Study on Improving the Energy Efficiency of a Building: Utilization of Daylight through Solar Film Sheets
Bibliographic record
Abstract
Daylight can contribute to substantial reductions in the energy consumed by artificial lighting applications. However, issues such as visual comfort, illumination intensity, and availability represent major issues when daylight is relied upon to illuminate buildings. There are many technologies that are used to control received sunlight and minimize its side effects. The placement of solar film sheets on window glass is a common and popular method utilized in many buildings to minimize electric lighting energy consumption without causing undue visual discomfort to occupants. To examine the practicality of this application and its effect on room lighting, a modern office was selected in which to conduct this field study. Two measures were used to evaluate this technique: firstly, field measurements and their comparison to the specified standard illumination levels; and secondly, a short-form questionnaire survey conducted to obtain occupants’ opinions of the office lighting. Actual measurements were conducted in the selected office spaces, with and without applying solar control film coating on the window glass. Indoor luminance levels and lighting comfort were systematically recorded and analyzed. The findings of this study show that using a solar film with a visible light transmittance of 50% can achieve savings in energy consumption of up to 33% if utilized as part of an integrated lighting system.
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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 | 0.000 | 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".