Evaluating and optimizing energy and comfort performance in airport cooling systems through dynamic occupancy modeling and time-series clustering

Energy inefficiency in airport HVAC systems challenges carbon reduction efforts, as traditional control strategies—like uniform and zone function-based controls—often cause over-conditioning and discomfort. Large public buildings with open-plan layouts require dynamic occupancy distribution and high-resolution zoning to optimize HVAC performance. Existing studies relying on low-resolution zoning overlook significant occupancy variations, limiting their effectiveness. This study addresses these challenges by developing an integrated simulation framework that combines ABM and BEM to capture spatio-temporal occupancy variations at a high resolution. Field surveys and Monte Carlo sampling were employed to generate passenger samples and airport service levels. A novel high-resolution partitioning rule was introduced to reflect the dynamic nature of occupant behavior in open spaces. Additionally, a cooling control strategy based on Dynamic Time Warping was tested, with 7 control scenarios designed to compare energy and thermal performance. The results demonstrate that the proposed high-resolution zoning approach and DTW-based clustering strategy outperform traditional low-resolution and zone-function-based control methods by more accurately capturing dynamic passenger density and temperature demand fluctuations. Unlike Euclidean distance-based clustering, DTW identifies temporal relationships, such as delayed peaks in thermal demand, enabling informed control strategies. Specifically, these methods achieved energy savings of 14.15–16.65 % and thermal comfort improvements ranging from 2.4 % to 3.18 % compared to zone function-based control. By integrating high-resolution occupancy data and advanced clustering techniques, this study offers a dynamic and adaptive solution for optimizing HVAC performance, addressing key shortcomings of existing control strategies, and advancing occupant-centric sustainable building management.