## 1 Introduction  
With regard to climate change and its effects, it is an important task to limit the global temperature rise to 1.5 K, which requires the increase of energy efficiency and the decrease of the total energy consumption by 2050, according to [1]. Accounting for shares of the global final energy consumption in different sectors shows that the building sector accounts for 30% [2]. Therefore, a significant part of the solution to achieve improved efficiency lies in applying intelligent control to building automation.  
The challenge to reduce energy usage and increase efficiency in the building sector has gained and continues to gain wide scientific interest. Intelligent control of buildings, especially, has been a major research topic in recent years [3, 4]. Within this research area, _model predictive control_ (MPC) for buildings significantly gained importance during the past few years and is well established in current research [3]. [PERSON] et al. [5] estimated possible energy savings of 15% to 20% and a peak load shifting potential of around 30%  
Methodologically, only few publications consider real-life applications [5]; most publications focus on method development using simulations [3, 4].  
Moreover, major parts of studies consider applications with relatively complex systems under control and focus on single buildings or a more abstract view of a district [3; 5; 6].  
To summarize, important and interesting research questions arise with the evaluation of automation within a larger-scale living-lab scenario. This especially holds true for several aspects, such as real-life control reliability, user acceptance, or a long-term evaluation of user interactions. Furthermore, the technical integration into existing infrastructure as well as into _information and communication technologies_ (ICT) infrastructure for scientific purposes is an active field of research [7; 8]. This part will become even more important when the different control layers, ranging from the district level through the building level and down to the room level, are included along with their interactions.  
In the _Living Lab Energy Campus_ (LLEC), we address the set of questions described above. Within this paper, the main focus is on aspects at the room level and to show options for equipping rooms with sensors and actuators, to gain better knowledge and realize the possibility of controlling energy usage as close to the user as possible. We describe workflow examples for enabling smart buildings in such a real-life laboratory. These are designed in a way to not only integrate new buildings, but to consider especially refurbishment measures to include existing buildings. Based on this, we offer insights on samples from the first set of measurement data.  
The remainder of the document is structured as follows. First, we give a rough overview of the literature regarding the monitoring, automation, and equipping of buildings in Section 2. Additionally, we list and compare different communication protocols and summarize the literature on existing living labs. In Section 3, we show the criteria and the methodology for selecting the building set to be incorporated as well as the buildings' main characteristics. Moreover, we present a requirement analysis on the technical equipment and describe the variants selected and implemented in detail. Based on this, we describe the workflows we follow to equip buildings effectively in Section 4. In Section 5, we discuss our results. Finally, we give a conclusion and an outlook for the next steps in Section 6.