Optimization od daylight in buildings to save energy and to improve visual comfort: analysis in different latitudes
Bibliographic record
Abstract
Natural light is irreplaceable because it is a full-spectrum light, it changes during the day and it is different every day of the year. A variable illumination throughout the day, in terms of intensity and colour temperature, creates dynamic indoor environments that are more pleasant for people. Daylight needs to be controlled, especially in office buildings, to avoid discomfort glare and high luminance reflections on display screens, to provide a good lighting level even in the deeper part of a room and to reduce cooling loads. To improve the quality of light, of visual comfort and to minimize lighting, heating and cooling loads advanced daylighting systems (such as BMS, Building Management Systems) and external shadings should be used. \nThe aim of this study is to optimize the availability of glare-free natural daylight in a building’s interior, in order to create spaces of high visual quality, where the energy demand for artificial lighting and cooling can be reduced by means of control strategies and shading devices. The same office room has been supposed at different latitudes, since each latitude needs a specific shading system . The lighting simulation has been carried out with the software Daysim, developed by the National Research Council del Canada and by the Fraunhofer Institute for Solar Energy Systems and the software Radiance, developed by Greg Ward and by the Lighting System Research group of the Lawrence Berkeley Laboratory.
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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".