Enhancing occupant-centric ventilation control in airport terminals: A predictive optimization framework integrating agent-based simulation

Abstract

Enhancing ventilation efficiency is essential for the sustainable operation of large airport terminals, which typically consume significantly more energy than regular public buildings. However, fluctuating passenger distributions in space and time—driven by flight schedules and airport services—pose significant challenges to ventilation control, often resulting in unnecessary energy use and suboptimal indoor air quality (IAQ) management. To address this, our study proposes an innovative optimization framework to automate coordinated multi-zone ventilation control in large airport terminals using an agent-based passenger flow simulation model. Based on flight schedules, passenger flow within the terminal was simulated through pedestrian agents governed by the Social Force Model, enabling the accurate characterization of spatiotemporal passenger distribution and activities across different zones. This simulation was integrated with a physical ventilation model and a distributed evolutionary algorithm to achieve optimal IAQ management, energy efficiency, and flexibility in energy use. Over a two-month evaluation period, the optimized control framework demonstrated marked improvements over baseline fixed-schedule control, with the CO₂ compliance rate rising from 89.32 % to 99.6 %, total energy consumption reduced by 29.6 %, and daily peak power demand decreased by 27.2 %. This study showcases a practical, occupant-centric approach to improving operational sustainability in complex built environments.