Title :
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
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;
Conference_Titel :
Business Intelligence and Financial Engineering, 2009. BIFE '09. International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-0-7695-3705-4
DOI :
10.1109/BIFE.2009.43
