Short-term load forecasting based on support vector machines regression

被引：0
|
作者：
Zhang, MG [1 ]
机构：
[1] Lanzhou Univ Technol, Sch Elect & Informat Engn, Lanzhou 730050, Peoples R China
来源：
Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Vols 1-9 | 2005年
关键词：
support vector machines(SVM); short-term load forecasting(STLF); structural risk minimization (SRM); BP neural network;
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 novel method based on SVM for the electric power system short-term load forecasting was presented. The proposed algorithm embodies the Structural Risk Minimization (SRM) principle is more generalized performance and accurate as compared to artificial neural network which embodies the Embodies Risk Minimization (ERM) principle. The theory of the SVM algorithm is based on statistical learning theory. Training of SVM leads to a quadratic programming problem. In order to improve forecast accuracy, the SVM interpolates among the load and temperature data in a training data set. Analysis of the experimental results proved that SVM could achieve greater accuracy and faster speed than the BP neural network.
引用
收藏
页码：4310 / 4314
页数：5
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