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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Xiaozhi Gao & Lichi Gao & Hsiung-Cheng Lin & Yanming Huo & Yaheng Ren & Wang Guo, 2022. "Development of MVMD-EO-LSTM Model for a Short-Term Photovoltaic Power Prediction," Energies, MDPI, vol. 15(19), pages 1-15, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7332-:d:934393
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    References listed on IDEAS

    as
    1. Ren, Xiaoqing & Liu, Shulin & Yu, Xiaodong & Dong, Xia, 2021. "A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM," Energy, Elsevier, vol. 234(C).
    2. Medine Colak & Mehmet Yesilbudak & Ramazan Bayindir, 2020. "Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information," Energies, MDPI, vol. 13(4), pages 1-19, February.
    3. Eseye, Abinet Tesfaye & Zhang, Jianhua & Zheng, Dehua, 2018. "Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information," Renewable Energy, Elsevier, vol. 118(C), pages 357-367.
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