• DocumentCode
    3069967
  • Title

    Short-term electricity demand forecasting method for smart meters

  • Author

    Weranga, K.S.K. ; Chandima, D.P. ; Munasinghe, S.R. ; Kumarawadu, S.P. ; Harsha S, A.M.

  • Author_Institution
    Dept. of Electr. Eng., Univ. of Moratuwa, Katubedda, Sri Lanka
  • fYear
    2012
  • fDate
    27-29 Sept. 2012
  • Firstpage
    266
  • Lastpage
    272
  • Abstract
    The short-term electricity demand forecasting has become one of the major research area in power system engineering. By combining the smart metering to the short-term demand forecasting techniques, new features can be added to save on demand and electricity bill. This paper illustrates the methodology used to forecast electricity demand over short period of time which can be used with smart meters. Polynomial fitting with interpolation is used to forecast the demand by taking the apparent power sample points from smart meters. The outcome of this work will be beneficial to the residential or industrial electricity consumers to control the demand side loads. It will help the industrial consumers to save on maximum demand charge with the introduction of warning message or residential consumers to reduce their electricity bill by cutting down non-essential loads in peak hours.
  • Keywords
    consumer electronics; curve fitting; demand forecasting; interpolation; load flow control; load forecasting; polynomials; smart meters; demand side load control; electricity bill; electricity demand forecasting method; industrial electricity consumer; interpolation; polynomial fitting; power system engineering; residential electricity consumer; smart meter; warning message; Accuracy; Demand forecasting; Electricity; Interpolation; Load modeling; Mathematical model; Polynomials;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information and Automation for Sustainability (ICIAfS), 2012 IEEE 6th International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4673-1976-8
  • Type

    conf

  • DOI
    10.1109/ICIAFS.2012.6419915
  • Filename
    6419915