Ontological Approach to Digital Twin for Building Energy Maintenance

The digital transformation in construction, focusing on BIM, is driving interest in building maintenance. Our study aims to develop and validate a predictive maintenance method using a building's digital twin, emphasizing energy equipment. Our approach involves creating a building ontology and integrating data using a graph-oriented database (GDB) for enhanced BIM interoperability. This enables predictive maintenance tools, including Fault Detection and Diagnosis (FDD), for energy installations. We identify maintenance queries to validate the approach, showing the ontology's capacity to address complex issues. While limitations exist, the GDB implementation and FDD methods suggest promising advancements in building maintenance.