  Standard (black-box) regression models may not necessarily suffice for
accurate identification and prediction of thermal dynamics in buildings. This
is particularly apparent when either the flow rate or the inlet temperature of
the thermal medium varies significantly with time. To this end, this paper
analytically derives, using physical insight, and investigates linear
regression models with nonlinear regressors for system identification and
prediction of thermal dynamics in buildings. Comparison is performed with
standard linear regression models with respect to both a) identification error,
and b) prediction performance within a model-predictive-control implementation
for climate control in a residential building. The implementation is performed
through the EnergyPlus building simulator and demonstrates that a careful
consideration of the nonlinear effects may provide significant benefits with
respect to the power consumption.
