Optimizing HVAC Operations in Office Spaces: A Sensor-Fusion Approach for Zone-Level Occupancy Prediction
Bibliographic record
Abstract
Heating, Ventilation, and Air Conditioning (HVAC) systems account for over 50% of energy consumption in Canada's commercial building sector, highlighting the need for operational optimization to enhance energy efficiency.This study addresses this challenge by analyzing occupant presence to avoid conditioning unoccupied spaces.This investigation focuses on a Montreal case study to optimize HVAC schedules by predicting zone-level occupancy through sensor fusion, combining motion, CO₂, and temperature data.The study objectives are to: (1) identify earliest and latest arrival and departure times using a cumulative relative frequency approach; (2) analyze daily peak occupancy through association analysis using the Frequent Pattern Growth algorithm; and (3) predict zone-level occupancy using machine learning models, including Logistic Regression, Decision Trees, Random Forest, and Long Short-Term Memory networks.The results reveal significant variations in arrival and departure times across zones.Despite strong results from all models, Random Forest outperformed the rest, with accuracy and F1 scores above 80% across all zones.Finally, these findings demonstrate the potential for tailoring HVAC operations to distinct occupancy patterns, promoting substantial energy savings in commercial buildings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".