Passenger spatiotemporal distribution prediction in airport terminals based on insect intelligent building architecture and its contribution to fresh air energy saving

Abstract

As the essential infrastructure and important transportation hubs, airport terminals play a strategic role in sustainable cities and urbanization. Due to the strict service requirement and round-the-clock operation, the high energy consumption poses unprecedented challenges to developing environment-friendly airport terminals. Actually, passenger flow as a root cause decides the startup and operation of service facilities including air-conditioning systems. But due to spatial connectivity, parameter coupling, and passenger mobility, resource allocation and system tuning have not been effectively related to passenger travel. In this work, Guangzhou Baiyun International Airport Terminal 2 was taken as an object, and a passenger spatiotemporal distribution model was developed and its contribution to fresh air energy saving was estimated. Among them, zone partition and expression rules were formulated based on insect intelligent building (I2B) architecture to construct Wi-Fi positioning system; Next, for domestic and international passenger, their arrival patterns were fitted by Chi-Square distribution with R2 > 0.993, and their spatiotemporal distribution was predicted with R2 over 0.74 and 0.67 respectively; Lastly, a hybrid reset strategy for fresh air volume set-point combing fuzzy and linear rules was proposed with results showing that HVAC systems running on variable occupancy would lead to considerable energy savings.