Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 58-65.doi: 10.11896/JsJkx.191000042

• Artificial Intelligence • Previous Articles     Next Articles

Application of Power Load Prediction Based on Improved Support Vector Regression Machine

TANG Cheng-e1 and WEI Jun2   

  1. 1 College of Electromechanical and Information Engineering,Guangxi Vocational and Technical College,Nanning 530226,China
    2 Guangxi Zhuang Autonomous Region Admission Examination Institute,Nanning 530021,China
  • Published:2020-07-07
  • About author:TANG Cheng-e, born in 1983, lecturer.Her main research interests include neural networks and automation of electric power systems.
  • Supported by:
    This work was supported by the 2018 Guangxi College Young and Middle-aged Teacher’s Basic Ability Improvement ProJect(2018KY0956).

Abstract: Electricity forecasting is an important engineering application.In order to solve the accuracy problem of dynamical granular support vector regression machine for power load forecasting (DGSVRM),this paper proposes a hybrid algorithm of glowworm swarm optimization (GSO) and pattern search (PS) to optimize the key parameters of DGSVRM forecasting model.Simulation results show that the prediction accuracy is greatly improved by optimizing the parameters of the prediction model.

Key words: Dynamical granular support vector regression machine, Glowworm swarm optimization, Pattern search algorithm

CLC Number: 

  • TP391
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