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

Liwei Zhang, Lisang Liu (), Wenwei Chen, Zhihui Lin, Dongwei He and Jian Chen
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Liwei Zhang: School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, China
Lisang Liu: School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, China
Wenwei Chen: School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, China
Zhihui Lin: School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, China
Dongwei He: School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, China
Jian Chen: School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, China

Energies, 2025, vol. 18, issue 12, 1-25

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.

Keywords: short-term photovoltaic power generation forecasting; long short-term memory network; modal decomposition; sparrow search algorithm; double decomposition; deep learning (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: 2025
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