  Improving the energy-efficiency of heating, ventilation, and air-conditioning
(HVAC) systems has the potential to realize large economic and societal
benefits. This paper concerns the system identification of a hybrid system
model of a building-wide HVAC system and its subsequent control using a hybrid
system formulation of learning-based model predictive control (LBMPC). Here,
the learning refers to model updates to the hybrid system model that
incorporate the heating effects due to occupancy, solar effects, outside air
temperature (OAT), and equipment, in addition to integrator dynamics inherently
present in low-level control. Though we make significant modeling
simplifications, our corresponding controller that uses this model is able to
experimentally achieve a large reduction in energy usage without any
degradations in occupant comfort. It is in this way that we justify the
modeling simplifications that we have made. We conclude by presenting results
from experiments on our building HVAC testbed, which show an average of 1.5MWh
of energy savings per day (p = 0.002) with a 95% confidence interval of 1.0MWh
to 2.1MWh of energy savings.
