Towards improved thermal comfort predictions for building controls
Bibliographic record
Abstract
This paper updates the findings of a prior work that found evidence to suggest that predictions of thermal comfort can be improved by adding measurements of indoor CO2 concentrations. This work first updates these findings by adding 150 new samples of IEQ measurements collected from occupants of office spaces at the University of British Columbia in 2019. This paper then formulates and proposes a novel Hierarchical Bayesian model, trained on the expanded field dataset, that predicts thermal satisfaction based on thermal IEQ metrics and measurements of CO2 levels. Posterior predictive results revealed a robust and statistically significant correlation between perceived thermal comfort and indoor CO2 levels. Cross-validation and posterior checks revealed stronger evidence that including indoor CO2 concentrations as an independent variable when predicting thermal satisfaction improves its prediction accuracy. The proposed model can be integrated into building control systems to predict thermal comfort in office spaces based on thermal conditions and ventilation rates, which improves the prediction accuracy of thermal comfort, mitigate the performance gap between predictions and observations of thermal comfort, and may result in energy savings while not sacrificing indoor air quality and well-being, an important challenge to building controls.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".