Developing occupant centric models to better understand the thermal comfort and wellbeing of older Australians

  • YEAR
    Martins, Larissa Arakawa
    Soebarto, Veronica
    Williamson, Terence
    Pisaniello, Dino
    2019 Conference Papers
    Building Performance Evaluation
    Conference Papers


The worldwide demographic trend of an ageing society has important design implications for the built environment. Older people’s individual differences and intrinsic capacities are very wide and undifferentiated consideration of this population is problematic, from both comfort and concomitant wellbeing perspectives. With respect to thermal comfort in dwellings, the traditional approach may
result in a significant proportion of older occupants experiencing thermal discomfort, and subsequent
health issues. This study reports on the development of an alternative to the generalized static thermal
comfort models. The approach utilises occupant-centric and data-driven models with deep learning
algorithms. Better focussed models will facilitate design guidelines for older people’s built environment
that respond more directly to their needs, helping to decrease thermally-related vulnerability, enhance
well-being, as well as minimizing reliance on heating and cooling and reducing energy use.

Keywords: Thermal comfort, personal comfort model, machine learning, older people.


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