  Herein we report a multi-zone, heating, ventilation and air-conditioning
(HVAC) control case study of an industrial plant responsible for cooling a
hospital surgery center. The adopted approach to guaranteeing thermal comfort
and reducing electrical energy consumption is based on a statistical
non-parametric, non-linear regression technique named Gaussian processes. Our
study aimed at assessing the suitability of the aforementioned technique to
learning the building dynamics and yielding models for our model predictive
control (MPC) scheme. Experimental results gathered while the building was
under regular use showcase the final controller performance while subject to a
number of measured and unmeasured disturbances. Finally, we provide readers
with practical details and recommendations on how to manage the computational
complexity of the on-line optimization problem and obtain high-quality
solutions from solvers.

Systems and Control;Systems and Control