• DocumentCode
    2058769
  • Title

    Forecasting electricity demand in Australian National Electricity Market

  • Author

    Shu Fan ; Hyndman, R.J.

  • Author_Institution
    Bus. & Econ. Forecasting Unit, Monash Univ., Clayton, VIC, Australia
  • fYear
    2012
  • fDate
    22-26 July 2012
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Load forecasting is a key task for the effective operation and planning of power systems. It is concerned with the prediction of hourly, daily, weekly, and annual values of the system demand and peak demand. Such forecasts are sometimes categorized as short-term, medium-term and long-term forecasts, depending on the time horizon. Long-term load forecasting is an integral process in scheduling the construction of new generation facilities and in the development of transmission and distribution systems, while short-term forecasting provides essential information for economic dispatch, unit commitment and electricity market. A comprehensive forecasting solution developed by Monash University is described in this paper. The semi-parametric additive models based forecasting system has been used to forecast the electricity demands for regions in the National Electricity Market. The forecasting system covers the time horizon from hours ahead up to years ahead, and provides both point forecasts (i.e., forecasts of the mean or median of the future demand distribution), and density forecasts (providing estimates of the full probability distributions of the possible future values of the demand). The performance of the methodology have been validated through the developments of the past years, and the forecasting system is currently used by the Australian Energy Market Operator (AEMO) for system planning and schedule.
  • Keywords
    load forecasting; power generation dispatch; power generation scheduling; power markets; power system planning; time series; Australian national electricity market; density forecasts; economic dispatch; electricity demand forecasting; load forecasting; peak demand; power system planning; semiparametric additive models; system demand; time horizon; unit commitment; Biological system modeling; Educational institutions; Electricity; Forecasting; Load forecasting; Load modeling; Predictive models; additive model; forecast distribution; load forecasting; time series;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power and Energy Society General Meeting, 2012 IEEE
  • Conference_Location
    San Diego, CA
  • ISSN
    1944-9925
  • Print_ISBN
    978-1-4673-2727-5
  • Electronic_ISBN
    1944-9925
  • Type

    conf

  • DOI
    10.1109/PESGM.2012.6345304
  • Filename
    6345304