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A Semantic Data-Driven Digital Twin for Real-Time Monitoring and Control in a University Living Lab Nanogrid
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A Semantic Data-Driven Digital Twin for Real-Time Monitoring and Control in a University Living Lab Nanogrid
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Journal Article
A Semantic Data-Driven Digital Twin for Real-Time Monitoring and Control in a University Living Lab Nanogrid
2026
Overview
The electrification of end uses and the rapid deployment of distributed energy resources are transforming buildings into active participants in grid-interactive energy systems. Unlocking this potential requires digital infrastructures capable of harmonizing fragmented telemetry, ensuring interoperability, and embedding flexibility attributes within operational control. This paper presents the development and validation of a semantic digital twin for the University of Cyprus (UCY) nanogrid, a living lab integrating photovoltaics, battery storage, electric vehicle charging, centralized cooling, smart calorimeters, and weather monitoring under supervisory SCADA management. The proposed methodology employs a layered semantic pipeline that combines Brick Schema, ASHRAE Standard 223P, the Energy Flexibility Ontology (EFOnt), and the Resistance -Capacitance Ontology (RCOnt). Brick Schema and ASHRAE 223P provide structural and device-level metadata, EFOnt formalizes controllability and service participation, and RCOnt encodes simplified 1R1C thermal models linked to building zones. Together, these annotations transform heterogeneous data streams into a coherent, machine-interpretable knowledge graph. Results demonstrate that subsystem diversity can be systematically unified into a queryable and extensible representation that preserves structural detail, characterizes flexibility potential, and incorporates thermal dynamics for predictive applications. The case study confirms the feasibility of implementing ontology-driven digital twinning in a campus nanogrid, highlighting its value for asset discovery, consistency validation, and integration with supervisory control strategies. The study establishes a reproducible foundation for scaling semantic twins to community infrastructures. Ongoing work focuses on calibrating the embedded thermal models with operational data to enable their use in supervisory predictive control, advancing semantic twins as active enablers of flexibility-oriented energy management.
