A Hybrid Algorithm for Short-Term Wind Power Prediction

Zhenhua Xiong, Yan Chen (), Guihua Ban, Yixin Zhuo and Kui Huang
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Zhenhua Xiong: School of Computer and Electronic Information, Guangxi University, Nanning 530004, China
Yan Chen: School of Computer and Electronic Information, Guangxi University, Nanning 530004, China
Guihua Ban: School of Computer and Electronic Information, Guangxi University, Nanning 530004, China
Yixin Zhuo: Dispatch and Control Center of Guangxi Power Grid, Nanning 530023, China
Kui Huang: Dispatch and Control Center of Guangxi Power Grid, Nanning 530023, China

Energies, 2022, vol. 15, issue 19, 1-11

Abstract: Accurate and effective wind power prediction plays an important role in wind power generation, distribution, and management. Inthis paper, a hybrid algorithm based on gradient descent and meta-heuristic optimization is designed to improve the accuracy of prediction and reduce the computational burden. The hybrid algorithm includes three steps: in the first step, we use the gradient descent algorithm to get the initial parameters. Secondly, we input the initial parameters into the meta-heuristic optimization algorithm to search for the “best parameters” (high-quality inferior solutions). Finally, we input optimized parameters into the RMSProp optimization algorithm and conduct gradient descent again to find a better solution. We used 2021 wind power data from Guangxi, China for the experiment. The results show that the hybrid prediction algorithm has better performance than the traditional Back Propagation (BP) in accuracy, stability, and efficiency.

Keywords: shuffled frog leaping algorithm (SFLA); back propagation neural network (BPNN); root mean square propagation (RMSProp); artificial neural network (ANN); wind power forecasting; short term predict (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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