Short-Term Wind Power Prediction by an Extreme Learning Machine Based on an Improved Hunter–Prey Optimization Algorithm

Xiangyue Wang, Ji Li (), Lei Shao (), Hongli Liu, Lei Ren and Lihua Zhu
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Xiangyue Wang: School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300384, China
Ji Li: Tianjin Key Laboratory for Control Theory & Application in Complicated Systems, Tianjin 300384, China
Lei Shao: Tianjin Key Laboratory for Control Theory & Application in Complicated Systems, Tianjin 300384, China
Hongli Liu: Tianjin Key Laboratory for Control Theory & Application in Complicated Systems, Tianjin 300384, China
Lei Ren: Tianjin Key Laboratory for Control Theory & Application in Complicated Systems, Tianjin 300384, China
Lihua Zhu: Tianjin Key Laboratory for Control Theory & Application in Complicated Systems, Tianjin 300384, China

Sustainability, 2023, vol. 15, issue 2, 1-14

Abstract: Considering the volatility and randomness of wind speed, this research suggests an improved hunter-prey optimization (IHPO) algorithm-based extreme learning machine (ELM) short-term wind power prediction model to increase short-term wind power prediction accuracy. The original wind power history data from the wind farm are used in the model to achieve feature extraction and data dimensionality reduction, using the partial least squares’ variable importance of projection (PLS-VIP) and normalized mutual information (NMI) methods. Adaptive inertia weights are added to the HPO algorithm’s optimization search process to speed up the algorithm’s convergence. At the same time, the initialized population is modified, to improve the algorithm’s ability to perform global searches. To accomplish accurate wind power prediction, the enhanced algorithm’s optimal parameters optimize the extreme learning machine’s weights and threshold. The findings demonstrate that the method accurately predicts wind output and can be confirmed using measured data from a wind turbine in Inner Mongolia, China.

Keywords: partial least squares’ variable importance of projection; normalized mutual information; hunter–prey optimization algorithm; extreme learning machine; wind power prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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