Applicability of different extreme weather datasets for assessing indoor overheating risks of residential buildings in a subtropical high-density city

Abstract

A failure to consider extreme weather conditions in building design can lead to poor resilience and low passive survivability of buildings. Several approaches exist to construct extreme weather files for building performance assessments. Since literature comparing such extreme weather datasets is limited, this study aims to examine the applicability and limitations of the Summer Reference Year (SRY), Typical Hot Year-Event (THY-E), Typical Hot Year-Intensity (THY-I), Extreme Meteorological Year (XMY), and Typical Meteorological Year (TMY) in assessing indoor overheating risks of residential buildings, especially in a subtropical high-density living environment like Hong Kong. By comparing the simulated temperature with on-site measurements on different summer days, building physical parameters of six typical residential archetypes are calibrated in EnergyPlus. Their indoor overheating risks are then evaluated by two overheating criteria: the static extreme and adaptive thermal comfort thresholds. Results reveal that using the THY-I can generally examine the severest daytime overheating, but may fail to evaluate the maximum heat intensity of well-shaded buildings. The longest duration of daytime overheating is observed when using the THY-E, and the severest and longest nighttime overheating are found using the XMY. By contrast, using the SRY is unsuitable for assessing nighttime overheating risks. This study suggests advantages of using a combination of different extreme weather datasets, e.g., the XMY with the THYs, to assess overheating risks in high-density settings over the use of a single weather dataset. Furthermore, the building type with balconies and openable windows coated with low-e consistently demonstrates better performance than the other types.