Occupancy matters: toward an occupancy-driven ventilation system using WiFi infrastructure and neural network
Bibliographic record
Abstract
Buildings accounted for 20% of total energy consumption and 54% of electricity usage in 2013 in Canada. Heating Ventilation and Air conditioning system is the main consumer of energy in the buildings. The common approach for designing a ventilation system is a predefined schedule based on the maximum capacity disregard the actual number of occupants. We believe that passive use of already existing WiFi infrastructures can replace the monitoring sensors and cut the cost of energy and extra sensors installation. A field study was conducted in graduate offices of Ryerson University to examine the opportunity of energy saving by changing the fixed ventilation schedule to the occupancy driven one. The number of occupants had been determined using pre-existing WiFi infrastructure and by using the real time occupancy data, the new system achieved 76% reduction in ventilation energy consumption. To further investigate the potentials of WiFi infrastructure, Finger Printing and Neural Network method had been used to map the occupant’s location by analyzing the Received Signal Strength Indicator (RSSI) of the wifi equipped device. The results showed 95% accuracy in the first round of testing and 92% accuracy after 1 week of retesting the model by using pattern recognition technique. Employing this approach could lead to even more energy saving by assigning the required airflow to each subzone proportionally to the number of its occupants.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".