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.

预测控制器可以大大提高建筑物中能源系统的性能。这些控制器的一个重要挑战是需要足够精确和简单的建筑模型来进行优化。灰盒模型是一种流行的方法，但是可靠的灰盒模型的识别受参数估计过程（尤其是多区域模型）的复杂性影响。因此，通常使用单区域模型，从而限制了预测控制器的性能和适用性。本文研究了识别多区域灰箱建筑模型的可行性以及在预测控制中使用这些模型的好处。为此，参数估计过程按各个区域划分，以获得有根据的初始猜测。BOPTEST框架中的虚拟测试用例可用于评估仿真和控制性能。结果表明，在多区域建筑中对区域之间的热相互作用进行建模是有意义的。