Data-Driven Models for Building Occupancy Estimation
Bibliographic record
Abstract
The availability of accurate occupancy information from different spaces in a building allows for significant reduction in the energy consumption of heating, ventilation, air conditioning, and lighting systems. This paper investigates the application of particle filters and time series neural networks to inferring the number of occupants of individual rooms from time series data collected by a set of occupancy-indicative sensors. Our approach is purely data driven and does not require developing customized and complex physics-based models to predict the occupancy level of the many rooms in a building. We evaluate the efficacy of the proposed methods on two data sets, one contains measurements of dedicated sensors while the other one contains measurements of HVAC sensors that are commonly available in commercial buildings. Our results indicate that time series neural networks are superior in this application, estimating the number of occupants with a root-mean-squared error of 0.3 and 0.8 in the two data sets with a maximum of 7 and 67 occupants, 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".