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A local-community-level, physically-based model of end-use energy consumption by Australian housing stock

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  • Ren, Zhengen
  • Paevere, Phillip
  • McNamara, Cheryl

Abstract

We developed a physics based bottom-up model to estimate annual housing stock energy consumption at a local community level (Census Collection District—CCD) with an hourly resolution. Total energy consumption, including space heating and cooling, water heating, lighting and other household appliances, was simulated by considering building construction and materials, equipment and appliances, local climates and occupancy patterns. The model was used to analyse energy use by private dwellings in more than five thousand CCDs in the state of New South Wales (NSW), Australia. The predicted results focus on electricity consumption (natural gas and other fuel sources were excluded as the data are not available) and track the actual electricity consumption at CCD level with an error of 9.2% when summed to state level. For NSW and Victoria 2006, the predicted state electricity consumption is close to the published model (within 6%) and statistical data (within 10%). A key feature of the model is that it can be used to predict hourly electricity consumption and peak demand at fine geographic scales, which is important for grid planning and designing local energy efficiency or demand response strategies.

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  • Ren, Zhengen & Paevere, Phillip & McNamara, Cheryl, 2012. "A local-community-level, physically-based model of end-use energy consumption by Australian housing stock," Energy Policy, Elsevier, vol. 49(C), pages 586-596.
    • Handle: RePEc:eee:enepol:v:49:y:2012:i:c:p:586-596
    DOI: 10.1016/j.enpol.2012.06.065
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    References listed on IDEAS

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    Cited by:

    1. Ren, Zhengen & Paevere, Phillip & Grozev, George & Egan, Stephen & Anticev, Julia, 2013. "Assessment of end-use electricity consumption and peak demand by Townsville's housing stock," Energy Policy, Elsevier, vol. 61(C), pages 888-893.
    2. Oraiopoulos, A. & Howard, B., 2022. "On the accuracy of Urban Building Energy Modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    3. Seya, Hajime & Yamagata, Yoshiki & Nakamichi, Kumiko, 2016. "Creation of municipality level intensity data of electricity in Japan," Applied Energy, Elsevier, vol. 162(C), pages 1336-1344.
    4. Higgins, Andrew & McNamara, Cheryl & Foliente, Greg, 2014. "Modelling future uptake of solar photo-voltaics and water heaters under different government incentives," Technological Forecasting and Social Change, Elsevier, vol. 83(C), pages 142-155.
    5. Kazas, Georgios & Fabrizio, Enrico & Perino, Marco, 2017. "Energy demand profile generation with detailed time resolution at an urban district scale: A reference building approach and case study," Applied Energy, Elsevier, vol. 193(C), pages 243-262.
    6. Motlagh, Omid & Paevere, Phillip & Hong, Tang Sai & Grozev, George, 2015. "Analysis of household electricity consumption behaviours: Impact of domestic electricity generation," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 165-178.
    7. Ren, Zhengen & Grozev, George & Higgins, Andrew, 2016. "Modelling impact of PV battery systems on energy consumption and bill savings of Australian houses under alternative tariff structures," Renewable Energy, Elsevier, vol. 89(C), pages 317-330.
    8. Ren, Zhengen & Paevere, Phillip & Chen, Dong, 2019. "Feasibility of off-grid housing under current and future climates," Applied Energy, Elsevier, vol. 241(C), pages 196-211.
    9. Ren, Zhengen & Chen, Dong, 2018. "Modelling study of the impact of thermal comfort criteria on housing energy use in Australia," Applied Energy, Elsevier, vol. 210(C), pages 152-166.
    10. Higgins, Andrew & Grozev, George & Ren, Zhengen & Garner, Stephen & Walden, Glenn & Taylor, Michelle, 2014. "Modelling future uptake of distributed energy resources under alternative tariff structures," Energy, Elsevier, vol. 74(C), pages 455-463.

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