Identification of multi-zone grey-box building models for use in model predictive control

Predictive controllers can greatly improve the performance of energy systems in buildings. An important challenge of these controllers is the need of a building model accurate and simple enough for optimization. Grey-box modelling stands as a popular approach, but the identification of reliable grey-box models is hampered by the complexity of the parameter estimation process, specifically for multi-zone models. Hence, single-zone models are commonly used, limiting the performance and applicability of the predictive controller. This paper investigates the feasibility of the identification of multi-zone grey-box building models and the benefits of using these models in predictive control. For this purpose, the parameter estimation process is split by individual zones to obtain an educated initial guess. A virtual test case from the BOPTEST framework is contemplated to assess the simulation and control performance. The results show the relevance of modelling thermal interactions between zones in the multi-zone building.