Geometric data in urban building energy modeling: Current practices and the case for automation

Urban building energy modeling (UBEM) is crucial for addressing energy consumption challenges in urban environments. This study investigates the significant role of geometric data in UBEM, focusing on its impact on accurately capturing urban morphology for realistic simulations and analyses. By reviewing and comparing various bottom-up modeling approaches-white-box, grey-box, and black-box models, this research highlights the methodologies, techniques, and advancements in geometric data collection. A framework is proposed to guide urban planners, architects, engineers, and policymakers in selecting appropriate geometric data collection strategies tailored to specific modeling needs, considering factors such as geometric features, data accuracy, resolution, scalability, and cost. Additionally, the study explores data preprocessing techniques, including noise reduction, feature extraction, and data integration, to improve the quality and usability of geometric data for energy modeling. Recent advancements, such as the integration of computer vision techniques and machine learning for automated building feature extraction and classification, are also examined. The findings provide practical guidance for enhancing the effectiveness and efficiency of UBEM, contributing to more sustainable urban energy management and better-informed decision-making in urban planning and policy development. This research offers a novel perspective by synthesizing current practices and proposing a comprehensive framework that addresses the ongoing challenges in geometric data collection and utilization in UBEM.