• DocumentCode
    2287947
  • Title

    Short-term power system load forecasting based on improved BP artificial neural network

  • Author

    Xinbo, Zhang ; Jinsai, Chen

  • Author_Institution
    Coll. of Inf. & Electron. Eng., Zhejiang Gongshang Univ., Hangzhou, China
  • Volume
    1
  • fYear
    2011
  • fDate
    10-12 June 2011
  • Firstpage
    14
  • Lastpage
    17
  • Abstract
    The accuracy of the forecast of power system loan, which is an important part of the forecast of short-term power system loan, will directly affect the economic of the power systems and its security and stability. The use of artificial neural network could get the similar feature like nonlinear system and use it on the short-term forecast. Researches about adding momentum into the improved BP network and combinating the same type of vague and mapping results when building input networks shows that it has better performance than standard BP algorithms. Meanwhile, after classification the input data categorize and dealing with the linear activate, putting these data to the corresponding sets, the result proved that its accuracy is higher than the standard of artificial neural network.
  • Keywords
    backpropagation; load forecasting; neural nets; power system economics; power system security; power system stability; improved BP artificial neural network; nonlinear system; power system economics; power system security; short-term power system load forecasting; BP neural network; momentum; short-term power load forecasting; the thought of similar day;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science and Automation Engineering (CSAE), 2011 IEEE International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4244-8727-1
  • Type

    conf

  • DOI
    10.1109/CSAE.2011.5953161
  • Filename
    5953161