Occupant-centric demand-controlled ventilation strategy for airport terminals using Wi-Fi data and CFD simulations

• 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.