Development and implementation of occupancy-based predictive controls for modulation of air handling units’ outdoor air dampers
Bibliographic record
Abstract
Current practices in the field of commercial and institutional building operation require ventilation to be provided to spaces assuming at or near full occupancy.However, full occupancy in the field of heating, ventilation, and air conditioning (HVAC) design is crudely estimated early in the design process and often does not reflect the actual maximum occupancy of the building.This results in the chronic overventilation of buildings, which wastes significant energy in heating climates where conditioning outdoor air is the main driver of HVAC energy use.Therefore, in forced air systems with air handling units, reducing outdoor air damper positions based on the predicted occupancy of buildings has the potential to generate significant energy savings.This research explores the potential of these occupancy-based predictive controls for outdoor air dampers in a case study building in Ottawa, Canada.Different sensors and model formalisms for estimating occupancy were evaluated.It was discovered that multiple linear regression models using Wi-Fi, plug-in equipment, and lighting load data produced accurate occupant-count estimates.A novel forecasting framework consisting of k-means clustering of occupancy data, motif and discord identification, and classification trees was employed to develop a rules-based method for occupancy prediction that could be trained offline and practically applied in existing commercial building automation systems.The results from a six-month long implementation of these occupancy-based controls in the outdoor air dampers showed that heating and cooling energy use were reduced by 38% and 10%, respectively, without any need for additional sensing or controls infrastructure.These findings indicate the potential benefit of data-driven building operations when occupants are accounted for directly in the sequence of operations and identifies areas that warrant further investigation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| 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".