Forecasting electricity demand in Australian National Electricity Market

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10 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2012 IEEE Power and Energy Society General Meeting
EditorsDan Nordell
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages4
ISBN (Print)9781467327275
Publication statusPublished - 2012
EventIEEE Power and Energy Society General Meeting 2012 - Manchester Grand Hyatt, San Diego, United States of America
Duration: 22 Jul 201226 Jul 2012 (Proceedings)


ConferenceIEEE Power and Energy Society General Meeting 2012
Abbreviated titlePES-GM 2012
Country/TerritoryUnited States of America
CitySan Diego
Internet address


  • additive model
  • forecast distribution
  • load forecasting
  • time series

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