## 7 Conclusions  
The study developed a system based on smart building and DT paradigms, integrating IoT components, automation flows for energy efficiency and machine learning algorithms for monitoring and predicting energy consumption and indoor environmental quality. Through real-time visualization of aggregated data in BIM models and interactive dashboards, the system provides an advanced tool for decision support, predictive management and optimization of energy resources and indoor environmental quality. The experimental application demonstrates the potential of this technology to improve energy efficiency and occupant comfort through continuous monitoring and the ability to make decisions based on real-time data. The results show that the integration of digital technologies in the building context can lead to a more sustainable and intelligent management of spaces, with significant benefits in terms of energy efficiency, air quality and environmental comfort. The experimental implementation of the system within the building demonstrated the effectiveness of the proposed methodology, confirming how the combined use of the IoT and DT enables detailed monitoring of environmental conditions and optimization of energy resources. ML, in particular, showed great potential in predicting future consumption and supporting more efficient operational decisions, thus contributing to sustainability and occupant comfort. At this research level, the results obtained represent an initial validation of the system, based on an initial campaign of measurements. Although they already demonstrate the effectiveness of the developed framework, further developments will be necessary to extend the application of the approach to the whole building. The proposed approach has some limitations that need to be addressed through further research. In order to test it in a building-scale application scenario, the DT prototype is currently being extended in terms of the number of rooms monitored, the different uses and the monitoring period. In addition, other platform services are being developed, such as the implementation of a web application for collecting user feedback, which can be used to explore the system's ability to identify problems and propose spatial solutions. As the quantity of data collected over time increases, the accuracy of predictions improves, enabling the formulation of sophisticated strategies for enhancing energy efficiency and environmental quality. The proliferation of sensors and the incorporation of additional functionalities will refine the predictive model, thereby enhancing the system's capacity to adapt to variations in usage and the requirements of occupants. The subsequent evolution of this technology will encompass the augmentation of the platform and the incorporation of additional data sources, thereby further consolidating digital building management strategies and promoting an increasingly sustainable and intelligent approach to the maintenance and use of working spaces [54; 55]. In conclusion, the project lays the foundations for further developments in the field of building management through digital tools, with the aim of extending these solutions to a wide range of buildings and contexts, as well as promoting a more innovative and sustainable approach to building management. Conceptualization, G.P., S.A. and F.M. ; methodology, G.P., S.A. and F.M. ; validation, G.P., S.A. and F.M. ; formal analysis, G.P., S.A. and F.M. ; investigation, G.P., S.A. and F.M. ; resources, G.P., S.A. and F.M. ; data curation, G.P., S.A. and F.M. ; writing--original draft preparation, G.P. and S.A.; writing--review and editing, G.P., S.A. and F.M. ; visualization, G.P., S.A. and F.M. ; supervision, G.P. ; funding acquisition, G.P. All authors have read and agreed to the published version of the manuscript. **Funding:** The work was funded by Sapienza University Research Call 2022--CUP B89J21032850001, project title: Automatic electrical microgrid management system through machine learning techniques.