• DocumentCode
    1753804
  • Title

    The Short-Term Load Forecasting Based on Grey Theory and RBF Neural Network

  • Author

    Li Xiao-cong ; Wang Le ; Li Qiu-wen ; Wang Ke

  • Author_Institution
    Sch. of Electr. Eng., Guangxi Univ., Nanning, China
  • fYear
    2011
  • fDate
    25-28 March 2011
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Interpolation method used to process the raw data, select Grey Theory and RBF (Radial Basis Function) neural network to forecast load. By comparing the accuracy of Grey Theory and RBF neural network forecasting, the results illustrate that the forecasting accuracy are satisfactory; accordingly it shows the validity and practicability of the methods.
  • Keywords
    interpolation; load forecasting; radial basis function networks; Grey theory; RBF neural network; interpolation method; radial basis function; short-term load forecasting; Accuracy; Artificial neural networks; Forecasting; Load forecasting; Mathematical model; Predictive models;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power and Energy Engineering Conference (APPEEC), 2011 Asia-Pacific
  • Conference_Location
    Wuhan
  • ISSN
    2157-4839
  • Print_ISBN
    978-1-4244-6253-7
  • Type

    conf

  • DOI
    10.1109/APPEEC.2011.5748765
  • Filename
    5748765
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1753804