Refereed Paper A SIMULATION DESIGN STUDY FOR THE FAÇADE RENOVATION
Bibliographic record
Abstract
Office buildings with large glass facades often face problems such as glare, thermal discomfort, overheating and increased energy demand. In this case study, thermal and daylighting simulation were performed in order to investigate the possible options for the facade renovation of a large office building in Montreal. For the daylighting part, the available daylight on the work plane surface of interior perimeter zones was calculated for each façade, considering several shading options: dark screens, roller shades with variable properties, reflective venetian blinds, translucent glazings and combinations of the above. Daylight rendering with and without shading helped in visualizing the results. The impact of different shading properties on glare was also studied, as well as the impact of window size on annual daylight autonomy for each case. For the thermal part, emphasis was given on the effect of shading location, properties and control on the thermal loads, since the glass facades account for a big part of the load. It was found that the shade location and properties have a significant impact on heating and cooling load as well as on the thermal comfort near the windows. The simulation results showed that a 60 % reduction in cooling demand – due to the façade- could be achieved, if controlled shades with small or variable transmittance and high outside reflectance will be used.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.044 | 0.002 |
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".