The NEAR Program>Predictors of Residential Air-conditioning Cooling Behaviour

Report

Predictors of Residential Air-conditioning Cooling Behaviour

Analyses of building, occupant and environmental factors in household air-conditioner use.

PublishedJun 2016
Location ACT, NSW, Qld, SA, Tas, Vic, WA
Author(s)
CSIRO
Citation

Goldsworthy, M., Seongwon, S., and Toscas, P. (2016) Predictors of Residential Air-conditioning Cooling Behaviour. CSIRO, Australia.

Download report
Full description

The report describes initial investigations into the key factors influencing residential air conditioning (AC) for space cooling. The report includes a review of relevant literature, focussing on the main factors which influence AC use and energy consumption, including the built environment, air-conditioning equipment, climate and occupant behaviour. Results follow from a model-based sensitivity study into the theoretical relative influence of building thermal parameters on peak AC energy consumption. This analysis considered a week of weather data consisting of several high-temperature days for four different Australian climates. A detailed AC and building thermal simulation model was used to predict AC energy consumption for varying combinations of building parameters, under the assumption of maximum use of AC (i.e. without taking account varying occupant behaviour).

To examine the interaction between building envelope, climate and occupant behaviour, the report includes statistical analyses of circuit-level metering data and survey response data for participants in the Residential Building Energy Efficiency (RBEE) study. The analyses include an examination of the influence of indoor and outdoor temperature, time of day and day of the week on air-conditioner use. Separately, the influence of building characteristics, household demographics and self-reported behavioural factors on air-conditioner and energy use were also assessed.

Finally, to provide preliminary insight into air-conditioner uptake and use across Australia, summary results from an online survey of 700 people who participated through the Google Survey platform is presented. The opt-in survey includes self-report data on demographics, building characteristics, and air-conditioning usage frequency and decision making. Note that cross-tabulations of responses from the survey are available through EUDM (see the Related Resources listing on this page). Care should be taken in interpreting the results, as the data may include demographic skew.

Interested in more information about the NEAR Program? Sign up to learn more about the project.

  • Department of Industry, Science, Energy and Resources logo
  • CSIRO logo
  • AEMO logo