  We introduce Cohort Comfort Models, a new framework for predicting how new
occupants would perceive their thermal environment. Cohort Comfort Models
leverage historical data collected from a sample population, who have some
underlying preference similarity, to predict thermal preference responses of
new occupants. Our framework is capable of exploiting available background
information such as physical characteristics and one-time on-boarding surveys
(satisfaction with life scale, highly sensitive person scale, the Big Five
personality traits) from the new occupant as well as physiological and
environmental sensor measurements paired with thermal preference responses. We
implemented our framework in two publicly available datasets containing
longitudinal data from 55 people, comprising more than 6,000 individual thermal
comfort surveys. We observed that, a Cohort Comfort Model that uses background
information provided very little change in thermal preference prediction
performance but uses none historical data. On the other hand, for half and one
third of each dataset occupant population, using Cohort Comfort Models, with
less historical data from target occupants, Cohort Comfort Models increased
their thermal preference prediction by 8~\% and 5~\% on average, and up to
36~\% and 46~\% for some occupants, when compared to general-purpose models
trained on the whole population of occupants. The framework is presented in a
data and site agnostic manner, with its different components easily tailored to
the data availability of the occupants and the buildings. Cohort Comfort Models
can be an important step towards personalization without the need of developing
a personalized model for each new occupant.
