Occupant-centric demand-controlled ventilation strategy for airport terminals using Wi-Fi data and CFD simulations
Bibliographic record
Abstract
• Developed a Wi-Fi–based approach to capture occupants’ spatial distribution. • Proposed a ventilation reset algorithm to maintain IAQ, using Wi-Fi data. • Evaluate the framework by generating CFD scenarios for an airport terminal. Maintaining indoor air quality (IAQ) while saving energy is a key challenge in building ventilation. In airport terminals, this challenge is amplified by the interplay between human activity, IAQ, and energy use. The study utilized Wi-Fi data to estimate occupant distribution scenarios, employing combination-based clustering to develop an occupant-centric Demand-Controlled Ventilation (DCV) framework for airport terminals. The CO₂ setpoint was determined following ASHRAE Guideline 36, and Computational Fluid Dynamics (CFD) simulations were performed for various occupant distributions to estimate the minimum outdoor air required to maintain IAQ across the terminal. A mixing box model was applied at diffuser boundaries to represent air recirculation effects. Based on these results, a dynamic ventilation reset algorithm was developed to adjust the outdoor air setpoint according to the spatiotemporal distribution of occupants. Results showed that occupant spatial dynamics have a strong influence on IAQ, with higher outdoor air percentages improving ventilation effectiveness but reducing uniformity. Integrating Wi-Fi–based occupant data enabled more responsive and energy-efficient DCV control. The method reduced average daily coil energy consumption by 23%, with thermal load variations from −26% at low occupancy to +360% at full occupancy. This approach is particularly suitable for large spaces such as airport terminals lacking CO₂ sensors but equipped with Wi-Fi infrastructure.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".