Digital Twin of a College Campus: Design, Implementation and Use Cases

Abstract

A Digital Twin (DT) is a dynamic, continuously synchronized virtual model that mirrors a real physical environment, enabling real-time monitoring, analysis, and decisionmaking. Within a campus ecosystem, a DT can integrate diverse systems—such as Internet of Things (IoT) devices, Building Information Modeling (BIM), Geographic Information Systems (GIS), and academic resources—to improve sustainability, operational efficiency, and user experience. This paper presents a detailed framework and working prototype of a Digital Twin for a college campus, highlighting its design, implementation, and applications. The proposed model unifies multiple data streams through IoT sensors, data analytics, and visualization dashboards to support predictive maintenance, anomaly detection, and energy forecasting. A campus-scale prototype was developed and evaluated, achieving up to 12

Introduction

The text presents a comprehensive study on the design, implementation, and validation of a campus-scale Digital Twin (DT) system. Originally developed for industrial applications, Digital Twin technology is adapted here to smart campus infrastructure, where interconnected systems such as buildings, energy networks, sensors, and academic operations can be monitored and optimized through real-time data synchronization.

The paper proposes a modular, multi-layer DT architecture that integrates IoT sensors, communication and edge computing, hybrid data storage with semantic modeling, advanced analytics and simulation, and interactive visualization and control dashboards. This architecture enables real-time monitoring, predictive energy management, anomaly detection, predictive maintenance, and “what-if” scenario simulations, including evacuation planning and load balancing.

Key contributions include the integration of heterogeneous data sources (IoT, BIM, GIS, and academic platforms), development of intelligent analytics modules (hybrid energy forecasting, anomaly detection, and predictive maintenance), experimental validation using real and synthetic campus data, and discussion of challenges related to scalability, privacy, and interoperability.

The system employs hybrid modeling techniques that combine physics-based and machine-learning approaches for energy forecasting, unsupervised methods for anomaly detection, machine-learning classifiers for predictive maintenance, and optimization-driven simulations for operational control. A functional prototype was deployed across a small campus with three buildings, demonstrating end-to-end data acquisition, analytics, and visualization using low-cost IoT hardware and a modular software stack.

Overall, the work addresses limitations of prior building-level DT studies by proposing a unified, campus-wide Digital Twin framework capable of supporting intelligent decision-making, sustainability goals, and efficient campus operations.

References

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Copyright

Copyright © 2026 Samruddhi Shewale, Bhumika Shetty, Radha Tripathi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.