SHORT TERM PHOTOVOLTAIC POWER GENERATION PREDICTIONBASED ON OVMD-CNN-BiLSTM

Liang Fuyuan; Wan Yanni; Yang Guohua; Li Fangyuan; Zhang Mengqing; Du Wenchao

doi:10.19912/j.0254-0096.tynxb.2024-1144

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (12) : 333-342. DOI: 10.19912/j.0254-0096.tynxb.2024-1144

SHORT TERM PHOTOVOLTAIC POWER GENERATION PREDICTIONBASED ON OVMD-CNN-BiLSTM

Liang Fuyuan¹, Wan Yanni², Yang Guohua^2,3, Li Fangyuan⁴, Zhang Mengqing², Du Wenchao²

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Abstract

As a kind of clean and environmentally friendly sustainable energy, photovoltaic power generation plays an important role in the process of building a new type of power system with new energy as the main body. However, the random intermittency of photovoltaic power generation brings great challenges to the stable operation of the grid. Therefore, this paper proposes convolutional neural network-bidirectional long short-term memory （CNN-BiLSTM） prediction model based on grey wolf optimization （GWO） algorithm improved variational mode decomposition （VMD） to improve the accuracy of short-term photovoltaic power generation prediction. Firstly, the pre-processed PV power generation data is decomposed into multiple frequency modal components by optimal variational mode decomposition （OVMD） algorithm. Then, different modal signals and related influencing factors are used as the input of CNN-BiLSTM network for training, verification and testing. Finally, the prediction error is analyzed and reconstructed. The simulation results based on the practical data of a photovoltaic power station in Ningxia verifies that the OVMD-CNN-BiLSTM model proposed in this paper has significant advantages in prediction accuracy and stability compared with various reference models.

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photovoltaic power / power prediction / variational mode decomposition / grey wolf optimization / convolutional neural networks / bidirectional long short term memory network

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Liang Fuyuan, Wan Yanni, Yang Guohua, Li Fangyuan, Zhang Mengqing, Du Wenchao. SHORT TERM PHOTOVOLTAIC POWER GENERATION PREDICTIONBASED ON OVMD-CNN-BiLSTM[J]. Acta Energiae Solaris Sinica. 2025, 46(12): 333-342 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1144

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