• DocumentCode
    1652211
  • Title

    Middle-long Power Load Forecasting Based on Genetic Algorithm

  • Author

    Dongxiao, Niu ; Jinchao, Li ; Jinying, Li ; Da, Liu

  • Author_Institution
    North China Electr. Power Univ., Beijing
  • fYear
    2007
  • Firstpage
    790
  • Lastpage
    793
  • Abstract
    Middle-long forecasting of electric power is the guarantee for the healthy development of the electric industry. In this paper, several forecasting methods are measured by several indexes, and then the entropy method is used to form a comprehensive index to set up the object function of genetic algorithm. Next the genetic algorithm is used to calculate the weight of every forecasting method. At last, we get the final result by adding all the results of every forecasting method. Example in this paper shows that this method will improve the accuracy of middle-long forecasting of electric power and decrease the forecasting risk.
  • Keywords
    electricity supply industry; genetic algorithms; load forecasting; comprehensive index; electric industry; entropy method; genetic algorithm; middle-long electric power load forecasting; object function; Economic forecasting; Energy management; Entropy; Genetic algorithms; Industrial economics; Load forecasting; Power generation economics; Predictive models; Entropy; Error index; Genetic algorithm; Power load forecasting;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control Conference, 2007. CCC 2007. Chinese
  • Conference_Location
    Hunan
  • Print_ISBN
    978-7-81124-055-9
  • Electronic_ISBN
    978-7-900719-22-5
  • Type

    conf

  • DOI
    10.1109/CHICC.2006.4347372
  • Filename
    4347372