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Photovoltaic Power Generation Forecasting Based on Secondary Data Decomposition and Hybrid Deep Learning Model

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  • Liwei Zhang

    (School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, China
    Fujian Province Industrial Integrated Automation Industry Technology Development Base, Fuzhou 350118, China)

  • Lisang Liu

    (School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, China
    Fujian Province Industrial Integrated Automation Industry Technology Development Base, Fuzhou 350118, China)

  • Wenwei Chen

    (School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, China
    Fujian Province Industrial Integrated Automation Industry Technology Development Base, Fuzhou 350118, China)

  • Zhihui Lin

    (School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, China
    Fujian Province Industrial Integrated Automation Industry Technology Development Base, Fuzhou 350118, China)

  • Dongwei He

    (School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, China
    Fujian Province Industrial Integrated Automation Industry Technology Development Base, Fuzhou 350118, China)

  • Jian Chen

    (School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, China
    Fujian Province Industrial Integrated Automation Industry Technology Development Base, Fuzhou 350118, China)

Abstract

Accurate forecasting of photovoltaic (PV) power generation is crucial for optimizing grid operation and ensuring a reliable power supply. However, the inherent volatility and intermittency of solar energy pose significant challenges to grid stability and energy management. This paper proposes a learning model named CECSVB-LSTM, which integrates several advanced techniques: a bidirectional long short-term memory (BILSTM) network, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), variational mode decomposition (VMD), and the Sparrow Search Algorithm (CSSSA) incorporating circle chaos mapping and the Sine Cosine Algorithm. The model first uses CEEMDAN to decompose PV power data into Intrinsic Mode Functions (IMFs), capturing complex nonlinear features. Then, the CSSSA is employed to optimize VMD parameters, particularly the number of modes and the penalty factor, ensuring optimal signal decomposition. Subsequently, BILSTM is used to model time dependencies and predict future PV power output. Empirical tests on a PV dataset from an Australian solar power plant show that the proposed CECSVB-LSTM model significantly outperforms traditional single models and combination models with different decomposition methods, improving R 2 by more than 7.98% and reducing the root mean square error (RMSE) and mean absolute error (MAE) by at least 60% and 55%, respectively.

Suggested Citation

  • Liwei Zhang & Lisang Liu & Wenwei Chen & Zhihui Lin & Dongwei He & Jian Chen, 2025. "Photovoltaic Power Generation Forecasting Based on Secondary Data Decomposition and Hybrid Deep Learning Model," Energies, MDPI, vol. 18(12), pages 1-25, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:12:p:3136-:d:1679255
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    References listed on IDEAS

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