• DocumentCode
    468924
  • Title

    Special periods peak load analysis and superior forecasting method based on LS-SVM

  • Author

    Wang, Jian-Zhou ; Wu, Liang ; Lu, Hai-yan

  • Author_Institution
    Lanzhou Univ., Lanzhou
  • Volume
    1
  • fYear
    2007
  • fDate
    2-4 Nov. 2007
  • Firstpage
    249
  • Lastpage
    253
  • Abstract
    People often try to smooth or eliminate load outliers all together in traditional power load forecasting. This, however, could result in the loss of important hidden information. In other words, the power load outliers themselves may be particular important. Hence there is a beforehand estimate to change and characteristic of power load, especially in power load outliers or peak load, is a precondition of power system carry through economy dispatch, reduce production cost and prevent widespread blackout or collapse on electric system. In this paper propose a novel method for special periods power peak load detection, mining and forecasting. It incorporates the characteristic of high level load and maximum peak load analysis with optimum forecasting algorithm based on support vector machine. The validity of the method is proved by real data calculation.
  • Keywords
    data mining; least squares approximations; load forecasting; power system analysis computing; support vector machines; electric system; least square-support vector machine; power peak load detection; power peak load mining; power system load forecasting; Artificial neural networks; Costs; Economic forecasting; Load forecasting; Pattern analysis; Power generation; Power system analysis computing; Power system security; Predictive models; Statistical analysis; Data Mining; Load Outliers; Peak Load Forecast; Support Vector Machine;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Wavelet Analysis and Pattern Recognition, 2007. ICWAPR '07. International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4244-1065-1
  • Electronic_ISBN
    978-1-4244-1066-8
  • Type

    conf

  • DOI
    10.1109/ICWAPR.2007.4420673
  • Filename
    4420673