• DocumentCode
    3469650
  • Title

    Real time load forecast in power system

  • Author

    Daneshi, H. ; Daneshi, A.

  • Author_Institution
    LCG Consulting Eng., Los Altos, CA
  • fYear
    2008
  • fDate
    6-9 April 2008
  • Firstpage
    689
  • Lastpage
    695
  • Abstract
    This paper presents an overview of different practical techniques to forecast the load for real time applications. The accuracy of load forecast often determines the amount of energy to be procured in the imbalance market. Therefore to reduce exposures to real-time risks and obtain economic, reliable and secure operations of power system, an accurate real-time forecast is required. It can be used by vertically integrated utilities as well as the ISOs in restructured power system. In this paper, we discuss different approaches based on time series and artificial neural network (ANN). The ISO New England market data are used to illustrate and compare the models.
  • Keywords
    load forecasting; neural nets; power engineering computing; power markets; power system reliability; power system security; time series; ANN; ISO New England market; artificial neural network; imbalance market; power system; power system reliability; power system restructuring; power system security; real time load forecast; time series; Artificial neural networks; Economic forecasting; ISO; Load forecasting; Power generation economics; Power system economics; Power system modeling; Power system reliability; Power systems; Real time systems; Artificial Neural Network; load forecast; power system simulation; quantitative method; real time load forecast; regression method; time series; very short-term load forecast;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electric Utility Deregulation and Restructuring and Power Technologies, 2008. DRPT 2008. Third International Conference on
  • Conference_Location
    Nanjuing
  • Print_ISBN
    978-7-900714-13-8
  • Electronic_ISBN
    978-7-900714-13-8
  • Type

    conf

  • DOI
    10.1109/DRPT.2008.4523494
  • Filename
    4523494