Towards an understanding of comfort and wellbeing of older people using occupant centric models

The worldwide demographic trend of an ageing society has important implications in the built environment. As the ageing population increases, designers are increasingly challenged to manage the diverse needs of older citizens so that health and quality of life are improved and independent living – or “ageing in place” - can be guaranteed.

In this context, since older people’s individual differences are excessively wide, considering them as a single and uniform population can lead to misleading conclusions. Consequently, this study aims to investigate relationships between thermal comfort and wellbeing of older people on an individualized level, by developing occupant centric and data driven comfort models using machine learning algorithms, as opposed to the generalized static models traditionally used today.

This approach seeks to enable more adequate design guidelines and thermal conditioning management for older people’s built environment, which could help decrease health-related vulnerability, enhance wellbeing, minimize reliance on heating and cooling, reduce energy use and ultimately diminish fuel poverty.