Adjustable Internal Shading for Home Office Daylighting in Tropical Climates
Bibliographic record
Abstract
Home-based workspaces have considerably increased all over the world. Besides, the recent outbreak of the COVID-19 disease forced many people to work from their homes. However, existing residential apartment buildings (ERABs) had been designed for accommodation but not for office works. Low-quality visual environments in ERABs, which have no shading controls on their windows, are evident in tropical climates with extremely high solar radiation. Thus, interior retrofit is significant to provide visual comfort for users in ERABs with low flexibility for modification of their facades. Different interior design variables were simulated by the Radiance-based program to analyse daylighting in a closed-plan room. Before the simulation experiments, field measurement of daylight was performed under a tropical sky to validate the results, and the findings revealed significant Pearson correlations. This paper showed that ERABs are confronting extremely high indoor daylight quantity, up to 10,228 lx, and low quality with intolerable glare. An adjustable model of internal shading, including an integrated Venetian blind with a horizontal light shelf and the window films, was proposed to improve quantitative and qualitative performances of daylighting in tropical regions. This dynamic model could be adjusted to various positions based on daylighting conditions in the buildings. By comparing the simulation results of this model with the base model, indoor illuminance levels could successfully reduce from 32% to 86%; Illuminance Uniformity Ratio (IUR) and Target Daylight Illuminance (TDI) significantly improved up to 180% and 300%, respectively; Daylight Glare Probability (DGP) and CIE Glare Index (CGI) changed from intolerable to imperceptible status. Accordingly, the proposed model can considerably improve daylight quantity and quality in the test room during different times. This study concludes that the dynamic model of internal shadings could provide efficient daylighting, by decreasing the extremely high indoor illuminance and glare in the ERABs in tropical climates.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".