Enhancing energy performance assessment and labeling in buildings: A review of BIM-based approaches

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.