A deep learning approach to personal thermal comfort models for an ageing population

Abstract

Recent years have shown an increasing number of studies on personal thermal comfort models as an alternative to the conventional approach to understanding thermal comfort in the built environment. Instead of basing on an average response from a large population, personalized models are designed to predict individuals’ thermal comfort responses, using a person’s direct feedback and personal characteristics as calibration inputs. However, personal comfort models have mainly used data from office environments and healthy younger adults. Studies on personal comfort models that focus on older people and dwellings are still absent in the literature. Nonetheless, considering the worldwide changing climate, the ageing population and older people’s heterogeneity in terms of intrinsic capacities and needs, personalized models could be the most appropriate path towards recognizing diversity and predicting individual thermal preferences in a more accurate way. This paper shows examples of personal comfort models, using deep learning algorithms and environmental and personal characteristics as inputs, derived from an on-going study that monitored people aged 65 and over in South Australia who live at home. The results have so far indicated that, on average, the individualised models improved the predictions by 69% when compared to traditional models.