Optimize VMD-LSTM photovoltaic force combination prediction model based on SSA parameter optimization

Haoliang Yang; Liang Ma; Yilin Zhao; Jiajun Li; Kun Zang

doi:10.1117/12.3030652

5 June 2024 Optimize VMD-LSTM photovoltaic force combination prediction model based on SSA parameter optimization

Haoliang Yang, Liang Ma, Yilin Zhao, Jiajun Li, Kun Zang

Proceedings Volume 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024); 131632T (2024) https://doi.org/10.1117/12.3030652
Event: International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 2024, Xi'an, China

Abstract

In order to solve the problem of low prediction accuracy caused by intermittency and uncertainty of photovoltaic output, In this paper, a parameter optimization variation mode decomposition (variation mode decomposition) based on sparrow search algorithm (SSA) is proposed. Combined prediction model of VMD and long short-term memory neural network (LSTM). Firstly, Pearson correlation coefficient (PCC) was used to analyze the factors affecting PV output. Secondly, the core parameters of VMD (k value and penalty factor coefficient α) are automatically optimized by SSA. After decomposing the original PV output time series by SSA-VMD, the learning parameters in LSTM are optimized by SSA, and the SSALSTM prediction model is established for each sub-sequence obtained by decomposition. Finally, the predicted values of each subsequence are superimposed and the final predicted values are obtained. The measured data of a PV power station in Xizang Autonomous Region are used to verify the results. The results show that the prediction accuracy of the proposed combined model SSA-VMD-LSTM is significantly improved compared with the original model LSTM and the unoptimized model VMD-LSTM. Therefore, the SSA parameter optimization method can effectively improve the prediction accuracy of VMD-LSTM combined model, and is more adaptable in PV output prediction.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Haoliang Yang, Liang Ma, Yilin Zhao, Jiajun Li, and Kun Zang "Optimize VMD-LSTM photovoltaic force combination prediction model based on SSA parameter optimization", Proc. SPIE 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 131632T (5 June 2024); https://doi.org/10.1117/12.3030652

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KEYWORDS

Photovoltaics

Mathematical optimization

Data modeling

Atmospheric modeling

Education and training

Particle swarm optimization

Meteorology

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