A HIGH-RESOLUTION SHORT-TERM POWER GENERATION FORECASTING METHOD FOR PHOTOVOLTAIC

Zhang Fei; Liao Qianguo; Li Xingcai; Hu Weiwei; Bo Tianli; Ma Xin

doi:10.19912/j.0254-0096.tynxb.2024-1190

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (11) : 235-243. DOI: 10.19912/j.0254-0096.tynxb.2024-1190

A HIGH-RESOLUTION SHORT-TERM POWER GENERATION FORECASTING METHOD FOR PHOTOVOLTAIC

Zhang Fei¹, Liao Qianguo¹, Li Xingcai^1,2, Hu Weiwei¹, Bo Tianli¹, Ma Xin¹

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Abstract

This study introduces a novel hybrid forecasting model, which uniquely employs the sparrow search algorithm to optimize the parameters of variational model Decomposition. Subsequently, the model utilizes variational mode decomposition and complete ensemble empirical mode decomposition with adaptive noise for dual rounds of time series decomposition related to solar power, followed by the application of convolutional neural networks and long short-term memory with attention mechanisms to train and forecast the decomposed series data. The proposed model undergoes validation using real-world data across various weather conditions and resolutions. The results consistently demonstrate the superior predictive performance of the new method across different time resolutions （2 or 10 minutes）, forecast ranges （1, 3, or 5 days）, and diverse weather conditions, with a remarkable correlation coefficient of the predicted values exceeding 0.97.

Key words

photovoltaic power generation / forecasting / variational mode decomposition / hybrid models / complete ensemble empirical mode decomposition with adaptive noise

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Zhang Fei, Liao Qianguo, Li Xingcai, Hu Weiwei, Bo Tianli, Ma Xin. A HIGH-RESOLUTION SHORT-TERM POWER GENERATION FORECASTING METHOD FOR PHOTOVOLTAIC[J]. Acta Energiae Solaris Sinica. 2025, 46(11): 235-243 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1190

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