  This paper presents a novel architecture for model predictive control (MPC)
based indoor climate control of multi-zone buildings to provide energy
efficiency. Unlike prior works we do not assume the availability of a
high-resolution multi-zone building model, which is challenging to obtain.
Instead, the architecture uses a low-resolution model of the building which is
divided into a small number of "meta-zones" that can be easily identified using
existing data-driven modeling techniques. The proposed architecture is
hierarchical. At the higher level, an MPC controller uses the low-resolution
model to make decisions for the air handling unit (AHU) and the meta-zones.
Since the meta-zones are fictitious, a lower level controller converts the
high-level MPC decisions into commands for the individual zones by solving a
projection problem that strikes a trade-off between two potentially conflicting
goals: the AHU-level decisions made by the MPC are respected while the climate
of the individual zones is maintained within the comfort bounds. The
performance of the proposed controller is assessed via simulations in a
high-fidelity simulation testbed and compared to that of a rule-based
controller that is used in practice. Simulations in multiple weather conditions
show the effectiveness of the proposed controller in terms of energy savings,
climate control, and computational tractability.
