Exploring occupant detection model generalizability for residential buildings using supervised learning with IEQ sensors
Why this work is in the frame
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Bibliographic record
Abstract
This study explores two modeling approaches for occupancy detection at room level for residential buildings in Denmark. The aim is to assess the performance and generalizability of occupant detection models using XGBoost method trained on a rich dataset comprising indoor environmental quality (IEQ) variables and occupancy ground truth. A global approach and a room-specific approach are considered. After a thorough feature selection and importance analysis process, the occupancy detection models (ODMs) are trained and tested in a nested cross-validation schema. The time of the day, indoor CO2 concentration, and feature transforms related to short-term IEQ dynamics were found to be the most important features of the ODMs. Both the global and room-specific models show good occupancy prediction performance, especially for bedrooms. When tested for generalizability with an unseen dataset from a different residential building, the ODMs maintain very good performance for the bedroom but not for the office room. This discrepancy could be explained by significant differences in occupancy and ventilation patterns, and large air infiltration from adjacent rooms. Although currently limited in terms of generalizability, XGBoost-based ODMs using IEQ data have the potential to provide robust and scalable occupancy detection for occupant-centric control and occupant-aware building performance assessments. The IEQ dataset with occupancy ground truth collected for this study is made available in open access.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it