Wind-Speed Prediction in Renewable-Energy Generation Using an IHOA

Guoxiong Lin, Yaodan Chi (), Xinyu Ding, Yao Zhang, Junxi Wang, Chao Wang, Ying Song and Yang Zhao
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Guoxiong Lin: College of Electrical and Computer, Jilin Jianzhu University, Changchun 130118, China
Yaodan Chi: College of Electrical and Computer, Jilin Jianzhu University, Changchun 130118, China
Xinyu Ding: College of Electrical and Computer, Jilin Jianzhu University, Changchun 130118, China
Yao Zhang: College of Electrical and Computer, Jilin Jianzhu University, Changchun 130118, China
Junxi Wang: College of Electrical and Computer, Jilin Jianzhu University, Changchun 130118, China
Chao Wang: College of Electrical and Computer, Jilin Jianzhu University, Changchun 130118, China
Ying Song: College of Electrical and Computer, Jilin Jianzhu University, Changchun 130118, China
Yang Zhao: College of Electrical and Computer, Jilin Jianzhu University, Changchun 130118, China

Sustainability, 2025, vol. 17, issue 14, 1-25

Abstract: Accurate wind-speed prediction plays an important role in improving the operation stability of wind-power generation systems. However, the inherent complexity of meteorological dynamics poses a major challenge to forecasting accuracy. In order to overcome these limitations, we propose a new hybrid framework, which combines variational mode decomposition (VMD) for signal processing, enhanced quantum particle swarm optimization (e-QPSO), an improved walking optimization algorithm (IHOA) for feature selection and the long short-term memory (LSTM) network, and which finally establishes a reliable prediction architecture. The purpose of this paper is to optimize VMD by using the e-QPSO algorithm to improve the problems of excessive filtering or error filtering caused by parameter problems in VMD, as the noise signal cannot be filtered completely, and the number of sources cannot be accurately estimated. The IHOA algorithm is used to find the optimal hyperparameters of LSTM to improve the learning efficiency of neurons and improve the fitting ability of the model. The proposed e-QPSO-VMD-IHOA-LSTM model is compared with six established benchmark models to verify its predictive ability. The effectiveness of the model is verified by experiments using the hourly wind-speed data measured in four seasons in Changchun in 2023. The MAPE values of the four datasets were 0.0460, 0.0212, 0.0263, and 0.0371, respectively. The results show that e-QPSO-VMD effectively processes the data and avoids the problem of error filtering, while IHOA effectively optimizes the LSTM parameters and improves prediction performance.

Keywords: wind-speed prediction; VMD; LSTM; HOA (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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