Meeting the growing demand for thermal comfort without substantially increasing building energy consumption presents a critical challenge to sustainable building development. Yet, the geographic and climatic variability of residential cooling demand remains insufficiently understood. This study establishes a quantitative link between regional climate characteristics, household air-conditioning operation, and cooling energy use by analyzing minute-level operational data from 5004 residential air conditioner (A/C) units across six Chinese cities representing four major climate zones. The results reveal clear regional disparities in cooling behavior and indoor comfort conditions. Indoor air temperature thresholds for A/C activation differ by more than 1.5 °C among the studied regions. Residents in hot-humid Guangzhou exhibit responsive and multi-scale cooling usage patterns, whereas those in the cold-climate city of Shenyang demonstrate habit-driven and trend-dominated behaviors with minimal adjustments. The analysis further quantifies regional differences in how indoor comfort temperatures respond to outdoor thermal conditions. As outdoor temperatures increase, the acceptable indoor comfort range systematically shifts upward, with pronounced variations among individuals and climatic zones. Incorporating these observed patterns of indoor comfort temperature variation with outdoor conditions into building energy simulations suggests potential energy savings of up to 12 % annually and reductions exceeding 10 % in peak cooling demand compared with a fixed 26 °C thermostat setting. These findings provide practical evidence that accounting for real-world operational behavior under varying climatic conditions can substantially improve the accuracy of residential cooling demand forecasts and support more regionally tailored energy efficiency strategies. • Analyzed residential cooling behavior across four Chinese climate zones. • Significant regional differences found in residential cooling usage behavior. • Hot-humid residents show over 1.5 °C higher comfort thresholds than cold-climate ones. • Integrating adaptive comfort models enables notable energy savings in cooling.
Lyu et al. (Sun,) studied this question.