An important consideration for future age-friendly cities is that older people are able to live in housing appropriate for their needs. While thermal comfort in the home is vital for the health and well-being of older people, there are currently few guidelines about how to achieve this. This study is part of a research project that aims to improve the thermal environment of housing for older Australians by investigating the thermal comfort of older people living independently in South Australia and developing thermal comfort guidelines for people ageing-in-place. This paper describes the approach fundamental for developing the guidelines, using data from the study participants’ and the concept of personas to develop a number of discrete “thermal personalities”. Hierarchical Cluster Analysis (HCA) was implemented to analyse the features of research participants, resulting in six distinct clusters. Quantitative and qualitative data from earlier stages of the project were then used to develop the thermal personalities of each cluster. The thermal personalities represent different approaches to achieving thermal comfort, taking into account a wide range of factors including personal characteristics, ideas, beliefs and knowledge, house type, and location. Basing the guidelines on thermal personalities highlights the heterogeneity of older people and the context-dependent nature of thermal comfort in the home and will make the guidelines more user-friendly and useful.

Older people are often over-represented in morbidity and mortality statistics associated with hot and cold weather, despite remaining mostly indoors. The study “Improving thermal environment of housing for older Australians” focused on assessing the relationships between the indoor environment, building characteristics, thermal comfort and perceived health/wellbeing of older South Australians over a study period that included the warmest summer on record. Our findings showed that indoor temperatures in some of the houses reached above 35 °C. With concerns about energy costs, occupants often use adaptive behaviours to achieve thermal comfort instead of using cooling (or heating), although feeling less satisfied with the thermal environment and perceiving health/wellbeing to worsen at above 28 °C (and below 15 °C). Symptoms experienced during hot weather included tiredness, shortness of breath, sleeplessness and dizziness, with coughs and colds, painful joints, shortness of breath and influenza experienced during cold weather. To express the influence of temperature and humidity on perceived health/wellbeing, a Temperature Humidity Health Index (THHI) was developed for this cohort. A health/wellbeing perception of “very good” is achieved between an 18.4 °C and 24.3 °C indoor operative temperature and a 55% relative humidity. The evidence from this research is used to inform guidelines about maintaining home environments to be conducive to the health/wellbeing of older people.

Purpose: This paper presents the development of personal thermal comfort models for older adults and assesses the models’ performance compared to aggregate approaches. This is necessary as individual thermal preferences can vary widely between older adults, and the use of aggregate thermal comfort models can result in thermal dissatisfaction for a significant number of older occupants. Personalised thermal comfort models hold the promise of a more targeted and accurate approach.Design/methodology/approach: Twenty-eight personal comfort models have been developed, using deep learning and environmental and personal parameters. The data were collected through a nine-month monitoring study of people aged 65 and over in South Australia, who lived independently. Modelling comprised dataset balancing and normalisation, followed by model tuning to test and select the best hyperparameters’ sets. Finally, models were evaluated with an unseen dataset. Accuracy, Cohen’s Kappa Coefficient and Area Under the Receiver Operating Characteristic Curve (AUC) were used to measure models’ performance.Findings: On average, the individualised models present an accuracy of 74%, a Cohen’s Kappa Coefficient of 0.61 and an AUC of 0.83, representing a significant improvement in predictive performance when compared to similar studies and the “Converted” Predicted Mean Vote (PMVc) model.Originality/value: While current literature on personal comfort models have focussed solely on younger adults and offices, this study explored a methodology for older people and their dwellings. Additionally, it introduced health perception as a predictor of thermal preference – a variable often overseen by architectural sciences and building engineering. The study also provided insights on the use of deep learning for future studies.

Older people generally want to live in their own homes for as long as possible. This can be facilitated by home modifications, but improving the thermal quality of their living environment remains under-researched. This paper discusses the application of building performance simulation to explore strategies to improve the homes’ thermal environment and their effects on older people’s thermal comfort, with the aim of developing design guidelines. Indoor temperature data recorded hourly were used to calibrate simulation models of four sample houses. Six strategies that affect thermal comfort were tested based on the thermal preferences of six older adults’ personas. Results show significant differences in the effectiveness of the strategies depending on the thermal preferences of the occupants, the house types, the seasons, and the climate zones. The study highlights the importance of considering occupant diversity and contextual factors when using building simulation to explore design strategies.