Support Vector Machine with PSO Algorithm in Short-term Load Forecasting

被引：3

作者：

Gao Rong ^{[1
]}

Liu Xiaohua ^{[1
]}

机构：

[1] Ludong Univ, Shool Math & Informat, Yantai 264000, Peoples R China

来源：

2008 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-11 | 2008年

关键词：

load forecasting; particle warm optimization; support vector machine;

D O I：

10.1109/CCDC.2008.4597492

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Support vector machines(SVM) have been successfully employed to solve nonlinear regression and time series problem. In this paper SVM and particle swarm optimization(PSO) have been employed to forecast electricity load. PSO algorithm was employed to choose the parameters of a SVM. Subsequntly, examples of electricity load data from Shandong electric company were used to illustrate the proposed method. The result reveal that the proposed method was effective.

引用

页码：1140 / 1142

页数：3