• DocumentCode
    2731209
  • Title

    Improved RBF network applied to short-term load forecasting

  • Author

    Dongxiao, Niu ; Ling, Ji ; Jie, Tian

  • Author_Institution
    Dept. of Bus. & Adm. Manage., North China Electr. Power Univ., Beijing, China
  • fYear
    2011
  • fDate
    15-17 July 2011
  • Firstpage
    864
  • Lastpage
    867
  • Abstract
    From the practical application of short-term load forecasting, this article introduced the radial basis function network and use nearest neighbor clustering algorithm to determine the width of radial basis function, select the cluster centers and weights. The predicted results show that the method is faster and has higher precision.
  • Keywords
    load forecasting; pattern clustering; power engineering computing; radial basis function networks; RBF network; nearest neighbor clustering algorithm; radial basis function network; short-term load forecasting; Clustering algorithms; Heuristic algorithms; Load forecasting; Prediction algorithms; Predictive models; Radial basis function networks; Training; network; short-term load forecasting; the radial basis function;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Software Engineering and Service Science (ICSESS), 2011 IEEE 2nd International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4244-9699-0
  • Type

    conf

  • DOI
    10.1109/ICSESS.2011.5982477
  • Filename
    5982477
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2731209