Occupant counting model development for urban building energy modeling using commercial off-the-shelf Wi-Fi sensing technology
Bibliographic record
Abstract
Urban Building Energy Models (UBEMs) are vital for estimating building energy use and related greenhouse gas emissions. However, their reliability needs boosting by using more real-world data, especially regarding occupancy behavior. Presently, UBEMs often use standard occupancy patterns, which may not reflect the real building use, especially for commercial and institutional buildings. Wi-Fi sensing is a reliable approach that can improve UBEMs due to its wide availability and cost-effectiveness. In this study, we leverage detailed signal data derived from Wi-Fi sensing technology to create a realistic, scalable and cost-effective occupancy model. A framework has been developed to derive the number of people in a building, which will influence energy usage patterns and enhance UBEMs. The developed model employs various machine learning techniques and achieves a test accuracy of 77%. Limited availability and diversity of the initial dataset necessitated the use of data augmentation techniques, enabling the model to learn varied representations and thus achieve better test performance of 91% post-augmentation. To evaluate the effectiveness of the developed model, it has been applied to two institutional buildings of a specific inner-city district in Montreal, Canada, to compute their heating and cooling demands. The outcomes are then compared with those obtained using standard schedules, revealing a considerable discrepancy in annual peak and total annual cooling demand of about %5 and 20% respectively.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".