Smart Glazing Systems vs Conventional Glazing: A Comprehensive Study on Temperature Control, Daylighting, and Sustainability
Bibliographic record
Abstract
Glazing systems play a significant role in managing the interaction between indoor and outdoor temperatures within a building.This research was conducted in a laboratory at Najran University in Saudi Arabia, focusing on six different glass samples, including two conventional (double low-E glass and vacuum clear glazing) and four smart glazing configurations (PDLC and SPD in both ON & OFF states), aiming to analyze their impact on indoor-outdoor temperature interaction, solar radiation control, and visual comfort.Key findings include SPD-OFF's superior temperature control (limiting increase to 24.28℃ compared to 10.53℃ for double low-E), PDLC-ON's optimal daylight illumination (2901.43lux), and its higher energy efficiency (97.97 W/m² heat gain) compared to 44.8 W/m² for double low-E.The study also highlighted cost and environmental differences, with double low-E being the most cost-effective ($1.43/m² ) and PDLC-ON the most eco-friendly (14.5 kg CO2/m² ), despite higher operating costs.Overall, smart glazing systems, particularly the PDLC configuration, demonstrated notable advantages in thermal and visual comfort management.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".