Building Information Modeling (BIM) to Building Energy Modeling (BEM) integration offers a promising pathway to enhance energy performance assessment, yet the variability in BIM modeling granularity remains a significant source of uncertainty, contributing to the performance gap. This study systematically investigates how different facets of modeling granularity impact BEM outcomes. Using a university building archetype, a parametric matrix of 62 scenarios was generated by varying four domains of modeling granularity: spatial configuration, construction assemblies, thermal zoning, and internal conditions. An automated BIM-BEM workflow was employed to process and simulate each scenario. The analysis reveals a significant performance spread across the scenarios, with the predicted Energy Use Intensity (EUI) varying by up to 68 %. The results identify internal conditions, a form of semantic granularity, as the dominant factor driving this variability, organizing outcomes into distinct, non-overlapping performance clusters that far outweigh the influence of spatial or construction detail. Furthermore, the study demonstrates that simplified semantic models can significantly distort the building’s energy balance, in some cases overestimating total cooling energy use by 18%. This distortion, in turn, can lead to design misdirection by masking localized load peaks and altering the identification of critical spaces for system sizing. Based on these findings, the study articulates a conceptual model that distinguishes between spatial and semantic granularity. This model serves as a heuristic guide to facilitate the shift from a paradigm of “as detailed as possible” to a more resilient strategy of “as detailed as useful”, thereby promoting a more conscious and purpose-driven approach to modeling granularity.

In the Architecture, Engineering, and Construction (AEC) domain, integrating Building Information Modeling (BIM) and Building Performance Simulations (BEPS) is essential for optimizing building design and performance. This study investigates the potential of AI to enhance the integration of BIM and BEPS through Industry Foundation Classes (IFC). This study also examines the challenges inherent in the BIM-BEPS workflow and the barriers to AI adoption in this domain. The paper aims to present solutions that support IFC-based interoperability, identifying the most effective approaches within the categories of the mapped problems. These include tools for extracting geometry from IFC models, algorithms for geometric enrichment, ontologies for rule-based model verification, machine learning techniques for space classification, external libraries, and IFC extensions for property addition to models. The integration of AI demonstrates significant potential to improve BIM-BEPS workflows, particularly in automating geometry extraction from BIM, enriching model data, and detecting inconsistencies in IFC models. The study also explores opportunities to enhance the BIM-BEPS workflow through IFC4 and future IFC generations, focusing on combining ontology frameworks with machine learning. Furthermore, the study emphasizes the industry’s role in developing better user support solutions, underscoring the need for users to adhere to well-defined design requirements and workflows to maximize the benefits of these advancements.

Natural ventilation is a key strategy of building design that can reduce energy consumption and greenhouse gas emissions, while improving indoor air quality and thermal comfort. Although BEPS (Building Energy Performance Simulation) tools assess such systems and BIM (Building Information Modeling) facilitates data exchange via the IFC (Industry Foundation Classes) standard, current BIM-BEPS workflows present challenges like data loss and limited support for natural ventilation. This study proposes a methodology to enhance BIM-BEPS workflows by providing robust support for natural ventilation simulations using the AirflowNetwork model. The approach involved applying the Information Delivery Manual (IDM) methodology to structure the processes and organize the information requirements. Based on this structure, a semantic data dictionary aligned with the BuildingSMART Data Dictionary (bSDD) is developed, incorporating AirflowNetwork-supported systems in EnergyPlus. A Python-based add-on is created to enrich IFC models with data from the dictionary ensuring compatibility for simulation purposes. Model validation is performed through Information Delivery Specification (IDS). The proposed methodology addresses current limitations and establishes a foundation for broader applications in the energy domain, expanding its usefulness beyond this area. It is fully structured within an openBIM workflow, relying exclusively on open data standards and freely accessible tools for both development and implementation.

Buildings are central to global efforts to reduce greenhouse gas emissions. However, energy labeling programs, intended to encourage sustainable practices, often encounter challenges such as data inaccuracies, manual processes, and discrepancies between estimated and actual energy consumption. This study examines how Building Information Modeling (BIM) can contribute to improving the accuracy, reliability, and efficiency of energy labeling, addressing existing gaps in current practices. Through a systematic literature review, the research compiles insights from recent studies that demonstrate BIM's potential to centralize and automate building data, reduce errors in energy assessments, and facilitate integration with Building Energy Modeling (BEM) tools for more consistent simulations. The findings indicate that BIM-based methods, including model generation techniques, multi-objective optimization, and digital twin applications, help mitigate the performance gap by enabling data updates, improving interoperability, and offering better occupant behavior modeling. Nevertheless, several challenges remain, such as ensuring data quality, enhancing open data standards, and refining tools to accommodate evolving energy systems. A key novelty of this review lies in synthesizing a broad range of BIM-driven approaches into a cohesive perspective on how energy labeling can be modernized and enriched. By proposing future research paths that further integrate these BIM-based solutions, this study underscores BIM's capacity to streamline labeling processes through advanced analytics and automation, ultimately supporting more informed retrofits, regulatory compliance, and sustainable design strategies. In summary, the review suggests that BIM can enhance current labeling methodologies and contribute to broader sustainability goals in the built environment.