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The Importance of Environmental Factors in Forecasting Australian Power Demand

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Abstract

We develop a time series model to forecast weekly peak power demand for three main states of Australia for a yearly timescale, and show the crucial role of environmental factors in improving the forecasts. More precisely, we construct a seasonal autoregressive integrated moving average (SARIMA) model and reinforce it by employing the exogenous environmental variables including, maximum temperature, minimum temperature, and solar exposure. The estimated hybrid SARIMA-regression model exhibits an excellent mean absolute percentage error (MAPE) of \(3.41\%\). Moreover, our analysis demonstrates the importance of the environmental factors by showing a remarkable improvement of \(46.3\%\) in MAPE for the hybrid model over the crude SARIMA model which merely includes the power demand variables. In order to illustrate the efficacy of our model, we compare our outcome with the state-of-the-art machine learning methods in forecasting. The results reveal that our model outperforms the latter approach.

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Data Availability

All data are available online as provided in [19] and [20].

Notes

  1. cran.r-project.org/web/packages/astsa/index.html

  2. cran.r-project.org/web/packages/forecast/index.html

  3. cran.r-project.org/web/packages/tseries/index.html

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Acknowledgements

The authors thank the Advisory Editor and two anonymous reviewers for their invaluable comments that helped improve the previous version of this paper.

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The authors declare no funding was used in support of this research.

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Authors and Affiliations

  1. School of Information and Physical Sciences, The University of Newcastle, NSW, Newcastle, Australia

    Ali Eshragh, Benjamin Ganim & Terry Perkins

  2. School of Computing and Information Systems Melbourne Centre for Data Science, University of Melbourne, VIC, Melbourne, Australia

    Kasun Bandara

Authors
  1. Ali Eshragh
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  2. Benjamin Ganim
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  3. Terry Perkins
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  4. Kasun Bandara
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Contributions

All four authors have had significant contributions in preparing this paper, including the design of the work, the acquisition, analysis, and interpretation of the data, drafting and revising the paper.

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Correspondence to Ali Eshragh.

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Eshragh, A., Ganim, B., Perkins, T. et al. The Importance of Environmental Factors in Forecasting Australian Power Demand. Environ Model Assess 27, 1–11 (2022). https://doi.org/10.1007/s10666-021-09806-1

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