Photovoltaic Power Forecasting Based on Variational Mode Decomposition and Long Short-Term Memory Neural Network

Zhijian Hou, Yunhui Zhang (), Xuemei Cheng and Xiaojiang Ye ()
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Zhijian Hou: School of Mechanical and Electrical Engineering, Shenzhen Polytechnic University, Shenzhen 518055, China
Yunhui Zhang: School of Mechanical and Electrical Engineering, Shenzhen Polytechnic University, Shenzhen 518055, China
Xuemei Cheng: School of Mechanical and Electrical Engineering, Shenzhen Polytechnic University, Shenzhen 518055, China
Xiaojiang Ye: School of Optical Information and Energy Engineering, Wuhan Institute of Technology, Wuhan 430200, China

Energies, 2025, vol. 18, issue 13, 1-28

Abstract: The accurate forecasting of photovoltaic (PV) power is vital for grid stability. This paper presents a hybrid forecasting model that combines Variational Mode Decomposition (VMD) and Long Short-Term Memory (LSTM). The model uses VMD to decompose the PV power into modal components and residuals. These components are combined with meteorological variables and their first-order differences, and feature extraction techniques are used to generate multiple sets of feature vectors. These vectors are utilized as inputs for LSTM sub-models, which predict the modal components and residuals. Finally, the aggregation of prediction results is used to achieve the PV power prediction. Validated on Australia’s 1.8 MW Yulara PV plant, the model surpasses 13 benchmark models, achieving an MAE of 63.480 kW, RMSE of 81.520 kW, and R 2 of 92.3%. Additionally, the results of a paired t -test showed that the mean differences in the MAE and RMSE were negative, and the 95% confidence intervals for the difference did not include zero, indicating statistical significance. To further evaluate the model’s robustness, white noise with varying levels of signal-to-noise ratios was introduced to the photovoltaic power and global radiation signals. The results showed that the model exhibited higher prediction accuracy and better noise tolerance compared to other models.

Keywords: PV power forecasting; sample entropy; variational mode decomposition; long short-term memory; signal-to-noise ratio (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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