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Article

A PSO-VMD-LSTM-Based Photovoltaic Power Forecasting Model Incorporating PV Converter Characteristics

1
China State Grid Yichun Electric Power Supply Company, Yichun 336000, China
2
College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(19), 10612; https://doi.org/10.3390/app151910612
Submission received: 20 August 2025 / Revised: 28 September 2025 / Accepted: 29 September 2025 / Published: 30 September 2025
(This article belongs to the Section Energy Science and Technology)

Abstract

High-precision photovoltaic (PV) power generation prediction models are essential for ensuring secure and stable grid operation and optimized dispatch. Existing models often ignore the significant variations in PV grid-connected inverter loss distributions and exhibit inadequate data decomposition processing, which influences the accuracy of the prediction models. This paper proposes a PSO-VMD-LSTM prediction model that includes PV converter loss characteristics. Firstly, the Particle Swarm Optimization (PSO) algorithm is employed to optimize the parameters of Variational Mode Decomposition (VMD), enabling effective decomposition of data under different weather conditions. Secondly, the decomposed sub-modes are individually fed into Long Short-Term Memory (LSTM) networks for prediction, and the results are subsequently reconstructed to obtain preliminary predictions. Finally, a neural network-based equivalent model for inverter losses is constructed; the preliminary predictions are fed into this model to obtain the final prediction results. Simulation case studies demonstrate that the proposed PSO-VMD-LSTM-based model can comprehensively consider the impact of uneven converter loss distribution and effectively improve the accuracy of PV power prediction models.
Keywords: photovoltaic power forecasting; neural networks; variational mode decomposition; long short-term memory networks; particle swarm optimization photovoltaic power forecasting; neural networks; variational mode decomposition; long short-term memory networks; particle swarm optimization

Share and Cite

MDPI and ACS Style

Pan, H.; Li, C.; Xiao, F.; Zhou, H.; Zhu, B. A PSO-VMD-LSTM-Based Photovoltaic Power Forecasting Model Incorporating PV Converter Characteristics. Appl. Sci. 2025, 15, 10612. https://doi.org/10.3390/app151910612

AMA Style

Pan H, Li C, Xiao F, Zhou H, Zhu B. A PSO-VMD-LSTM-Based Photovoltaic Power Forecasting Model Incorporating PV Converter Characteristics. Applied Sciences. 2025; 15(19):10612. https://doi.org/10.3390/app151910612

Chicago/Turabian Style

Pan, Hailong, Chao Li, Fuming Xiao, Hai Zhou, and Binxin Zhu. 2025. "A PSO-VMD-LSTM-Based Photovoltaic Power Forecasting Model Incorporating PV Converter Characteristics" Applied Sciences 15, no. 19: 10612. https://doi.org/10.3390/app151910612

APA Style

Pan, H., Li, C., Xiao, F., Zhou, H., & Zhu, B. (2025). A PSO-VMD-LSTM-Based Photovoltaic Power Forecasting Model Incorporating PV Converter Characteristics. Applied Sciences, 15(19), 10612. https://doi.org/10.3390/app151910612

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