A Campus-Scale Digital Twin for Smart Environment Monitoring and Sustainability Management

The integration of sensing systems, Artificial Intelligence (AI), and data analytics technologies is enabling the development of digital twins for monitoring and managing complex built environments. University campuses represent suitable scenarios for the deployment and evaluation of these technologies due to their diversity of facilities, heterogeneous operational systems, and dynamic usage patterns. This paper presents the design and deployment of a campus-scale operational digital twin developed for the University of Alicante Smart Campus. The proposed environment integrates multiple data sources including indoor environmental sensors, electricity and water consumption monitoring systems, photovoltaic generation data, irrigation networks, and WiFi connectivity information used as a proxy for occupancy estimation. Through a continuously updated and spatially synchronized digital representation of the campus, the platform supports environmental comfort monitoring, occupancy analysis, environmental quality assessment, anomaly detection, alert generation, and AI-enabled analytical services. A distinguishing characteristic of the proposed approach is its long-term real-world deployment. The system currently manages several years of historical data comprising hundreds of millions of time-series measurements collected from distributed monitoring systems across the campus. The results demonstrate the feasibility of maintaining a campus-scale digital twin operating as an institutional monitoring and decision-support environment.