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Recent advances in intra-hour solar forecasting: A review of ground-based sky image methods

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  • Lin, Fan
  • Zhang, Yao
  • Wang, Jianxue

Abstract

As the penetration of solar energy generation into power systems keeps rising, intra-hour solar forecasting (IHSF) is becoming increasingly important for the secure and economical operation of a power system. One major difficulty in providing very accurate IHSF emanates from rapid cloud changes in the sky. The ground-based sky image (GSI) provides the intuitive information of intra-hour cloud changes and has thus been widely utilized in studies on IHSF. This paper presents a systematic review of the state-of-the-art of ground-based sky image-based intra-hour solar forecasting (GSI-IHSF). To our knowledge, we first propose a generic framework of GSI-IHSF consisting of four modules, i.e., sky image acquisition, sky image preprocessing, cloud forecasting, and solar forecasting. Then, as for each module, this paper introduces its core function, shows the major challenges, briefly reviews several extensively used techniques, summarizing research trends. Finally, this paper offers a prospect of GSI-IHSF research, discusses recent advances that demonstrate the potential for a great improvement in forecast accuracy, pointing out some new requirements and challenges that should be further investigated in the future.

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  • Lin, Fan & Zhang, Yao & Wang, Jianxue, 2023. "Recent advances in intra-hour solar forecasting: A review of ground-based sky image methods," International Journal of Forecasting, Elsevier, vol. 39(1), pages 244-265.
    • Handle: RePEc:eee:intfor:v:39:y:2023:i:1:p:244-265
    DOI: 10.1016/j.ijforecast.2021.11.002
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    References listed on IDEAS

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    Cited by:

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    2. Markos A. Kousounadis-Knousen & Ioannis K. Bazionis & Athina P. Georgilaki & Francky Catthoor & Pavlos S. Georgilakis, 2023. "A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models," Energies, MDPI, vol. 16(15), pages 1-29, July.
    3. M. Osama abed el-Raouf & Soad A. A. Mageed & M. M. Salama & Mohamed I. Mosaad & H. A. AbdelHadi, 2023. "Performance Enhancement of Grid-Connected Renewable Energy Systems Using UPFC," Energies, MDPI, vol. 16(11), pages 1-22, May.
    4. Kajal Lahiri & Cheng Yang, 2023. "ROC and PRC Approaches to Evaluate Recession Forecasts," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(2), pages 119-148, September.
    5. Xie, Qiyue & Ma, Lin & Liu, Yao & Fu, Qiang & Shen, Zhongli & Wang, Xiaoli, 2023. "An improved SSA-BiLSTM-based short-term irradiance prediction model via sky images feature extraction," Renewable Energy, Elsevier, vol. 219(P2).
    6. Sebastián Vázquez-Ramírez & Miguel Torres-Ruiz & Rolando Quintero & Kwok Tai Chui & Carlos Guzmán Sánchez-Mejorada, 2023. "An Analysis of Climate Change Based on Machine Learning and an Endoreversible Model," Mathematics, MDPI, vol. 11(14), pages 1-26, July.

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