AI-Based Semantic Digital Twin for Building Energy Optimization
The University of Nottingham
To develop an efficient approach to integrate GIS and BIM data that supports energy design at the urban scalein aweb based visualization environment.
Industrial Foundation Classes (BIM data schema) and CityGML (GIS data schema) will be investigated and translated into Ontology Web Langauage (OWL), and then RDF graphs will be developed. Schema mediation and semantic mapping will be performed and a IFC-CityGML Semantically integrated data model will be generated and input in a web based visualization system in ShareBIM for modelling, simulating and analysing building energy performance to be used a decision making tool by design engineers, policy makers, developers and urban planners in achieving more energy efficient designs.
• Demonstration of the feasibility of BIM and GIS data integration to facilitate urban energy design.
• Standard methodology/framework to achieve semantic integration of GIS and BIM data in the domain of urban energy design.
• Development of a ShareBIM-EnerGISweb based visualization system.