Quantifying building thermal resilience under extreme heat: A change-point regression analysis of retrofit performance for urban overheating adaptation

초록

Extreme heat driven by climate change increasingly challenges building energy performance and indoor thermal comfort by intensifying cooling demand. Although typical and extreme weather-year datasets are widely used to estimate future energy consumption, limited attention has been paid to how buildings physically respond to extreme heat. This study applies a three-parameter change-point regression to daily cooling energy under extreme warm-year conditions to characterize the cooling energy-temperature response through the change-point temperature (beta 1), temperature sensitivity (beta 2), and base load (beta 3), while evaluating cooling peak demand and indoor thermal conditions. Residential and office prototypes were simulated across four Korean climate zones under SSP5-8.5 for present, mid-century, and late-century climates using Typical Downscaled Year and Extreme Warm Year weather datasets. In the extreme warm-year baseline for the Seoul medium office, beta 3 increased by 112.7% and beta 1 rose by 2.79 degrees C in the 2080s relative to the base year. Passive envelope retrofits increased beta 3 by 30%, whereas active measures reduced beta 2 by 25%. The combined strategy decreased beta 1 by 2.3 degrees C and reduced beta 2 and beta 3 by 41% and 2.4%, respectively. Combined retrofit reduced cooling peak demand by 23%-28% for medium offices and 38%-46% for high-rise apartments. Apartments maintained near-zero SET-based heat-stress exposure, whereas medium offices showed limited or adverse warm-discomfort changes. Overall, these results suggest that operational resilience under extreme warm conditions is better assessed through complementary dimensions-cooling energy-temperature response, peak demand, and occupant thermal safety-than through aggregate energy metrics alone, while change-point parameters provide useful diagnostics for the energy-response dimension.

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