SHORT TERM PHOTOVOLTAIC POWER GENERATION PREDICTION BASED ON IMVMD AND BILSTM-SARIMA COMBINATION MODEL IN STATION AREA

Li Chenghao; Yang Yongbiao; Song Jiaqi; Zhang Xiangying; Xu Qingshan

doi:10.19912/j.0254-0096.tynxb.2023-1492

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (2) : 433-440. DOI: 10.19912/j.0254-0096.tynxb.2023-1492

SHORT TERM PHOTOVOLTAIC POWER GENERATION PREDICTION BASED ON IMVMD AND BILSTM-SARIMA COMBINATION MODEL IN STATION AREA

Li Chenghao^1,2, Yang Yongbiao^1,2, Song Jiaqi³, Zhang Xiangying⁴, Xu Qingshan^1,2

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Abstract

Aiming at the difficulty of low accuracy of short-term power prediction of distributed PV in the station area, a multivariate variational modal decomposition approach based on the enhanced whale optimization algorithm is proposed and combined with a back propagation neural network coupled with bidirectional long and short-term memory network and a seasonal differential autoregressive sliding average is combined to achieve the prediction of short-term power generation of distributed PV in the station area. In the paper, the convergence factor and weight of the whale optimization algorithm are firstly improved, and then it is used to optimize the number of channels and the penalty factor in the multivariate variational modal decomposition method, to get the parameter values of the best decomposition effect. Then for the PV power time series data which is strongly correlated with external meteorological factors, the sequence is optimally decomposed using the improved multivariate modal decomposition. The decomposed modal components are input into the separately constructed bidirectional long- and short-term memory network and seasonal differential autoregressive sliding average model to obtain the component prediction values, and the component prediction values obtained from the two models are superimposed to obtain their respective complete prediction results. The final prediction result is obtained

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modal decomposition / neural networks / PV power generation / forecasting / BiLSTM / SARIMA

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Li Chenghao, Yang Yongbiao, Song Jiaqi, Zhang Xiangying, Xu Qingshan. SHORT TERM PHOTOVOLTAIC POWER GENERATION PREDICTION BASED ON IMVMD AND BILSTM-SARIMA COMBINATION MODEL IN STATION AREA[J]. Acta Energiae Solaris Sinica. 2025, 46(2): 433-440 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1492

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