Electricity Demand Drivers in Airport Infrastructure: Operational and Climatic Influences in a Tropical Context

Understanding electricity consumption patterns in airport terminals is critical for improving energy efficiency and supporting renewable energy integration. This study investigates monthly electricity consumption at Kotoka International Airport, Ghana, over the period 2019–2022 using correlation analysis and K-means clustering to examine the influence of air passenger throughput, climatic variables, and operational regimes. After excluding the anomalous COVID-19 period, a strong and statistically significant relationship is observed between electricity consumption and passenger volume (r = 0.705, p < 0.001), confirming passenger activity as the dominant driver of monthly electricity demand. Climatic variables act as secondary modifiers, with electricity consumption positively correlated with ambient temperature (r = 0.317) and specific humidity (r = 0.280), and negatively correlated with solar irradiance (r = –0.343) and wind speed (r = –0.431), reflecting system-level responses to thermal and environmental conditions.K-means clustering identifies three distinct monthly operational regimes—baseline, normal, and peak demand—highlighting non-linear interactions between passenger activity and climate. Peak-demand months exhibit electricity consumption reaching approximately 1.33 GWh, while analysis of the COVID-19 period reveals a substantial baseline energy floor, with approximately 70–80% of electricity demand maintained despite severely reduced passenger traffic, driven by essential airport systems.The findings demonstrate that monthly aggregated data effectively capture structural and behavioural patterns in airport electricity use but do not resolve short-term operational dynamics such as real-time HVAC control or lighting responses. Policy and practical implications include prioritizing system-level energy efficiency improvements, demand management, and on-site solar photovoltaic integration to enhance resilience and support Ghana’s Renewable Energy Act and Renewable Energy Master Plan. Future research should incorporate high-resolution data, subsystem-level metering, and advanced modelling approaches to better capture short-term dynamics and improve predictive accuracy.