A Hybrid Algorithm for Short-Term Wind Power Prediction

被引：6

作者：

Xiong, Zhenhua ^{[1
]}

Chen, Yan ^{[1
,2
]}

Ban, Guihua ^{[1
]}

Zhuo, Yixin ^{[3
]}

Huang, Kui ^{[3
]}

机构：

[1] Guangxi Univ, Sch Comp & Elect Informat, Nanning 530004, Peoples R China

[2] Guangxi Key Lab Multimedia Commun & Network Techn, Nanning 530004, Peoples R China

[3] Dispatch & Control Ctr Guangxi Power Grid, Nanning 530023, Peoples R China

来源：

ENERGIES | 2022年 / 15卷 / 19期

关键词：

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; MODEL; SYSTEMS;

D O I：

10.3390/en15197314

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

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.

引用

页数：11

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