The effect of Urban-scale shading on building energy modelling results of an educational building in Montréal
Bibliographic record
Abstract
The interconnectivity between building-scale and urban-scale modeling is beneficial for building energy assessment. The energy consumption estimation of individual buildings could be affected by numerous factors from the surrounding environment. The purpose of this study is to compare the energy consumption of a stand-alone building versus the same building modeled within the urban context. A case study high-rise building in Montreal was investigated in detail in its urban context to analyze the impact of shading on building energy demand. Three scenarios are introduced for the surrounding buildings: high-rise, low-rise, and the actual context. Each scenario's effect on the target building's energy consumption is estimated and compared with the stand-alone condition. The impact of each alternative on building performance was assessed by calculating yearly total energy consumption (heating, cooling). The results show that shading due to the nearby buildings plays an essential role in energy demand throughout the year. Increasing the height of the surrounding buildings in winter increases the heating consumption by up to 44\%, and a reduction in cooling by up to 40\% is seen during summertime. This study confirms that considering the effect of neighbor buildings does affect the energy-related simulation's outcomes. Therefore, building energy behavior analysis in urban and street planning can be the theoretical foundation for logical architecture design and energy consumption reduction when efficient cities are constructed. Moreover, the influence of other urban environmental factors, such as meteorological loads, Urban Heat Island (UHI) effects, or urban morphology, could be investigated for future studies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".