The Evaluation of Bidder´s Competitive Power Based on LS-SVM Optimized by Dynamic Inertia Weight PSO Algorithm

Author

Yuan, Xiu-E ; Sun, Xiaoya

Author_Institution

Dept. of Econ. & Manage., North China Electr. Power Univ., Baoding, China

fYear

2009

fDate

24-26 July 2009

Firstpage

148

Lastpage

151

Abstract

The evaluation of competitive power is very important for bidder in power system, how to improve the accuracy and efficiency of evaluation is the keystone people pay attention to, and many researches have been done around it. A combined model of least squares support vector machines optimized by an improved particle swarm optimization algorithm is proposed in this paper to do evaluate the competitive. A real case is experimented with to test the performance of the model, the result shows that the proposed algorithm can reduce testing error and improve the efficiency of traditional evaluate model.

Keywords

commerce; least squares approximations; particle swarm optimisation; power system economics; support vector machines; LS-SVM; bidder competitive power; dynamic inertia weight PSO algorithm; least squares support vector machines; particle swarm optimization algorithm; power system; Competitive intelligence; Conference management; Kernel; Least squares methods; Performance gain; Power engineering and energy; Power system modeling; Sun; Support vector machines; Testing; LS-SVM; competitive power; dynamic inertia weight PSO;

fLanguage

English

Publisher

ieee

Conference_Titel

Business Intelligence and Financial Engineering, 2009. BIFE '09. International Conference on

Conference_Location

Beijing

Print_ISBN

978-0-7695-3705-4

Type

conf

DOI

10.1109/BIFE.2009.43

Filename

5208914