• DocumentCode
    3364985
  • Title

    Gray-Regression Variable Weight Combination Model for Load Forecasting

  • Author

    Zhang Fuwei ; Zhou Xuelian

  • Author_Institution
    Sch. of Bus. Adm., North China Electr. Power Univ., Beijing
  • fYear
    2008
  • fDate
    4-6 Nov. 2008
  • Firstpage
    311
  • Lastpage
    316
  • Abstract
    A gray model and regression model based middle and long term load forecasting method using variable weight combination model is proposed. In view of the shortcomings of grey prediction model is not very suitable for middle and long term load forecasting, the equivalent dimensions additional data processing technology is adopted to build the equivalent dimensions additional grey model to improve the model. At the same time, there are some characteristic within mid-long term load forecasting such as the long study time span, the complex factors with large uncertainty which have great influence on load forecasting, and the possible original error occurring in basic data of forecasting, the time-varying weight combinational prediction method is adopted to overcome the shortcomings of the fixed weight, it is more practical. The example results show that this model is applicable in the long-term load forecasting, and it has a high forecasting accuracy.
  • Keywords
    combinatorial mathematics; grey systems; load forecasting; regression analysis; time-sharing systems; time-varying systems; data processing technology; gray-regression variable weight combination model; long-term load forecasting; time-varying prediction method; Economic forecasting; Load forecasting; Load modeling; Power system management; Power system modeling; Power system planning; Power system reliability; Power system stability; Prediction methods; Predictive models; equivalent dimensions addition; grey model; load forecasting; regression model; variable weight combination;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Risk Management & Engineering Management, 2008. ICRMEM '08. International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-0-7695-3402-2
  • Type

    conf

  • DOI
    10.1109/ICRMEM.2008.14
  • Filename
    4673246