Short-Term load forecasting based on self-organizing map and support vector machine

  • Authors:

  • Zhejing Bao;Daoying Pi;Youxian Sun

  • Affiliations:

  • National Laboratory of Industrial Control Technology, Dept. of Control Sci. and Eng., Zhejiang University, Hangzhou, P.R. China;National Laboratory of Industrial Control Technology, Dept. of Control Sci. and Eng., Zhejiang University, Hangzhou, P.R. China;National Laboratory of Industrial Control Technology, Dept. of Control Sci. and Eng., Zhejiang University, Hangzhou, P.R. China

  • Venue:
  • ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
  • Year:
  • 2005

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Abstract

An approach for short-term load forecasting by combining self-organizing map(SOM) and support vector machine(SVM) is proposed in this paper. First, historical load data of same type are clustered using SOM, and then daily 48-point load values are vertically predicted respectively based on SVM. In clustering, factors such as date type, weather conditions and time delay are considered. In addition, influences of kernel function and SVM parameters on load forecasting are discussed and performance of SOM-SVM is compared with pure SVM. It is shown that normal smoothing technique in preprocessing is not suitable to be used in vertical forecasting. Finally, the approach is tested by data from EUNITE network, and results show that the approach runs with high speed and good accuracy.