• DocumentCode
    1847807
  • Title

    Load forecasting using artificial neural networks

  • Author

    Pham, Khanh D.

  • Author_Institution
    Elcon Associates Inc., Portland, OR
  • fYear
    1995
  • fDate
    30 Apr-2 May 1995
  • Abstract
    Artificial neural networks, modeled after their biological counterpart, have been successfully applied in many diverse areas including speech and pattern recognition, remote sensing, electrical power engineering, robotics and stock market forecasting. The most commonly used neural networks are those that gain knowledge from experience. Experience is presented to the network in the form of training data. Once trained, the neural network can recognize data that it has not seen before. This paper presents a fundamental introduction to the manner in which neural networks work and how to use them in load forecasting
  • Keywords
    learning (artificial intelligence); load forecasting; neural nets; power system analysis computing; artificial neural networks; backpropagation; data recognition; fault tolerance; generalisation; load forecasting; neural network architecture; parallel processing; trained neural network; training; transfer function; Artificial neural networks; Biological system modeling; Load forecasting; Neural networks; Pattern recognition; Power engineering; Predictive models; Remote sensing; Robot sensing systems; Speech;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Rural Electric Power Conference, 1995. Papers Presented at the 39th Annual Conference
  • Conference_Location
    Nashville, TN
  • Print_ISBN
    0-7803-2043-3
  • Type

    conf

  • DOI
    10.1109/REPCON.1995.470937
  • Filename
    470937