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Generation of synthetic sequences of electricity demand: Application in South Australia

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  • Magnano, L.
  • Boland, J.W.

Abstract

We have developed a model to generate synthetic sequences of half-hourly electricity demand. The generated sequences represent possible realisations of electricity load that could have occurred. Each of the components included in the model has a physical interpretation. These components are yearly and daily seasonality which were modelled using Fourier series, weekly seasonality modelled with dummy variables, and the relationship with current temperature described by polynomial functions of temperature. Finally the stochastic component was modelled with autoregressive moving average (ARMA) processes. These synthetic sequences were developed for two purposes. The first one is to use them as input data in market simulation software. The second one is to build probability distributions of the outputs to calculate probabilistic forecasts. As an application several summers of half-hourly electricity demand were generated and from them the value of demand that is not expected to be exceeded more than once in 10 years was calculated.

Suggested Citation

  • Magnano, L. & Boland, J.W., 2007. "Generation of synthetic sequences of electricity demand: Application in South Australia," Energy, Elsevier, vol. 32(11), pages 2230-2243.
    • Handle: RePEc:eee:energy:v:32:y:2007:i:11:p:2230-2243
    DOI: 10.1016/j.energy.2007.04.001
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    7. Rafał Czapaj & Jacek Kamiński & Maciej Sołtysik, 2022. "A Review of Auto-Regressive Methods Applications to Short-Term Demand Forecasting in Power Systems," Energies, MDPI, vol. 15(18), pages 1-31, September.
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    10. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2013. "Evaluation of time series techniques to characterise domestic electricity demand," Energy, Elsevier, vol. 50(C), pages 120-130.
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