Demographics as Determinants of Building Occupants’ Indoor Environmental Perceptions: Insights from a Machine Learning Incremental Modeling and Analysis Approach
Bibliographic record
Abstract
The relationship between the demographical characteristics of building occupants and their perception of indoor comfort is increasingly being studied. However, the added value from accounting for such characteristics when modeling and predicting occupants’ perceptions remains unclear. An incremental machine learning (ML) modeling and analysis approach is proposed to quantify the influence of four demographical factors (gender, age, nationality, and time lived in the environment) on occupants’ perceptions of their indoor environment conditions. A three-step methodology is presented: (1) data collection through sensors and a questionnaire administered on 206 occupants of academic and office buildings in Abu Dhabi, UAE, (2) development of ML models (i.e., support vector machine, random forest, and gradient boosting) to predict occupants’ perceptions under different scenarios of demographical representation (i.e., from no representation to all demographical parameters included), and (3) analysis of the impact of demographical parameters’ inclusion on the performance of the ML models in terms of predictive accuracy, F1-scores, and computing time. Results confirm that including demographical variables could increase prediction accuracy and F1-scores by approximately 19% and 56%, respectively. However, in some instances, the inclusion of these variables reduced model performance while increasing computing time by as much as 50%. A detailed discussion is presented on the comparative performance of the different tested ML algorithms and the need to strike a balance between increasing model complexity and computational costs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".