Towards a residential air-conditioner usage model for Australia

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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.

Figures

  • Figure 1. Conceptual diagr m showing differ nt inter-connecti s i t e problem of developing a general r sidential air-conditioner usage model.
  • Figure 2. (a) Contours of cooling degree days and (b) heating degree days across Australia based on apparent temperature as calculated using 2011 Bureau of Meteorology (BOM) weather station measurements.
  • Figure 3. Cont.
  • Figure 3. (a,c) Regression fits of Weibull distribution scale parameter and shape parameter for distributions of annual energy consumption for Statistical Area Level 2 (SA2) regions across Australia; (b,d) Comparison of relative main effects sizes of significant regression predictors in the fitted models for scale parameter and shape parameter.
  • Figure 4. Probability of air-conditioner (a/c) “switch on” action and “switch off” actions as a function of apparent ambient and apparent indoor temperatures for days.
  • Figure 5. Cumulative probability function for a/c switch off-behaviour as a function of duration on.

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CITATION STYLE

APA

Goldsworthy, M. (2017). Towards a residential air-conditioner usage model for Australia. Energies, 10(9). https://doi.org/10.3390/en10091256

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