Key Words Window blinds Energy Thermal comfort
Bibliographic record
Abstract
Algorithms to describe the manual control of window blinds and electric light-ing, in response to comfort stimuli, were incorporated into a building thermal model. By comparison with fixed control schedules, this paper examines the impact of manual control on model predictions of thermal comfort and build-ing energy consumption. For a typical, south-facing office in Toronto, the ther-mal comfort of an occupant close to the window was substantially improved by the provision of window blinds. Compared to an office with no blinds, mean PPD (predicted percentage of occupants dissatisfied with the thermal environment) was lowered from 22 to 13%, and overheated hours were reduced by over 200 per year. However, when the lighting was also manually controlled, the blinds imposed an energy penalty. Though reduced solar gain lowered cooling energy by 7%, heating energy increased by 17%, and reduced daylight increased lighting energy by 6 6 %. The implications of these results on the modelling of occupied buildings are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".