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Short-term load forecasting based on a semi-parametric additive model

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Author Info

  • Shu Fan

    ()

  • Rob Hyndman

    ()

Abstract

Short-term load forecasting is an essential instrument in power system planning, operation and control. Many operating decisions are based on load forecasts, such as dispatch scheduling of generating capacity, reliability analysis, and maintenance planning for the generators. Overestimation of electricity demand will cause a conservative operation, which leads to the start-up of too many units or excessive energy purchase, thereby supplying an unnecessary level of reserve. On the contrary, underestimation may result in a risky operation, with insufficient preparation of spinning reserve, causing the system to operate in a vulnerable region to the disturbance. In this paper, semi-parametric additive models are proposed to estimate the relationships between demand and the driver variables. Specifically, the inputs for these models are calendar variables, lagged actual demand observations and historical and forecast temperature traces for one or more sites in the target power system. In addition to point forecasts, prediction intervals are also estimated using a modified bootstrap method suitable for the complex seasonality seen in electricity demand data. The proposed methodology has been used to forecast the half-hourly electricity demand for up to seven days ahead for power systems in the Australian National Electricity Market. The performance of the methodology is validated via out-of-sample experiments with real data from the power system, as well as through on-site implementation by the system operator.

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File URL: http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2010/wp17-10.pdf
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Bibliographic Info

Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 17/10.

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Length: 14 pages
Date of creation: 12 Aug 2010
Date of revision:
Handle: RePEc:msh:ebswps:2010-17

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Keywords: Short-term load forecasting; additive model; time series; forecast distribution;

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Cited by:
  1. Roman Frigg & Seamus Bradley & Hailiang Du & Leonard A. Smith, . "Laplace’s Demon and Climate Change," Grantham Research Institute on Climate Change and the Environment Working Papers 103, Grantham Research Institute on Climate Change and the Environment.

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