• DocumentCode
    1585565
  • Title

    Middle -- long Electric Power Load Forecasting Based on Co-integration and Support Vector Machine

  • Author

    Niu, Dongxiao ; Li, Jinchao ; Li, Jinying

  • Author_Institution
    North China Electr. Power Univ., Beijing
  • Volume
    1
  • fYear
    2007
  • Firstpage
    596
  • Lastpage
    600
  • Abstract
    Middle - long forecasting of electric power is crucial to the electric investment, which is the guarantee of the healthy development of electric industry. There are so many factors which influence the middle-long electric power load. So in this paper the co-integration technology is used to analyze the influencing factors´ inter characters, then a co- integration relating formula is found which is used to forecasting the middle-long electric power load, at last the support vector machine is used to correct the forecast error of the co-integrating method in order to improve the forecasting accuracy. The combined of the upper two methods can find out the long term equilibrium relationship and short term fluctuating relationship among the electric power load and other influencing factors, so the forecasting result become more sensible and scientific. A case showed that the proposed method is feasible and effective for middle- long electric power load forecasting.
  • Keywords
    electricity supply industry; integration; load forecasting; power system analysis computing; power system economics; support vector machines; co-integration technology; electric industry; electric investment; electric power load forecasting; middle-long electric power load; support vector machine; Autocorrelation; Econometrics; Economic forecasting; Equations; Investments; Load forecasting; Power generation economics; Support vector machines; Technology forecasting; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation, 2007. ICNC 2007. Third International Conference on
  • Conference_Location
    Haikou
  • Print_ISBN
    978-0-7695-2875-5
  • Type

    conf

  • DOI
    10.1109/ICNC.2007.472
  • Filename
    4344260