Maddalena, Emilio T.Mueller, Silvio A.dos Santos, Rafael M.Salzmann, ChristopheJones, Colin N.2022-10-102022-10-102022-10-102022-09-1510.1016/j.enbuild.2022.112316https://infoscience.epfl.ch/handle/20.500.14299/191319WOS:000853693200004Herein 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. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Construction & Building TechnologyEnergy & FuelsEngineering, CivilConstruction & Building TechnologyEnergy & FuelsEngineeringhvac systemsmodel predictive controlgaussian processesdata -driven methodsbayesian calibrationenergy modelsoptimizationtext::journal::journal article::research article