Electric Load Forecasting Based on Improved LS-SVM Algorithm

被引：0

作者：

Yan, Gang ^{[1
]}

Tang, Gao-hui ^{[1
]}

Xiong, Ji-ming ^{[1
]}

机构：

[1] Hunan Univ Finance & Econ, Dept Informat Management, Changsha, Hunan, Peoples R China

来源：

PROCEEDINGS OF THE 10TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA 2012) | 2012年

关键词：

electric load forecasting; least squares support vector machine; chaos optimization;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

An Improved least squares support vector machine (LS-SVM) algorithm is proposed for 24 points electric load forecasting. First of all, facing with the problem how to choose the optimal LS-SVM algorithm parameters, an improved LS-SVM algorithm based on chaos optimization is put forward to obtain the optimal LS-SVM algorithm parameters and corresponding model parameters. Then, a method of 24 points electric load forecasting based on the improved LS-SVM algorithm is presented, which makes 24 points forecasting models respectively. Compared with the RBF neural network method, the prediction accuracy of the proposed method is better than that of neural network method, so the validity and the superiority of the proposed method are proved.

引用

页码：3064 / 3067

页数：4