Development of MVMD-EO-LSTM Model for a Short-Term Photovoltaic Power Prediction

Xiaozhi Gao, Lichi Gao, Hsiung-Cheng Lin (), Yanming Huo, Yaheng Ren and Wang Guo
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Xiaozhi Gao: College of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
Lichi Gao: College of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
Hsiung-Cheng Lin: Department of Electronic Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan
Yanming Huo: College of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
Yaheng Ren: Institute of Applied Mathematics, Hebei Academy of Sciences, Shijiazhuang 050011, China
Wang Guo: College of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China

Energies, 2022, vol. 15, issue 19, 1-15

Abstract: The accuracy and stability of short-term photovoltaic (PV) power prediction is crucial for power planning and dispatching in a grid system. For this reason, the multi-resolution variational modal decomposition (MVMD) method is proposed to achieve multi-scale input features mining for short-term PV power prediction. Here, the MVMD combined with Spearman extracts correlation features of the weather data. An equilibrium optimizer (EO) is integrated with MVMD to achieve optimal values of the long short-term memory (LSTM) parameters. Firstly, the correlation of input features is determined and selected by Spearman. The MVMD model is used to mine the high correlation features of solar radiation and conduct cross-correlation analysis to extract input feature components. Secondly, the similar weather days of the sample set are classified to ensure a good adaptability in different weather situations. Finally, the high correlation features are introduced into the photovoltaic power prediction model of EO optimized LSTM. Performance analysis using actual output power data from a PV plant shows that the proposed MVMD feature extraction method can effectively mine correlation features to achieve an optimized dataset under different seasons. Compared with the gray wolf and particle swarm optimization algorithms, the proposed model has a better optimization performance in a low discrimination of input feature decomposition components and low correlation with output power.

Keywords: solar energy; short-term PV power forecast; multiresolution variational modal decomposition (MVMD); feature extraction; equilibrium optimizer (EO); long short-term memory (LSTM) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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