## 4 Case Study  
The aim of the project is to create a prototype, identified with some of the offices on the first floor of an office building, which is part of historical and institutional building in Rome, that will allow experimentation with digital technologies and processes oriented towards a real-time management and monitoring of parameters identified as relevant for the management and energy efficiency of buildings, the predictive maintenance of components, as well as the safety, use and enhancement of spaces according to the paradigms of the Smart Building and the DT. In general, the aim of the project is to implement real estate management models based on digital tools and methods oriented towards Building Information Modelling (BIM), and therefore, towards the valorization of the BIM survey and restitution that started in 2024 for all the buildings belonging to the entire compendium. Specifically, the data to be collected, monitored and controlled mainly concerns four types of domains:  
1. Energy management and efficiency;
2. Indoor air quality control;Predictive maintenance;  
4. Management and optimization of office space. The rooms selected are used as a meeting room, individual office and press area (Figure 2), and they are particularly significant as they represent the most potentially critical working environments in terms of energy efficiency, environmental quality and thermo-hygrometric comfort. Digitization strategy subsequently included the extension of the system to a larger number of monitored environments in order to collect a more representative sample of applications and to verify the effectiveness of DT in more complex and diverse operational contexts (Figure 3). This development is essential to ensure the scalability and adaptability of the system in real applications. In further extension of the system, it was applied to additional room types, including single offices, open-plan offices, and service rooms, which encompass buvette, toilets and printer areas. These new operational contexts are characterized by a marked heterogeneity of operating conditions, which introduces new design and management challenges that the DT must address to ensure effective automation and optimal resource management:  
Figure 3: Environments implemented in the DT. Figure 2: Prototype environments. * In individual offices, the main challenge lies in the customization of environmental comfort conditions. Occupants may have individual preferences in terms of parameters such as temperature, lighting and ventilation. The DT must therefore implement advanced predictive models to optimize the adjustment of these parameters while maintaining a balance between user satisfaction and energy consumption reduction. Machine learning systems, based on historical data analysis, can be used to identify individual preference patterns and dynamically adapt the operation of systems. * Open space offices have very different operational dynamics, with occupancy levels that can vary significantly throughout the day. In this context, it is necessary to implement strategies that balance the thermal and lighting comfort of several people at the same time. Computational models based on thermal and lighting simulations can be integrated into the DT to optimize the distribution of energy resources. In addition, the use of distributed sensors allows the system to monitor changes in occupancy in real time and adjust system settings for optimal energy efficiency. * Service environments, such as buvette, present a particular challenge in terms of intermittent use. In these spaces, peaks in occupancy occur at short intervals, requiring dynamic management of lighting and air conditioning systems. The DT can use presence detection algorithms and occupancy prediction models to activate systems only when necessary, minimizing energy waste. At the same time, the analysis of the collected data allows the detection of any anomalies in the operation of the systems and the planning of proactive maintenance interventions.