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A new support vector machine optimized by improved particle swarm optimization and its application

  • Li Xiang Email author
  • Yang Shang-dong 
  • Qi Jian-xun 
Article

Abstract

A new support vector machine (SVM) optimized by an improved particle swarm optimization (PSO) combined with simulated annealing algorithm (SA) was proposed. By incorporating with the simulated annealing method, the global searching capacity of the particle swarm optimization (SAPSO) was enhanced, and the searching capacity of the particle swarm optimization was studied. Then, the improved particle swarm optimization algorithm was used to optimize the parameters of SVM (c, σ and ε). Based on the operational data provided by a regional power grid in north China, the method was used in the actual short term load forecasting. The results show that compared to the PSO-SVM and the traditional SVM, the average time of the proposed method in the experimental process reduces by 11.6 s and 31.1 s, and the precision of the proposed method increases by 1.24% and 3.18%, respectively. So, the improved method is better than the PSO-SVM and the traditional SVM.

Key words

support vector machine particle swarm optimization algorithm short-term load forecasting simulated annealing 

CLC number

TU457 TU413.6 

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Copyright information

© Science Press 2001

Authors and Affiliations

  1. 1.School of Business AdministrationNorth China Electric Power UniversityBeijingChina

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