• DocumentCode
    1269353
  • Title

    Integrated ANN approach to forecast load

  • Author

    Swarup, K. Shanti ; Satish, B.

  • Author_Institution
    Indian Inst. of Technol., Madras, India
  • Volume
    15
  • Issue
    2
  • fYear
    2002
  • fDate
    4/1/2002 12:00:00 AM
  • Firstpage
    46
  • Lastpage
    51
  • Abstract
    The demand for electricity is known to vary by the time of the day, week, month, temperature, and usage habits of the consumers. Though usage habit is not directly observable, it may be implied in the patterns of usage that have occurred in the past. A short-term load-forecasting (STLF) program that uses an integrated artificial neural network (ANN) approach is capable of predicting load for basic generation scheduling functions, assessing power system security, and providing timely dispatcher information. How well training data is chosen in an ANN is the defining factor in how well the network´s output will match the event being modeled.
  • Keywords
    learning (artificial intelligence); load forecasting; neural nets; power system analysis computing; ANN; assessment; dispatcher information; electricity demand; generation scheduling functions; integrated artificial neural network; power system security; short-term load-forecasting program; training data; usage habit; Economic forecasting; Fuel economy; Humidity; Input variables; Load forecasting; Load management; Power generation economics; Power system economics; Temperature; Weather forecasting;
  • fLanguage
    English
  • Journal_Title
    Computer Applications in Power, IEEE
  • Publisher
    ieee
  • ISSN
    0895-0156
  • Type

    jour

  • DOI
    10.1109/67.993760
  • Filename
    993760