Development of a 3D ray tracing-based direct solar shading model for urban building energy simulation
Bibliographic record
Abstract
Direct solar shading by nearby buildings plays a significant role in urban building energy models (UBEMs) for predicting building energy performance. Analytical methods currently dominate solar shading analysis in UBEMs; however, (I) they require geometry simplifications to process complex geometries, such as buildings with curved surfaces, and struggle to model structures like trees, and (II) they rely on hard-to-replicate computational acceleration schemes for large-scale simulations due to repeated surface-by-surface shading analysis. These shortcomings highlight the need to explore alternative approaches for direct solar shading simulations required by UBEMs. In this paper, we developed a ray tracing-based solar shading model designed for large-scale simulations, which can successfully process complex structures. Our 3D shading model tracks sun rays across the urban area simultaneously, eliminating repeated shading calculations and, thereby, the need for traditional acceleration schemes necessary for analytical methods. The model's accuracy is demonstrated through comparisons with measurements and results from existing tools. Furthermore, the model is integrated into our in-house UBEM, CityBEM, to enhance solar radiation assessments. Simulation results for a downtown Montréal (Quebec, Canada) test case suggest that neglecting solar shading led to an average underestimation of winter energy use by 13% and an average overestimation of summer energy use by 15%. Moreover, rooftop solar radiation was overestimated by more than 20% for over a third of buildings, while about 30% of buildings had overestimations of 10-30%. Future work will incorporate additional factors, such as geometry partitioning, GPU acceleration, and adaptive model inputs, for scalable urban simulations. In conclusion, our research provides a valuable tool for improving UBEM predictions and supporting sustainable urban design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".