e-ISSN 2231-8526
ISSN 0128-7680
Lei Wang, Mazran Ismail and Hazril Sherney Basher
Pertanika Journal of Science & Technology, Volume 33, Issue 5, August 2025
DOI: https://doi.org/10.47836/pjst.33.5.15
Keywords: Airport terminal building, data-driven energy modeling, energy efficiency, HVAC optimization, systematic review, thermal comfort assessment
Published on: 2025-08-28
Airport terminal buildings (ATBs) exhibit highly dynamic occupancy patterns and extended operating hours, leading to notably higher energy consumption and carbon emissions than other building types. Among the various energy-intensive systems, review findings indicate that heating, ventilation, and air conditioning (HVAC) systems, the most energy-intensive in ATBs, account for approximately 40–60% of total energy consumption, underscoring the need for efficiency improvements, notably during cooling periods. This study systematically reviews 63 studies from 2003 to 2024 to evaluate energy efficiency and thermal comfort performance in ATBs, identifying key research trends and gaps. Despite their significant impact, real-time variations in passenger density and movement patterns pose significant challenges to HVAC optimization, yet existing studies have overlooked mainly their influence on energy performance. The findings reveal that research on ATB energy efficiency has shifted towards integrated approaches that balance energy efficiency and passenger comfort, rather than optimizing either factor independently. Regarding optimization methods, two dominant approaches have been identified: physics-based and data-driven methods, with the latter being the most popular, adopted in 49% of the reviewed studies. Future research should focus on hybrid approaches that integrate physics-based and AI-driven optimization models to improve predictive accuracy and computational efficiency. Additionally, incorporating real-time occupant behaviour into energy optimization strategies is crucial for balancing efficiency and passenger comfort. Advancing robust datasets and enhancing model interpretability will be key to next-generation ATB energy management.
ISSN 0128-7680
e-ISSN 2231-8526