Building a Smart Campus Digital Twin: System, Analytics and Lessons Learned From a Real-World Project

• Published in: IEEE internet of things journal Vol. 11; no. 3; p. 1
• Main Authors: Roda-Sanchez, Luis, Cirillo, Flavio, Solmaz, Gurkan, Jacobs, Tobias, Garrido-Hidalgo, Celia, Olivares, Teresa, Kovacs, Erno
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-02-2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 5G mobile communication; Actuators; Anomalies; anomaly detection; Biological system modeling; Buildings; Cloud computing; Colleges & universities; Data analysis; Digital Twin; Digital twins; Edge computing; Energy consumption; HVAC; Intelligence; Internet of Things; Knowledge representation; Machine Learning; predictions; Predictive models; real-world projects; Semantics; Sensors; smart campus; Smart Cities; Solar energy
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2023.3300447

Smart solutions increasingly involve the use of sensor data to represent the physical world in the digital world and apply intelligence to such representation. The main approach is a vertical end-to-end solution from sensors to intelligence and back to actuators. Recently, a new holistic approach, the so-called Digital Twin, has emerged. This goes beyond traditional smart solutions by replicating, with high fidelity cross-domain aspects, a physical object into the digital world. The key differentiation is the inclusion of semantics information in the digital replica in a form of knowledge graph. Further, analytics continuously enrich Digital Twin's information with predictions and insights. In this article, we adopt the Digital Twin approach for the creation of a digital replica of Espinardo's campus at the University of Murcia (Spain). The starting point is the existing sensor network deployment and the infrastructure under development of Fog-Edge-Cloud computing based on a 5G private network. The smart campus Digital Twin is formed by the Digital Twins of 23 buildings for which different sets of data features have been thoroughly selected. We implement the concept of Digital Twin by merging sensor data with external open data sources, analytics models implemented and information processed by these analytics. We report our experience showing the issues encountered handling the data and producing various analytics models for predicting energy consumption, building occupancy, room usage, solar energy, and anomaly detection. From our experience, we highlight some lessons learned and directions towards the full operational smart campus Digital Twin.