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.

Understanding cross-modal environmental perception is essential for improving occupant well-being and human-centric building design. This paper presents an open-access, multi-site database developed under the IEA-EBC Annex 79 project to test the Hue-Heat Hypothesis (HHH), which hypothesizes that light hue may influence thermal perceptions. The database comprises 543 experimental rounds conducted in eight laboratories across six countries and diverse climate zones, following a shared, rigorously designed protocol. During summer and winter campaigns, participants were exposed to controlled thermal environments and counterbalanced lighting conditions (neutral, reddish, bluish). The database includes detailed metadata on environmental variables, physiological measurements (i.e., heart rate and skin temperature), and self-reported perceptual responses. It also provides standardized technical documentation for each test room, including the detailed experimental protocol and translated survey instruments. All materials are available on the Open Science Framework under the “Multi-site Hue-Heat-Hypothesis Testing” repository. This resource supports research into multi-domain human comfort, enabling analysis of cross-modal and combined effects on human perception and physiological reactions.

Personalized Environmental Control Systems (PECS) offer adaptive strategies to enhance occupants’ comfort while improving energy efficiency. These systems may support the restoration of thermal comfort after spatial transitions. This study investigates occupants' psychophysiological responses after outdoor-to-indoor transitions in Florianópolis, Brazil. The experiments were designed to mimic a typical office day, in which participants transition from outdoors in the morning upon arrival and after the lunch break. Participants underwent three experimental scenarios: using a small evaporative cooler, a desk fan, or no PECS. Conducted in a university living lab with a slightly elevated cooling setpoint (26 °C vs. the conventional 23 °C–24 °C), results showed higher usage of evaporative coolers (50.0 %–87.5 %) compared to desk fans (37.5 %–58.3 %). Both PECS effectively modified near-body thermal conditions, reducing wrist-level air temperature (evaporative coolers: 1.10 °C ± 1.88 °C; desk fans: 0.96 °C ± 1.18 °C, mean ± SD), with long-term effects. Mean skin temperature (MST) reductions were higher using evaporative coolers. However, statistical modeling confirmed that the use of both PECS impacts MST. While PECS did not alter descriptive thermal comfort indicators (e.g., thermal sensation) under neutral indoor conditions, they influenced hedonic responses such as thermal preference. This suggests that thermal PECS may help reduce the preference bias toward cooler thermal sensations, commonly observed in warm-to-hot regions. Since PECS also affected physiological signals, the shift in preference likely results from a combination of physiological changes and lowered expectations for cooling. Consequently, using PECS may support higher setpoint temperatures and contribute to energy savings in buildings.