Combination forecasting model for Mid-Long Term Load Based on Least Squares Support Vector Machines and a mended Particle Swarm Optimization algorithm

被引：3

作者：

Niu, Dongxiao ^{[1
]}

Lv, Haitao ^{[1
]}

Zhang, Yunyun ^{[2
]}

机构：

[1] N China Elect Power Univ, Sch Business & Management, Beijing, Peoples R China

[2] N China Elect Power Univ, Dept Econ & Management, Baoding, Peoples R China

来源：

2009 INTERNATIONAL JOINT CONFERENCE ON BIOINFORMATICS, SYSTEMS BIOLOGY AND INTELLIGENT COMPUTING, PROCEEDINGS | 2009年

基金：

中国国家自然科学基金;

关键词：

combination forecasting model; Mid-long term forecasting; LS-SVM; MPSO;

D O I：

10.1109/IJCBS.2009.16

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Mid-Long term load forecasting(MTLF) plays an important role in power system. With more factors involved, single forecasting method becomes hard to satisfy requirement. This paper proposes a new combination model for MTLF based on least squares support vector machines (LS-SVM) and particle swarm optimization (PSO) algorithm. LS-SVM is a new kind of SVM which regresses faster than standard, and a mended particle swarm optimization (MPSO) algorithm is employed to optimize the parameters of LS-SVM. With a real case test, the result shows proposed model outperforms tradition combination model.

引用

页码：525 / +

页数：2

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