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

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Publicado en:Applied Sciences vol. 15, no. 19 (2025), p. 10612-10626
Autor principal: Pan Hailong
Otros Autores: Li, Chao, Xiao Fuming, Zhou, Hai, Zhu Binxin
Publicado:
MDPI AG
Materias:
Data processing
Accuracy
Random variables
Fourier transforms
Forecasting
Bandwidths
Optimization algorithms
Signal processing
Statistical methods
Neural networks
Efficiency
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:2076-3417
DOI:10.3390/app151910612
Fuente:Publicly Available Content Database