Genetic algorithm-based RBF neural network load forecasting model

被引：0

作者：

Yang, Zhangang ^{[1
]}

Che, Yanbo ^{[1
]}

Cheng, K. W. Eric ^{[2
]}

机构：

[1] Tianjin Univ, Sch Elect Engn & Automat, Tianjin 300072, Peoples R China

[2] Hong Kong Polytech Univ, Dept Elect Engn, Hong Kong, Peoples R China

来源：

2007 IEEE POWER ENGINEERING SOCIETY GENERAL MEETING, VOLS 1-10 | 2007年

关键词：

load forecasting; RBF neural network; real coding; genetic algorithm; convergence rate;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

To overcome the limitation of the traditional load forecasting method, a new load forecasting system basing on radial basis Gaussian kernel function (RBF) neural network is proposed in this paper. Genetic algorithm adopting the real coding, crossover probability and mutation probability was applied to optimize the parameters of the neural network, and a faster convergence rate was reached. Theoretical analysis and simulations prove that this load forecasting model is more practical and has more precision than the traditional one.

引用

页码：1560 / 1565

页数：6

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