• DocumentCode
    648453
  • Title

    Study of artificial neural network based short term load forecasting

  • Author

    Webberley, Ashton ; Gao, David Wenzhong

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Univ. of Denver, Denver, CO, USA
  • fYear
    2013
  • fDate
    21-25 July 2013
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    With more and more renewable energy integrated into the power grid and demand response in smart grid environment, electric load forecasting becomes more important. Accurate load forecasting facilitates better renewable energy integration and electricity market operation. Over the years, different load forecasting methods have been developed and applied. Multiple linear regression and artificial neural network based methods are well accepted by industries. This paper focuses on ANN-based method and provides detailed steps of load forecasting including data processing and neural network design.
  • Keywords
    load forecasting; neural nets; power engineering computing; power markets; regression analysis; smart power grids; artificial neural network; demand response; electric load forecasting; electricity market; multiple linear regression; power grid; renewable energy integration; short term load forecasting; smart grid environment; Artificial neural networks; Biological neural networks; Load forecasting; Load modeling; Mathematical model; Predictive models; Temperature distribution; Load forecast; artificial neural network; electric load forecasting; smart grid;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power and Energy Society General Meeting (PES), 2013 IEEE
  • Conference_Location
    Vancouver, BC
  • ISSN
    1944-9925
  • Type

    conf

  • DOI
    10.1109/PESMG.2013.6673036
  • Filename
    6673036