• DocumentCode
    2457384
  • Title

    An Improved Combined Model for the Electricity Demand Forecasting

  • Author

    Wang, Huiting ; Zhu, Suling ; Zhao, Jing ; Li, Guanhong

  • Author_Institution
    Dept. of Math. & Stat., Xuchang Univ., Xuchang, China
  • fYear
    2010
  • fDate
    17-19 Dec. 2010
  • Firstpage
    108
  • Lastpage
    111
  • Abstract
    In this paper, we propose an improved combined forecasting model integrates the merits of data pretreatment, combined model and Markov chain, known as Markov combined model. The moving average is used for the data pretreatment or determination of trend, combined model is designed for the trend forecasting, and the Markov chain is employed for modifying the forecasting results of combined model. The forecasting results of electricity demand provide valuable information for the supply chain management. Therefore, we apply the Markov combined model to forecast the electricity demand. The forecasting results testify that the proposed combined forecasting technique is an effective method.
  • Keywords
    Markov processes; load forecasting; supply chain management; Markov chain; data pretreatment; electricity demand forecasting; improved combined model; supply chain management; Biological system modeling; Data models; Electricity; Forecasting; Markov processes; Mathematical model; Predictive models; Combined model; Electricity demand; Forecasting;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational and Information Sciences (ICCIS), 2010 International Conference on
  • Conference_Location
    Chengdu
  • Print_ISBN
    978-1-4244-8814-8
  • Electronic_ISBN
    978-0-7695-4270-6
  • Type

    conf

  • DOI
    10.1109/ICCIS.2010.34
  • Filename
    5709025