Bayesian inference of thermal comfort: evaluating the effect of “well-being” on perceived thermal comfort in open plan offices
Bibliographic record
Abstract
Abstract The judgment of thermal comfort is a cognitive process which is influenced by physical, psychological and other factors. Prior studies have shown that occupants, who are generally satisfied with many non-thermal conditions of indoor environmental quality, are more likely to be satisfied with thermal conditions as well. This paper presents a novel approach that considers the effect of non-thermal building environmental design conditions, such as indoor air quality and noise levels, on perceived thermal comfort in open-plan offices. The methodology involves the use of Bayesian inference to relate the occupant’s thermal dissatisfaction in a building not only to thermal conditions and occupant metabolic factors (i.e., parameters of the original Fanger model), but also to measurable non-thermal metrics of indoor environmental quality. A Bayesian logistic regression approach is presented in this paper. The experimental context regards a prior indoor environmental quality measurement and evaluation study of 779 occupants of open-plan offices throughout Canada and the US. We present revised PMV-PPD curves for real-world offices that take into account both thermal and wellbeing IEQ parameters. The Bayesian inference analysis reveals that the occupant’s thermal dissatisfaction is influenced by many non-thermal IEQ conditions, such as indoor CO2 concentrations and the satisfaction with the office lighting intensity.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".