Despite the topicality of the paper, the novelty/originality of the manuscript is not much evident. Moreover, while the modelling part is accurately described and discussed, little attention is paid to the energy aspect. Authors are invited to address these problems.

Two major problems of the paper are the following:

1. While the modelling/mathematical point of view is accurately described and commented, little attention is paid to the energy aspect.
2. The paper merges together two previous publications of the authors ([23] and [24]). The methodology is an integration of distributed and hierarchcal MPC which have been previously discussed by the authors and the novelty/originality is not much evident. Even some figures are the same.

Authors are invited to address these two major problems. Moreover, proofreading is recommended. The manuscript must be adapted to the ECOS template.

Despite the topicality of the paper, the novelty/originality of the manuscript is not much evident. Moreover, while the modelling part is accurately described and discussed, little attention is paid to the energy aspect. Authors were invited to address these major problems.

• Ana María Blanco

• Martina Capone

Buildings contribute to approximately 30 % of the global energy consumption, which renders their energy-efficient control an effective measure to reduce overall energy consumption. This paper presents a hierarchical distributed Model Predictive Control (MPC) for building energy systems based on nonlinear Modelica controller models. It combines hierarchical and distributed optimization approaches to split the optimization complexity within the temporal and spatial dimensions. The hierarchical optimization approach considers different dynamics in complex building energy systems and preserves both anticipation for systems with high inertia and reactivity with regard to errors in the forecasting of disturbance quantities. The distributed optimization approach divides the centralized optimization problem into subproblems to improve scalability and adaptability of the control framework. The subproblems are solved in a parallel and iterative manner and account for both thermal (thermal transfer over zone boundaries) and hydraulic inter-zone coupling (induced by a central, shared Heating, Ventilation and Air Conditioning (HVAC) system). Particular focus of the control approach is placed on robustness with respect to errors in the forecasting of the disturbances impacting the building dynamics. The control performance of the proposed MPC framework is evaluated in a simulative case study of heating and shading control of a nonlinear six-room-building Modelica model, which is exposed to different forecast accuracies for the disturbances of ambient temperature, solar radiation and occupancy. The case study exhibits the benefits of the control framework in terms of energy consumption, thermal discomfort and computation time in comparison to a reference control concept of a non-hierarchical MPC configuration.