A Critical Analysis of the Energy Requirements of a Commercial Building Based on Various Types of Glass Insulations
Bibliographic record
Abstract
Heat loss through the building envelope comprises air leaks through the cracks and largely through the windows, which is the weakest link of the thermal envelope. Therefore, it is necessary to devise a systematic approach to analyze the rightful selection of glass for buildings. The investigation is to analyze the energy-saving potential of different glasses and their comparisons to the initial capital cost to find the payback time in terms of energy saving by using two different types of equipment. The quantitative simulation study was completed on the Hourly Analysis Program (HAP) to analyze the annual energy consumption of the HVAC system for seven glasses and two types of chilled water equipment. The results show that the performance glasses with a tint had better efficiency in terms of energy saving, with a payback time of 3–7 months. A comparison of all glasses illustrated that float glass contributes the most to the total cooling load among all glasses, which were 5.04%, 5.7%, 7.6%, and 8.9% for the N, S, E, and W orientations, respectively. Moreover, the lowest contribution of glass to the total cooling load was given by tinted double-glazed glass, which was 2%, 2.3%, 3.0%, and 3.01% for N, S, E, and W orientations, respectively.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".