Short-term Load Forecasting Based on Least Square Support Vector Machine Combined with Fuzzy Control

被引：0

作者：

Gao, Rong ^{[1
]}

Zhang, Liyuan ^{[1
]}

Liu, Xiaohua ^{[1
]}

机构：

[1] LuDong Univ, Sch Math & Informat, Yantai, Shandong, Peoples R China

来源：

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

关键词：

power system; short-term load-forecasting; lest square support vector machine; fuzzy control;

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：

A short-term load forecasting method based on least square support vector machine(LS-SVM) combined with fuzzy control was proposed. The peak load and valley load was forecasted by LS-SVM model which was built by analysis of load data and meteorological data. Then the peak load and valley load was tuned by fuzzy rules which has been built by forecasting error data. One day and one week ahead load has been got by combing peak load and valley load with similar day load change coefficient. The load data and meteorological data of Shan Dong electrical company of 2008 was utilized to test the forecasting model. The simulation result shows the proposed method can improve the predicting accuracy.

引用

页码：1048 / 1051

页数：4