• DocumentCode
    3384065
  • Title

    Electricity price forecasting considering residual demand

  • Author

    Motamedi, Ali ; Geidel, C. ; Zareipour, Hamidreza ; Rosehart, W.D.

  • Author_Institution
    Alberta Electr. Syst. Operator (AESO), Calgary, AB, Canada
  • fYear
    2012
  • fDate
    14-17 Oct. 2012
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    In this paper, short-term electricity price forecasting considering residual electricity demand is investigated. Residual, or net, demand is determined by subtracting any unpredictable generation from the system load. Focusing on wind energy as the main hard-to-predict source of electricity, we first examine the dependency of short-term electricity prices and wind power using data association mining algorithms. Second, we investigate the impact of including net demand in short-term electricity price forecasting, and we propose a new electricity price forecasting model. Data from the Alberta and the Nordic electricity markets are used to conduct studies and evaluate the forecasting results.
  • Keywords
    data mining; load forecasting; power engineering computing; power markets; pricing; sensor fusion; wind power plants; Alberta electricity markets; Nordic electricity markets; data association mining algorithms; residual electricity demand; short-term electricity price forecasting model; system load; wind energy; wind power; Data mining; Electricity; Electricity supply industry; Forecasting; Pragmatics; Predictive models; Wind power generation; Price forecasting; residual demand; smart grid; wind power;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Innovative Smart Grid Technologies (ISGT Europe), 2012 3rd IEEE PES International Conference and Exhibition on
  • Conference_Location
    Berlin
  • ISSN
    2165-4816
  • Print_ISBN
    978-1-4673-2595-0
  • Electronic_ISBN
    2165-4816
  • Type

    conf

  • DOI
    10.1109/ISGTEurope.2012.6465677
  • Filename
    6465677