IFC-based Energy Consumption Estimation

The building sector remains one of the largest contributors to global energy consumption and greenhouse gas emissions, accounting for a substantial share of both operational and embodied carbon across the lifecycle of built assets. Within this context, Building Energy Performance Simulation (BEPS) has become an essential tool for evaluating energy demand, optimizing design decisions, and supporting carbon reduction strategies. Building Information Modeling (BIM), particularly through open standards such as Industry Foundation Classes (IFC), provides a promising foundation for integrating design and performance analysis. However, despite the theoretical interoperability between BIM and Building Energy Modeling (BEM), the practical realization of seamless BIM-to-BEM transformation remains limited. Current workflows often require extensive manual intervention, leading to inefficiencies, inconsistencies, and reduced reliability of simulation outputs.

Recent studies have demonstrated meaningful progress toward automating BIM-to-BEM workflows, particularly through the use of IFC and gbXML as data exchange standards. Several frameworks report geometric translation accuracies exceeding 90% and reductions in modeling time by up to an order of magnitude compared to manual processes. Tools such as EnergyPlus, OpenStudio, and Modelica-based environments have been successfully integrated into semi-automated pipelines, enabling more efficient generation of simulation-ready models. Nevertheless, the literature consistently identifies persistent challenges, including geometric inconsistencies, missing elements, and significant discrepancies in simulation results, sometimes varying by several orders of magnitude across different workflows. These findings indicate that while geometric interoperability has improved, it alone is insufficient to ensure reliable energy modeling. A more critical limitation lies in the incomplete transfer and representation of semantic and thermophysical data. Many BIM models are developed primarily for design coordination rather than performance analysis, resulting in missing or inadequate metadata such as material properties, HVAC configurations, occupancy schedules, and thermal zone definitions. This gap necessitates manual enrichment during the BEM preparation phase, introducing subjectivity and limiting reproducibility. Furthermore, semantic information loss during data exchange undermines the fidelity of simulation models, as energy performance predictions are highly sensitive to non-geometric parameters. Existing studies confirm that current workflows either assume the presence of complete data which is rarely the case in practice or rely on labor-intensive manual input, thereby constraining scalability and automation potential.

In response to these challenges, recent research has emphasized the importance of standardized data validation and enrichment mechanisms. Information Delivery Specifications (IDS), introduced by buildingSMART as a formal standard for defining and verifying IFC data requirements, represent a promising approach to improving data consistency and interoperability. However, adoption remains limited, with only a small number of studies explicitly implementing IDS-based validation frameworks. At the same time, automated semantic enrichment where missing energy-relevant properties are systematically populated from standardized databases remains largely unexplored. The absence of integrated validation and enrichment workflows continues to hinder the development of fully automated, reliable BIM-to-BEM pipelines capable of supporting large-scale applications and diverse building typologies.

Another critical gap in the literature is the lack of domain-specific workflows, particularly for commercial facilities such as healthcare buildings. These buildings are among the most energy-intensive building types due to their continuous operation, stringent environmental controls, and complex HVAC requirements. Despite this, existing BIM-to-BEM studies have predominantly focused on residential and commercial office buildings, with limited attention given to healthcare contexts. Additionally, while operational energy modeling has been widely explored, the integration of carbon assessment, especially embodied carbon derived from BIM data, remains underdeveloped. These limitations underscore the need for comprehensive frameworks that combine geometric processing, semantic enrichment, standardized validation, and carbon assessment within a unified pipeline.

Addressing these gaps, this research proposes an automated BIM-to-BEM transformation framework that leverages IFC, IDS-based validation, and structured enrichment processes to generate high-fidelity EnergyPlus models. By focusing on case studies and integrating both operational and embodied carbon assessment, the proposed approach aims to advance the state of the art toward scalable, reliable, and fully automated building performance analysis workflows.