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Towards a Residential Air-Conditioner Usage Model for Australia

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  • Mark Goldsworthy

    (Commonwealth Scientific and Industrial Research Organisation (CSIRO), Newcastle 2304, Australia)

Abstract

Realistic models of occupant behaviour in relation to air-conditioner (a/c) use are fundamentally important for developing accurate building energy simulation tools. In Australia and elsewhere, such simulation tools are inextricably bound both in legislation and in the design of new technology, electricity infrastructure and regulatory schemes. An increasing number of studies in the literature confirm just how important occupants are in determining overall energy consumption, but obtaining the data on which to build behaviour models is a non-trivial task. Here data is presented on air-conditioner usage derived from three different types of case study analyses. These are: (i) use of aggregate energy consumption data coupled with weather, demographic and building statistics across Australia to estimate key predictors of energy use at the aggregate level; (ii) use of survey data to determine characteristic a/c switch on/off behaviours and usage frequencies; and (iii) use of detailed household level sub-circuit monitoring from 140 households to determine a/c switch on/off probabilities and their dependence on different building and occupant parameters. These case studies are used to assess the difficulties associated with translation of different forms of individual, aggregate and survey based information into a/c behaviour simulation models. Finally a method of linking the data gathering methodologies with the model development is suggested. This method would combine whole-of-house “smart”-meter data measurements with linked targeted occupant surveying.

Suggested Citation

  • Mark Goldsworthy, 2017. "Towards a Residential Air-Conditioner Usage Model for Australia," Energies, MDPI, vol. 10(9), pages 1-21, August.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:9:p:1256-:d:109600
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    References listed on IDEAS

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    1. Elisha R. Frederiks & Karen Stenner & Elizabeth V. Hobman, 2015. "The Socio-Demographic and Psychological Predictors of Residential Energy Consumption: A Comprehensive Review," Energies, MDPI, vol. 8(1), pages 1-37, January.
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    Cited by:

    1. Zhang, Chengyu & Luo, Zhiwen & Rezgui, Yacine & Zhao, Tianyi, 2024. "Enhancing building energy consumption prediction introducing novel occupant behavior models with sparrow search optimization and attention mechanisms: A case study for forty-five buildings in a univer," Energy, Elsevier, vol. 294(C).
    2. Zhang, Chengyu & Ma, Liangdong & Han, Xing & Zhao, Tianyi, 2024. "Reconstituted data-driven air conditioning energy consumption prediction system employing occupant-orientated probability model as input and swarm intelligence optimization algorithms," Energy, Elsevier, vol. 288(C).

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