Open Access

ARTICLE

Short-Term Wind Power Prediction Based on Optimized VMD and LSTM

Xinjian Li¹, Yu Zhang^1,2,*, Zewen Wang¹, Zhenyun Song¹

1 College of Mechanical and Control Engineering, Guilin University of Technology, Guilin, 541006, China
2 Guangxi Key Laboratory of Building New Energy and Energy Saving, Guilin, 541006, China

* Corresponding Author: Yu Zhang. Email:

(This article belongs to the Special Issue: AI in Green Energy Technologies and Their Applications)

Energy Engineering 2025, 122(11), 4603-4619. https://doi.org/10.32604/ee.2025.065799

Received 21 March 2025; Accepted 06 June 2025; Issue published 27 October 2025

Abstract

Power prediction has been critical in large-scale wind power grid connections. However, traditional wind power prediction methods have long suffered from problems, for instance low prediction accuracy and poor reliability. For this purpose, a hybrid prediction model (VMD-LSTM-Attention) has been proposed, which integrates the variational modal decomposition (VMD), the long short-term memory (LSTM), and the attention mechanism (Attention), and has been optimized by improved dung beetle optimization algorithm (IDBO). Firstly, the algorithm’s performance has been significantly enhanced through the implementation of three key strategies, namely the elite group strategy of the Logistic-Tent map, the nonlinear adjustment factor, and the adaptive T-distribution disturbance mechanism. Subsequently, IDBO has been applied to optimize the important parameters of VMD (decomposition layers and penalty factors) to ensure the best decomposition signal is obtained; Furthermore, the IDBO has been deployed to optimize the three key hyper-parameters of the LSTM, thereby improving its learning capability. Finally, an Attention mechanism has been incorporated to adaptively weight temporal features, thus increasing the model’s ability to focus on key information. Comprehensive simulation experiments have demonstrated that the proposed model achieves higher prediction accuracy compared with VMD-LSTM, VMD-LSTM-Attention, and traditional prediction methods, and quantitative indexes verify the effectiveness of the algorithmic improvement as well as the excellence and precision of the model in wind power prediction.

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Keywords

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