SHORT-TERM PV POWER PREDICTION CONSIDERING DATA DECOMPOSITION AND GISH-BITCN-MHSA

Liu Haipeng; He Yanping; Jin Huaiping; Fang Qiwen; Wu Hong

doi:10.19912/j.0254-0096.tynxb.2024-0603

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (8) : 430-438. DOI: 10.19912/j.0254-0096.tynxb.2024-0603

SHORT-TERM PV POWER PREDICTION CONSIDERING DATA DECOMPOSITION AND GISH-BITCN-MHSA

Liu Haipeng¹, He Yanping¹, Jin Huaiping¹, Fang Qiwen², Wu Hong¹

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Abstract

In order to effectively cope with the challenge of grid in stability caused by output power fluctuations of distributed PV power plants, a new short-term PV power prediction framework is proposed in this study. Firstly, the raw PV power data are decomposed into multiple modal components using the optimal variational modal decomposition （OVMD） technique, which is fused with relevant features to generate a series of subsequences. Then, a bidirectional time convolutional network （Gish-BiTCN） incorporating the Gish activation function is used to predict each subsequence, and the mechanism of multiple heads’attention （MHSA） is introduced to enable the model to pay more attention to and capture the time-related features. Finally, the final prediction results are obtained by reconstructing the predicted values of all subsequences. Its superiority in PV power generation prediction is verified through experiments.

Key words

photovoltaic power prediction / vriational modal decomposition / bidirectional time convolutional network / multi-head self-attention mechanism / whale optimization algorithm / activation function

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Liu Haipeng, He Yanping, Jin Huaiping, Fang Qiwen, Wu Hong. SHORT-TERM PV POWER PREDICTION CONSIDERING DATA DECOMPOSITION AND GISH-BITCN-MHSA[J]. Acta Energiae Solaris Sinica. 2025, 46(8): 430-438 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0603

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