Application of RBF Neural Network in Short-Term Load Forecasting

被引：0

作者：

Liang, Yongchun ^{[1
]}

机构：

[1] Hebei Univ Sci & Technol, Dept Elect & Informat, Shijiazhuang, Peoples R China

来源：

ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL INTELLIGENCE, PT I | 2010年 / 6319卷

关键词：

RBF network; Load forecasting; Power system;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A Radius Basic Function (RBF) neural network is proposed for the power load forecasting. RBF neural network can meet nonlinear recognition and process predition of the dynamic system, and has better adaptability to dynamic forecasting and prediction problem in mechnism. The RBF centres are determined by the orthogonal least squared (OLS) learning procedure. The effectiveness of the model and algorithm with the example of power load forecasting have been proved and approximation capability and learning speed of RBF neural network is better than BP neural network.

引用

页码：1 / 9

页数：9