BiLSTM SHORT-TERM PHOTOVOLTAIC POWER PREDICTION CONSIDERING DATA DECOMPOSITION AND EVOLUTIONARY PREDATION STRATEGIES

Jiao Pihua; Cai Xu; Wang Lele; Chen Jiajia; Cao Yunfeng

doi:10.19912/j.0254-0096.tynxb.2022-1536

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (2) : 435-442. DOI: 10.19912/j.0254-0096.tynxb.2022-1536

BiLSTM SHORT-TERM PHOTOVOLTAIC POWER PREDICTION CONSIDERING DATA DECOMPOSITION AND EVOLUTIONARY PREDATION STRATEGIES

Jiao Pihua¹, Cai Xu², Wang Lele¹, Chen Jiajia¹, Cao Yunfeng²

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Abstract

A short-term photovoltaic power prediction model based on bi-directional long short-term memory（BiLSTM） considering data decomposition and evolutionary predation strategy is proposed. Firstly, for a large number of high-frequency components and complex frequency components of the original PV power, the SCS method（SVD-CEEMD-SVD, SCS）is developed to fuse the complementary ensemble empirical modal decomposition（CEEMD）with the singular value decomposition（SVD）of matrix operations through the data decomposition theory, which can realize the secondary noise reduction of PV power data. In addition, a combined prediction model with evolutionary predation strategy（EPPS）and BiLSTM is established to better exploit the intrinsic features of the proposed model for improving the prediction accuracy. Finally, the effectiveness of the model in filtering out the PV power noise and improving the prediction accuracy is verified by taking an actual PV power plant in a region of Shandong as an example.

Key words

photovoltaic power / forecasting / singular value decomposition / evolutionary predation strategy / bidirectional long short-term memory network

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Jiao Pihua, Cai Xu, Wang Lele, Chen Jiajia, Cao Yunfeng. BiLSTM SHORT-TERM PHOTOVOLTAIC POWER PREDICTION CONSIDERING DATA DECOMPOSITION AND EVOLUTIONARY PREDATION STRATEGIES[J]. Acta Energiae Solaris Sinica. 2024, 45(2): 435-442 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1536

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