STUDY ON ULTRA-SHORT-TERM PHOTOVOLTAIC POWER STATION IRRADIANCE FORECASTING BASED ON IMPROVED BLOCK-LADDER METHOD

Dong Haomiao; Zhang Yao; Lin Fan; Li Jiaxing; Liao Jian; Zhang Beixi

doi:10.19912/j.0254-0096.tynxb.2025-0775

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (9) : 341-351. DOI: 10.19912/j.0254-0096.tynxb.2025-0775

STUDY ON ULTRA-SHORT-TERM PHOTOVOLTAIC POWER STATION IRRADIANCE FORECASTING BASED ON IMPROVED BLOCK-LADDER METHOD

Dong Haomiao, Zhang Yao, Lin Fan, Li Jiaxing, Liao Jian, Zhang Beixi

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Abstract

This paper proposes an ultra-short-term photovoltaic power station irradiance forecasting method based on an improved block-ladder method. Firstly, the dataset is preprocessed, including fisheye distortion correction on ground-based sky images. A clear sky model is established for the irradiance data to generate clear sky indices. Next, clouds in the ground-based sky images are identified using an adaptive threshold calculation method based on minimum cross-entropy, and cloud motion is predicted with a similarity-weighted cloud motion vector calculation method. Finally, ultra-short-term photovoltaic irradiance forecasting is performed using the improved block-ladder method. Experimental results on the UCSD-Folsom dataset show that this method improves forecasting accuracy and extends the forecasting period of the traditional block-ladder method.

Key words

photovoltaic power / solar irradiance / deep learning / ground-based sky image / cloud motion vector / block-ladder method

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Dong Haomiao, Zhang Yao, Lin Fan, Li Jiaxing, Liao Jian, Zhang Beixi. STUDY ON ULTRA-SHORT-TERM PHOTOVOLTAIC POWER STATION IRRADIANCE FORECASTING BASED ON IMPROVED BLOCK-LADDER METHOD[J]. Acta Energiae Solaris Sinica. 2026, 47(9): 341-351 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0775

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