Characterizing cooling load in multi-area airport terminal buildings: Clustering and uncertainty analysis for energy flexibility

An airport terminal building is a complex public transportation facility with an extremely high energy consumption intensity. Its numerous indoor areas with various functions have disparate characteristics of cooling loads, which poses a great challenge to energy-efficient design and operation of its air-conditioning system. In this paper, we investigated the characteristics of cooling loads in two typical multi-area airport terminal buildings by simulation. Clustering analysis was utilized to categorize the areas based on their distinct cooling load characteristics. Uncertainty analysis and Bayesian calibration were employed to reveal and compare the impact of primary parameters on these cooling loads. The indoor area of an airport terminal building can be classi- fied into three categories according to their different characteristics of cooling loads. The result shows that outdoor temperature (Tout), outdoor relative humidity (RHout), and air change rate(alpha) significantly contribute to the variances among the cooling loads in different areas. The peak cooling loads of the three categories were identified as 182.8 W/m2, 76.1 W/m2, and 58.3 W/m2, respectively. The Bayesian calibration result demonstrates that the differences of the main para- meters (i.e., Tin and RHin) between operation and design are the key factors causing the difference of cooling load between measurement and design. The potential for flexible cooling load adjustment across different area categories was also discussed, contributing to further quantification of the energy flexibility potential of airport terminal buildings. The unique value of this paper is manifested in the new approach it provides for understanding and managing cooling loads, contributing to more energy-efficient terminal design and operation.

Automated summary

This paper investigates the cooling load characteristics in airport terminal buildings and categorizes the indoor areas based on their distinct characteristics. The study reveals the significant impact of outdoor temperature, outdoor relative humidity, and air change rate on cooling loads. The Bayesian calibration analysis identifies the differences in main parameters as the key factors causing the discrepancy between measurement and design. The paper also discusses the potential for flexible cooling load adjustment across different area categories.