Automated and scalable BIM2BEM framework with zoning-based model simplification leveraging knowledge graph integration

Accurate and scalable generation of Building Energy Models (BEM) from Building Information Modelling (BIM) data is critical for performance-driven building design. However, existing methods are often constrained by data quality issues and rigid workflows, limiting automation. This paper proposes an automated and scalable BIM-to-BEM (BIM2BEM) framework enabled by knowledge graph integration, designed to support automation and scalability in model generation from imperfect BIM data. To manage model complexity, zoning-based mappings from BIM spaces to thermal zones are derived through multi-factor analysis of spatial relationships, functional usage, thermal load similarity, and HVAC configuration. Applied to a real-world complex building, the framework reduces simulation time by up to 70%, while maintaining energy use deviations within 3% and HVAC sizing variations up to 10%, compared with the full-model baseline. These findings indicate that the proposed framework can enhance BIM2BEM automation, supporting the scalable and flexible generation of simulation-ready models under practical data limitations. • Automated and scalable BIM2BEM framework with zoning-based simplification. • Graph-based representation of building spaces and systems. • Semantic-guided geometric processing for thermal zoning. • Zoning simplification reduces BEM simulation time by up to 70%. • Energy deviation 3% and HVAC sizing variation 10% across simplified BEM.