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Support vector machine forecasting method improved by chaotic particle swarm optimization and its application

  • Yan-bin Li (李彦斌)Email author
  • Ning Zhang (张宁)
  • Cun-bin Li (李存斌)
Article

Abstract

By adopting the chaotic searching to improve the global searching performance of the particle swarm optimization (PSO), and using the improved PSO to optimize the key parameters of the support vector machine (SVM) forecasting model, an improved SVM model named CPSO-SVM model was proposed. The new model was applied to predicting the short term load, and the improved effect of the new model was proved. The simulation results of the South China Power Market’s actual data show that the new method can effectively improve the forecast accuracy by 2.23% and 3.87%, respectively, compared with the PSO-SVM and SVM methods. Compared with that of the PSO-SVM and SVM methods, the time cost of the new model is only increased by 3.15 and 4.61 s, respectively, which indicates that the CPSO-SVM model gains significant improved effects.

Key words

chaotic searching particle swarm optimization (PSO) support vector machine (SVM) short term load forecast 

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Copyright information

© Central South University Press and Springer-Verlag GmbH 2009

Authors and Affiliations

  • Yan-bin Li (李彦斌)
    • 1
    • 2
    Email author
  • Ning Zhang (张宁)
    • 1
  • Cun-bin Li (李存斌)
    • 2
  1. 1.School of Economics and ManagementBeijing University of Aeronautics and AstronauticsBeijingChina
  2. 2.School of Business AdministrationNorth China Electric Power UniversityBeijingChina

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