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

Author

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  • 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.

Suggested Citation

  • Shu Fan & Rob Hyndman, 2010. "Short-term load forecasting based on a semi-parametric additive model," Monash Econometrics and Business Statistics Working Papers 17/10, Monash University, Department of Econometrics and Business Statistics.
  • Handle: RePEc:msh:ebswps:2010-17
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    File URL: http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2010/wp17-10.pdf
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    Citations

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    Cited by:

    1. Cho, Haeran & Goude, Yannig & Brossat, Xavier & Yao, Qiwei, 2013. "Modeling and forecasting daily electricity load curves: a hybrid approach," LSE Research Online Documents on Economics 49634, London School of Economics and Political Science, LSE Library.
    2. Nedellec, Raphael & Cugliari, Jairo & Goude, Yannig, 2014. "GEFCom2012: Electric load forecasting and backcasting with semi-parametric models," International Journal of Forecasting, Elsevier, vol. 30(2), pages 375-381.
    3. Eichler Michael & Grothe Oliver & Tuerk Dennis & Manner Hans, 2012. "Modeling spike occurrences in electricity spot prices for forecasting," Research Memorandum 029, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    4. Roman Frigg & Seamus Bradley & Hailiang Du & Leonard A. Smith, "undated". "Laplace�s Demon and Climate Change," GRI Working Papers 103, Grantham Research Institute on Climate Change and the Environment.

    More about this item

    Keywords

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

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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