• DocumentCode
    2084221
  • Title

    Short-Term Power Load Forecasting Based on LS-SVM

  • Author

    Bin, Liu ; Guang, Xu

  • Author_Institution
    Univ. of Sci. & Technol., Shaanxi Univ. of Sci. & Technol., Xi´´an, China
  • Volume
    1
  • fYear
    2010
  • fDate
    7-8 Aug. 2010
  • Firstpage
    311
  • Lastpage
    314
  • Abstract
    In order to solve the Short-term Load Forecasting problems in Power Systems, this article puts forward the Least Squares Support Vector Machine´s improved model by selecting the appropriate Gauss kernel function and proposing the error calculation analytical method, thus reduces the computational complicate problems when large amount of data is input in Short-term Power Load Forecasting. An example is given to prove the validity of the algorithm.
  • Keywords
    least squares approximations; load forecasting; power engineering computing; support vector machines; Gauss kernel function; error calculation analytical method; least squares support vector machine; power systems; short-term power load forecasting; Classification algorithms; Load forecasting; Load modeling; Power quality; Predictive models; Support vector machines; LS-SVM; Power System; Short-term Load Forecasting;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Science and Management Engineering (ISME), 2010 International Conference of
  • Conference_Location
    Xi´an
  • Print_ISBN
    978-1-4244-7669-5
  • Electronic_ISBN
    978-1-4244-7670-1
  • Type

    conf

  • DOI
    10.1109/ISME.2010.86
  • Filename
    5572535
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2084221