  Thermal preferences vary from person to person and may change over time. The
main objective of this paper is to sequentially pose intelligent queries to
occupants in order to optimally learn the indoor air temperature values which
maximize their satisfaction. Our central hypothesis is that an occupant's
preference relation over indoor air temperature can be described using a scalar
function of these temperatures, which we call the "occupant's thermal utility
function". Information about an occupant's preference over these temperatures
is available to us through their response to thermal preference queries :
"prefer warmer," "prefer cooler" and "satisfied" which we interpret as
statements about the derivative of their utility function, i.e. the utility
function is "increasing", "decreasing" and "constant" respectively. We model
this hidden utility function using a Gaussian process prior with built-in
unimodality constraint, i.e., the utility function has a unique maximum, and we
train this model using Bayesian inference. This permits an expected improvement
based selection of next preference query to pose to the occupant, which takes
into account both exploration (sampling from areas of high uncertainty) and
exploitation (sampling from areas which are likely to offer an improvement over
current best observation). We use this framework to sequentially design
experiments and illustrate its benefits by showing that it requires drastically
fewer observations to learn the maximally preferred temperature values as
compared to other methods. This framework is an important step towards the
development of intelligent HVAC systems which would be able to respond to
occupants' personalized thermal comfort needs. In order to encourage the use of
our PE framework and ensure reproducibility in results, we publish an
implementation of our work named GPPrefElicit as an open-source package in
Python.
