• DocumentCode
    3509575
  • Title

    An artificial neural network based short term load forecasting with special tuning for weekends and seasonal changes

  • Author

    Moharari, Nadar S. ; Debs, Atif S.

  • Author_Institution
    Sch. of Electr. Eng., Georgia Inst. of Technol., Atlanta, GA, USA
  • fYear
    1993
  • fDate
    1993
  • Firstpage
    279
  • Lastpage
    283
  • Abstract
    The artificial neural network (ANN) technique is utilized for power electric load forecasting using the backpropagation algorithm developed by the authors. The major contribution of this work is the ability to forecast the power electric load for weekends and holidays as well as weekdays with a relatively small training set. In addition the effect of seasonal change in load pattern can be tracked down. Their approach is to introduce three different sets of inputs to the ANN in order to follow the load pattern, weather pattern, seasonal factors and to consider special events like weekends and holidays.
  • Keywords
    backpropagation; load forecasting; neural nets; power engineering computing; power systems; AI; artificial neural network; backpropagation algorithm; power engineering computing; seasonal changes; short term load forecasting; training; tuning; weather pattern; weekends; Artificial neural networks; Backpropagation algorithms; Fuels; Load forecasting; Maintenance; Neurons; Power system reliability; Terminology; Testing; Weather forecasting;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks to Power Systems, 1993. ANNPS '93., Proceedings of the Second International Forum on Applications of
  • Conference_Location
    Yokohama, Japan
  • Print_ISBN
    0-7803-1217-1
  • Type

    conf

  • DOI
    10.1109/ANN.1993.264334
  • Filename
    264334