  Smart thermostats are one of the most prevalent home automation products.
They learn occupant preferences and schedules, and utilize an accurate thermal
model to reduce the energy use of heating and cooling equipment while
maintaining the temperature for maximum comfort. Despite the importance of
having an accurate thermal model for the operation of smart thermostats, fast
and reliable identification of this model is still an open problem. In this
paper, we explore various techniques for establishing a suitable thermal model
using time series data generated by smart thermostats. We show that Bayesian
neural networks can be used to estimate parameters of a grey-box thermal model
if sufficient training data is available, and this model outperforms several
black-box models in terms of the temperature prediction accuracy. Leveraging
real data from 8,884 homes equipped with smart thermostats, we discuss how the
prior knowledge about the model parameters can be utilized to quickly build an
accurate thermal model for another home with similar floor area and age in the
same climate zone. Moreover, we investigate how to adapt the model originally
built for the same home in another season using a small amount of data
collected in this season. Our results confirm that maintaining only a small
number of pre-trained thermal models will suffice to quickly build accurate
thermal models for many other homes, and that 1~day smart thermostat data could
significantly improve the accuracy of transferred models in another season.
