• DocumentCode
    2678507
  • Title

    A SVM and variable structure neural network method for short-term load forecasting

  • Author

    Zhang, Qian ; Liu, Tongna

  • Author_Institution
    Dept. of Economic Manage., North China Electr. Power Univ., Baoding, China
  • Volume
    5
  • fYear
    2010
  • fDate
    27-29 March 2010
  • Firstpage
    392
  • Lastpage
    396
  • Abstract
    This paper put forward a new method of the SVM and variable structure artificial neural network model for short-term load forecasting. The neural call function is basis of nonlinear wavelets. We overcome the shortcoming of single train set of SVM. It can be seen from the example this method can improve effectively the forecast accuracy and speed. The forecast model was tested and the result showed that it was an effective way to forecast short-term electric load.
  • Keywords
    load forecasting; neural nets; power systems; support vector machines; SVM; neural call function; nonlinear wavelets; short-term electric load forecasting; variable structure neural network method; Artificial neural networks; Economic forecasting; Energy management; Estimation error; Load forecasting; Neural networks; Power generation economics; Predictive models; Risk management; Support vector machines; SVM; electric load forecasting; variable structure neural network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Computer Control (ICACC), 2010 2nd International Conference on
  • Conference_Location
    Shenyang
  • Print_ISBN
    978-1-4244-5845-5
  • Type

    conf

  • DOI
    10.1109/ICACC.2010.5487080
  • Filename
    5487080