  Understanding the models that characterize the thermal dynamics in a smart
building is important for the comfort of its occupants and for its energy
optimization. A significant amount of research has attempted to utilize
thermodynamics (physical) models for smart building control, but these
approaches remain challenging due to the stochastic nature of the intermittent
environmental disturbances. This paper presents a novel data-driven approach
for indoor thermal model inference, which combines an Autoregressive Moving
Average with eXogenous inputs model (ARMAX) with a Normalized Mutual
Information scheme (NMI). Based on this information-theoretic method, NMI,
causal dependencies between the indoor temperature and exogenous inputs are
explicitly obtained as a guideline for the ARMAX model to find the dominating
inputs. For validation, we use three datasets based on building energy
systems-against which we compare our method to an autoregressive model with
exogenous inputs (ARX), a regularized ARMAX model, and state-space models.
