Some evidence of a time-varying thermal perception
Bibliographic record
Abstract
Recent research suggests that a time-varying indoor thermal environment can lead to energy savings and contribute to boost buildings' energy flexibility. However, thermal comfort standardization has so far considered thermal comfort criteria as constant throughout the day. In general, very little attention has been given to the ‘ time of day' variable in the context of thermal comfort research. In this paper, we show some evidence of a time-varying thermal perception by using: (1) data from about 10,000 connected Canadian thermostats made available as part of the ‘ Donate Your Data' dataset and (2) about 22,000 samples of complete (objective + ‘ right-here-right-now' subjective) thermal comfort field data from the ASHRAE I and SCATs datasets. We observe that occupants prefer colder thermal conditions at 14:00 and progressively warmer ones in the rest of the day, indistinctively in the morning and evening. Neutral temperature differences between 08:00 and 14:00 and 14:00 and 20:00 are estimated to be of the order of 2°C. We hypothesize that the human circadian rhythm is the cause of this difference. Nevertheless, the results of this study are only based on observational data. Thermal comfort experiments in controlled environmental chambers are required to confirm these findings and to better elucidate the effects of light and circadian timing and their interaction on thermal perception.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".