An improved WM method based on PSO for electric load forecasting

被引：48

作者：

Yang, Xueming ^{[1
,2
]}

Yuan, Jiangye ^{[3
]}

Yuan, Jinsha ^{[1
]}

Mao, Huina ^{[4
]}

机构：

[1] N China Elect Power Univ, Dept Elect & Commun Engn, Baoding 071003, Peoples R China

[2] Univ Pittsburgh, Dept Civil & Environm Engn, Pittsburgh, PA 15260 USA

[3] Ohio State Univ, Dept Comp Sci & Engn, Columbus, OH 43210 USA

[4] Indiana Univ, Sch Informat & Comp, Bloomington, IN 47408 USA

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2010年 / 37卷 / 12期

关键词：

Particle swarm optimization (PSO); Electric load forecasting; Fuzzy systems; Wang-Mendel (WM) method; FUZZY INFERENCE; SYSTEM;

D O I：

10.1016/j.eswa.2010.05.085

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The fuzzy system is an important method for intelligent modelling of electric load forecasting, and how to enhance the learning and data mining ability of fuzzy system is crucial for its practical application and the improvement of the load-forecasting accuracy. In this study. a PSO-based improved Wang-Mendel (WM) method is proposed, which is a new combined modelling method based on fuzzy system and evolutionary algorithm. This method adopts a modified Particle swarm optimization (PSO) algorithm to optimize the fuzzy rule centroid of data covered area and thus obtains complete fuzzy rule set through extrapolating. The electric load-forecasting model based on this proposed method is described, and a case study on short-term load forecast illustrates that this method effectively enhances the forecast accuracy of WM method, has a fast convergence rate, and is independent of the forecasting objects. (C) 2010 Elsevier Ltd. All rights reserved.

引用

页码：8036 / 8041

页数：6

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