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Generation of Synthetic Sequences of Electricity Demand with Applications

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Uncertainty and Environmental Decision Making

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 138))

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 can 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 componentwas modelled with ARMA processes. The temperature series was modelled in a similar fashion. The stochastic modelling was performed to build probability distributions of the outputs to calculate probabilistic forecasts.As one 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 ten years was calculated. Additionally, the bivariate temperature and demand model was used in software designed to optimise the orientation of photovoltaic cells to match demand.

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Author information

Authors and Affiliations

  1. School of Mathematics and Statistics, CIAM, ISST, University of South Australia, Mawson Lakes Boulevard, Mawson Lakes, Adelaide, South Australia, 5095, AUSTRALIA

    J. W. Boland

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  1. J. W. Boland
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Editor information

Editors and Affiliations

  1. ISST, University of South Australia, Mawson Lakes, 5095, Australia

    Jerzy A. Filar

  2. ORDECSYS / C-ORDEE, Place de L'Etrier 4, Chene-Bougeries, 1224, Switzerland

    Alain Haurie

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Boland, J.W. (2009). Generation of Synthetic Sequences of Electricity Demand with Applications. In: Filar, J., Haurie, A. (eds) Uncertainty and Environmental Decision Making. International Series in Operations Research & Management Science, vol 138. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1129-2_10

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