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There have been much research focus on modelling various types of occupant thermal adaptive behaviour with an aim to improve the reliability of building energy performance simulation (BEPS) tools. However, most existing studies exploring occupant interaction with residential building systems limit their scope to a single, isolated behaviour (e.g. window opening only), which fails to consider that occupant reactions are determined by the interconnected effects of series of adaptive actions. This study aimed to develop a stochastic window operation model to address the interdependency between occupant's window and A/C operation. Longitudinal field observations were conducted in a sample of 41 Brisbane region households over a one-year period. The sample households' occupancy status, indoor environmental conditions, use of windows and air-conditioning (A/C) were continuously recorded. The cut-off temperature beyond which the probability of windows being open begins to decrease with an increase of the outdoor temperature, was found to be approx. 27 °C in both living and sleeping areas within the monitored households. This paper proposes a window operation model that captures diverse window opening patterns observed amongst the sample households, with a particular focus on the effect of A/C operation state on window opening and closing actions. The proposed model was tested by running 1000 simulations on a validation dataset. A good agreement observed between 1000 simulations and actual window use patterns indicates that the model can be applied to dynamic building energy performance and indoor environment simulations. • Cut-off temperature was found to be approx. 27 °C in both living and sleeping areas. • Effect of A/C operation state on window opening and closing actions was quantified. • The model creates diverse window patterns observed amongst the 41 sample households. [ABSTRACT FROM AUTHOR]

Improving building energy efficiency is a cost-effective response to global warming. It can result in the reduction in energy consumption and related carbon emissions of buildings. At the same time, it is normally effective in reducing heat/cold risk in non-air-conditioned buildings. This study investigated the potential heat stress risks in non-air-conditioned dwellings during the heatwaves, such as those occurred in Brisbane in 2004 and Melbourne in 2009. Using building simulations, it was demonstrated that maladaptation by over-emphasizing building energy efficiency alone could expose occupants to greater health risks during heatwaves. In general, measures that can further reduce both the space cooling load and the total space heating and cooling (H/C) load, instead of the total H/C load only, are effective both in improving the energy performance and in reducing occupant exposure to heat stress risks. [ABSTRACT FROM AUTHOR]

Assessment criteria for thermal comfort in residential buildings are often defined on the basis of the adaptive comfort model due to their typical operational characteristics – i.e. mixed-mode or naturally ventilation. However, recent field studies demonstrate that the current adaptive comfort model prescribed in the international standard does not necessarily provide appropriate comfort criteria for residential buildings, since its occupants may be less thermally sensitive due to enhanced adaptability and greater tolerance of temperature variation. This paper examined perception of thermal comfort and behavioural adaptation of residents in two cities on the east coast of Australia – Sydney and Brisbane. With an aim to develop a method of deriving acceptable temperature ranges for different climate characteristics, statistical analyses were performed on two sets of longitudinal field study data collected in the two study regions. The statistical analysis estimates the acceptable temperature range for the two cities at around 11 K, which is 4 K wider than that prescribed by the ASHRAE's adaptive comfort model. The results also indicate that the 80% acceptability range can shift according to the climate, with 1.5 K difference being observed between the two study regions. The findings of this study can help climate-responsive design of houses, hence allowing the design to more closely meet occupants' comfort needs. • Residential thermal comfort and behavioural adaptation in two cities are presented. • Acceptable temperature range was 4 K wider than the ASHRAE's adaptive comfort model. • Acceptable temperatures can shift according to the climate characteristics. • Findings of this study can help designing houses to meet occupants' comfort needs. [ABSTRACT FROM AUTHOR]

Studies that attempt to model real patterns of residential occupant behaviour have become increasingly popular over the last decade. Most research of this kind tend to produce behaviour profiles based on an average occupant, yet the prediction of residential energy consumption varies significantly according to behavioural profiles of individual households. Oftentimes, averaged behavioural profiles are inadequate for predicting energy demands accurately, therefore requiring models to incorporate the diversity of occupants' behaviours. The main objective was to develop an air-conditioning (A/C) usage model that produces diverse A/C cooling and heating operation patterns in residential settings. Longitudinal field observations were conducted in 41 Brisbane region households of Australia over a one-year period. Regression coefficients in the model were set to be drawn from a normal distribution to reproduce observed variability in A/C operation patterns amongst the households. Simulations were performed on a validation dataset to test the predictive skill of the model. The result indicated that the frequency and duration of A/C usage observed in the validation dataset well matched with the prediction made by the model. The proposed model could be incorporated in building energy simulation tools to predict realistic energy use for A/C in a given residential building design. • A/C operation model is developed based on field observations in 41 households. • The model creates observed variability in A/C operation patterns amongst households. • The model is beneficial for risk analysis and dynamic environmental simulations. [ABSTRACT FROM AUTHOR]

[Display omitted] Pre-cooling is a method to shift peak air conditioning demand to off-peak and shoulder hours by utilizing energy storage in the thermal mass of buildings. This paper evaluates the pre-cooling potential of a range of residential buildings broadly representative of the Australian housing stock. Pre-cooling in 2-, 6-, and 8-star buildings with light, medium, and heavy construction weights in the cities of Adelaide, Brisbane, Melbourne, and Sydney is simulated, and its impacts on household bills, thermal comfort and peak air-conditioning demand reduction are assessed. Star rating is the method for rating Australian buildings based on their annual heating and cooling demand. The housing assessment by both star rating and construction weight highlights the varied potential of pre-cooling. Pre-cooling sensitivity to air conditioning size is also evaluated by simulating 5 k W th , 15 k W th , and 25 k W th air conditioning sizes representing typical small, medium, and large units in the Australian air conditioning market. Using an adaptive thermal comfort model, the impact of people's thermal comfort expectations on pre-cooling is also considered. The results highlight that compared with 2-star buildings, the percentage of daily cost savings from pre-cooling is considerably higher in 6-star and 8-star buildings. Moreover, with a small air conditioner, 2-star buildings are prone to negative seasonal cost savings as the air conditioner is not able to achieve thermostat temperature settings and hence runs longer. However, pre-cooling does offer considerable discomfort reduction to household members. A maximum summer seasonal cost savings of around $260 is observed in a 6-star light-weight building in Adelaide, and a 2-star light-weight building in Brisbane. Households that accept wider temperature variations with pre-cooling may not necessarily see cost savings. These households do provide greater peak air conditioning demand reductions. In conclusion, the value of pre-cooling for households and network peak demand reduction depends significantly on the housing type and location but can be highly valuable in many cases. [ABSTRACT FROM AUTHOR]