Photovoltaic Power Prediction Based on RBF Neural Network Optimized by Gray Wolf Algorithm

被引:1
|
作者
Xin, Wang [1 ,2 ]
Xin, Wang [1 ,2 ]
机构
[1] Jilin Elect Power Co Ltd, Yanbian Power Supply Co, Yanbian, Peoples R China
[2] Shanghai Jiao Tong Univ, Ctr Elect & Elect Technol, Shanghai, Peoples R China
关键词
photovoltaic power prediction; gray correlation degree; gray wolf algorithm; RBF neural network;
D O I
10.1109/ICCR51572.2020.9344245
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the vigorous development of photovoltaic power generation, the prediction accuracy of photovoltaic power generation output is getting higher and higher. Therefore, this paper proposes a photovoltaic power prediction model based on the gray wolf algorithm optimized RBF neural network. First, select the similar daily sample data through the gray correlation analysis method; then use the gray wolf algorithm to globally optimize the number of hidden layer nodes of the RBF neural network, and use the global optimal solution obtained by the gray wolf algorithm as the parameter of the RBF neural network. Finally, predict through the optimized RBF neural network. The simulation results show that the RBF neural network optimized by the gray wolf algorithm has greatly improved the prediction accuracy, and has certain significance for the actual photovoltaic power prediction.
引用
收藏
页码:226 / 230
页数:5
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