Multistep Prediction of Wind Speed Based on Grey Wolf Algorithm and Extreme Learning Machine

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Multistep Prediction of Wind Speed Based on Grey Wolf Algorithm and Extreme Learning Machine

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[1]ZHANG Wenyu,MA Keke,GUO Zhenhai,et al.Multistep Prediction of Wind Speed Based on Grey Wolf Algorithm and Extreme Learning Machine[J].Journal of Zhengzhou University (Engineering Science),2024,45(02):89-96.[doi:10.13705/j.issn.1671-6833.2026.05.008]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 02 Page number: 89-96 Column: Public date: 2024-03-06

Title:: Multistep Prediction of Wind Speed Based on Grey Wolf Algorithm and Extreme Learning Machine

Author(s):: ZHANG Wenyu; MA Keke; GUO Zhenhai; ZHAO Jing; QIU Wenzhi; 1. School of Earth Sciences and Technology, Zhengzhou University, Zhengzhou 450001, China; 2. School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China; 3. State Key Laboratory of Numerical Modeling of Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

Keywords:: wind speed prediction; multi-step prediction; signal decomposition; selection of features; grey wolf op; timization; extreme learning machine

CLC:: -

DOI:: 10.13705/j.issn.1671-6833.2026.05.008

Abstract:: In order to improve the multi-step prediction of wind speed, a hybrid prediction model based on data signal decomposition and grey wolf optimization algorithm was proposed to optimize extreme learning machine. Firstly, the original wind speed time series was decomposed into several intrinsic mode functions and a residual sequence using the complete ensemble empirical mode decomposition with adaptive noise, and the partial autocorrelation function model input. Then, the model was built and the prediction was made on the decomposition subsequence. An extreme learning machine neural network with multi-input-multi-output strategy was constructed, and grey wolf algorithm was used to solve the weight and bias of the optimal hidden layer. Finally, the subsequence was reconstructed and the final prediction result was obtained. Simulation experiments were conducted using multiple sets of measured data with a time resolution of 15 minutes. The root mean square errors of the proposed model in the three wind farms were 0.859, 0.925, and 0.927, respectively, which were lower than other comparative models, verifying the effectiveness of the model in predicting wind speed in the next four hours,i.e. 16 steps prediction.

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Last Update: 2024-03-08