transformer and recurrent neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 200(C).

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