Geometric data in urban building energy modeling: Current practices and the case for automation
Bibliographic record
Abstract
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. • Utilization of geometric data in urban building energy modeling (UBEM). • Recent advancements in geometric data collection and its processing for UBEM. • Implementation of geometric data in various bottom-up UBEM approaches. • Decision-making framework for the collection and utilization of geometric data. • Automation of geometric data collection and application of artificial intelligence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".