## 2 Literature  
### Monitoring and Controlling Real-Life Buildings  
In buildings, multiple energy-related services (such as heating) are required to create a comfortable indoor climate. The apparatus to supply such services is often indicated as a _building automation and control system_ (BACS). A BACS encompasses all technical components, including software, that are necessary to obtain information on and control a building's indoor climate [9]. The most vital parts of a BACS comprise _heating_, _ventilation_, _and air conditioning_ (HVAC) components, as well as lighting and shading applications [10]. Sources such as [11] give an overview of those techniques and a broad set of standards are formulated to provide guidelines for proper design and application, such as [12; 13; 14; 15]. Over the past decades, the understanding of BACS evolved from numerous, individual systems that serve a dedicated purpose (e.g., heating a single room), to integrated BACS systems that manage building-wide energy use, applying high-level controllers [16]. A very promising approach to building-wide control is model-based control, or _model predictive control_ (MPC) [3]. MPC applies models to predict system behavior, receiving continuous feedback in the form of measurements from the real-life building [17; 18]. Thus, MPC usually requires detailed knowledge of the current building state and its environment, as well as the ability to influence the building [19; 20]. To model BACS components, building envelopes, or other energy system components, different approaches exist [6]. The so-called _white-box_ models are based on the principal physical correlations which characterize the system. These first-principle models are usually derived from in-depth knowledge of the system. On the contrary, _black-box_ models rely fully on empirical relations to depict a system's behavior, which is why they are also referred to as _data-driven_ models. Data-driven models are derived from measurements obtained from a real-life system, and range from simple curves (e.g., for production efficiency), to modern _artificial neural network_ (ANN) or _long short-term memory_ (LSTM) modeling approaches [21]. Being a mixture of white-box and black-box models, _gray-box_ models employ features of both modeling paradigms. Gray-box models usually incorporate important underlying physics, while the model's parameters are fitted to real-life data [5]. Further information on modeling and MPC can be found in [3; 5; 6]. Applying MPC to building HVAC systems such as _air handling units_ (AHU) or _variable air volume_ (VAV) systems is an active field of research. In the most common approach for existing buildings, the current BACS is interfaced with the model-based controller via a dedicated interface [17; 18; 22; 23; 24; 25; 26; 27]. For example the authors in [18; 23] implement their model-based controller in MATLAB (Registered trademarks and proprietary names are not generally indicated as such in this publication. However, the absence of such indications does not imply that these names belong to the public domain with respect to trademark or copyright law.) [28], interfacing the BACS via its energy management tool, WebCTRL [29]. While [PERSON] et al. [24] also use the WebCTRL interface for implementing set-points, they retrieve sensor data by sending and receiving BACnet [30] telegrams on the communication bus directly. In [31], the so-called Building Controls Virtual Test Bed (BCVTB) is used to connect a controller and a BACS. In other cases, where no BACS exists or where one cannot be used, building automation components are addressed directly for the implementation of MPC set-points [32; 33; 34; 35; 36]. Such studies are usually limited to smaller buildings [32; 34], a subset of thermal zones [33; 35], or individual rooms [36]. However, the density of sensors and actuators deployed at the room level tends to be higher.