  The inter-temporal consumption flexibility of commercial buildings can be
harnessed to improve the energy efficiency of buildings, or to provide
ancillary service to the power grid. To do so, a predictive model of the
building's thermal dynamics is required. In this paper, we identify a
physics-based model of a multi-purpose commercial building including its
heating, ventilation and air conditioning system during regular operation. We
present our empirical results and show that large uncertainties in internal
heat gains, due to occupancy and equipment, present several challenges in
utilizing the building model for long-term prediction. In addition, we show
that by learning these uncertain loads online and dynamically updating the
building model, prediction accuracy is improved significantly.
