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Statistical modelling of district-level residential electricity use in NSW, Australia

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Abstract

Electricity network investment and asset management require accurate estimation of future demand in energy consumption within specified service areas. For this purpose, simple models are typically developed to predict future trends in electricity consumption using various methods and assumptions. This paper presents a statistical model to predict electricity consumption in the residential sector at the Census Collection District (CCD) level over the state of New South Wales, Australia, based on spatial building and household characteristics. Residential household demographic and building data from the Australian Bureau of Statistics (ABS) and actual electricity consumption data from electricity companies are merged for 74 % of the 12,000 CCDs in the state. Eighty percent of the merged dataset is randomly set aside to establish the model using regression analysis, and the remaining 20 % is used to independently test the accuracy of model prediction against actual consumption. In 90 % of the cases, the predicted consumption is shown to be within 5 kWh per dwelling per day from actual values, with an overall state accuracy of −1.15 %. Given a future scenario with a shift in climate zone and a growth in population, the model is used to identify the geographical or service areas that are most likely to have increased electricity consumption. Such geographical representation can be of great benefit when assessing alternatives to the centralised generation of energy; having such a model gives a quantifiable method to selecting the ‘most’ appropriate system when a review or upgrade of the network infrastructure is required.

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Acknowledgments

The authors gratefully acknowledge the assistance of Charles Xu from the New South Wales Office of Environment and Heritage, and the New South Wales utility companies for providing the data used in the analysis.

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Author notes

  1. Fanny Boulaire

    Present address: QUT Creative Industries, Brisbane, QLD, 4000, Australia

Authors and Affiliations

  1. CSIRO Ecosystem Sciences, Highett, VIC, 3190, Australia

    Fanny Boulaire, Greg Foliente & Cheryl McNamara

  2. CSIRO Ecosystem Sciences, Dutton Park, QLD, 4102, Australia

    Andrew Higgins

  3. Queensland University of Technology, 2 George Street, P Block, Level 8, Brisbane, QLD, 4000, Australia

    Fanny Boulaire

Authors
  1. Fanny Boulaire
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  2. Andrew Higgins
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  3. Greg Foliente
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  4. Cheryl McNamara
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Corresponding author

Correspondence to Fanny Boulaire.

Additional information

Handled by Vinod Tewari, The Energy and Resources Institute (TERI) University, India.

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Boulaire, F., Higgins, A., Foliente, G. et al. Statistical modelling of district-level residential electricity use in NSW, Australia. Sustain Sci 9, 77–88 (2014). https://doi.org/10.1007/s11625-013-0206-8

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  • DOI: https://doi.org/10.1007/s11625-013-0206-8

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