• DocumentCode
    2100090
  • Title

    The Application of Two New Integrated Models in Short-Term Load Forecast

  • Author

    Xu Jian ; Qiu Xiaoyan ; Zhang Zijian

  • Author_Institution
    Sch. of Electr. Eng. & Inf., Sichuan Univ., Chengdu, China
  • fYear
    2010
  • fDate
    28-31 March 2010
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Based on four general forecasting models (SVM model, BP neural network model, wavelet regression model and similar date model), two new models (integrated model I and integrated model II) are proposed in this paper. In the process of determining models and parameters, the virtual forecast conception is adopted. And a series of improvements on the aspects of historical data, temperature factor, holiday factor, economic growth, etc are made. Finally a global forecast competition which was held by EUNITE Network on August 1st, 2001 is taken as an example, showing the average daily error and maximum daily error in the forecasting of this two integrated models have been improved obviously with respect to the four simple models. So the two integrated models are proved to have has an important practical value.
  • Keywords
    backpropagation; load forecasting; neural nets; power engineering computing; support vector machines; wavelet transforms; BP neural network model; EUNITE Network; average daily error; maximum daily error; short-term load forecast; virtual forecast conception; wavelet regression model; Economic forecasting; Electronic mail; Error correction; Load forecasting; Load modeling; Neural networks; Power generation economics; Predictive models; Support vector machines; Temperature;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power and Energy Engineering Conference (APPEEC), 2010 Asia-Pacific
  • Conference_Location
    Chengdu
  • Print_ISBN
    978-1-4244-4812-8
  • Electronic_ISBN
    978-1-4244-4813-5
  • Type

    conf

  • DOI
    10.1109/APPEEC.2010.5448693
  • Filename
    5448693