Optimizing energy simulation processes through an ontology-based approach

Building Energy Modelling (BEM) traditionally faces significant interoperability challenges due to manual model simplifications and the fragmented nature of data sources, needed in the modelling process. This paper presents SPHEERE, an innovative framework for ontology-based semantic integration that automates the translation of Industry Foundation Classes (IFC) into a temporary energy-oriented model. The methodology defines an Energy Information Model (EIM) to identify parameters across seven categories, only 40% of which are natively present in IFC. We utilize a layered ontological stack, including ifcOWL, BOT, SAREF4BLDG, and BRICK, to generate a federated Knowledge Graph (KG). This KG is further enriched through semantic derivation and external integration of climatic and operational data, creating a queryable "lean digital twin" without the need for a dedicated, isolated BEM. The framework is validated via a case study involving federated as-is and to-be retrofit models. Results demonstrate that SPARQL queries can successfully retrieve parameters that are relevant to drive the modelling process. This approach preserves the original BIM as the single source of truth while providing a transparent, auditable infrastructure for building performance modelling and optimization. The contribution lies in bridging the gap between geometric modeling and physics-grounded energy analysis through machine-interpretable semantic views.