Search

• Heatwave events were quantitatively classified based on duration and intensity. • The impact of different heatwave types on indoor overheating was quantified. • Short and moderate heatwaves were the most common type in eight cities in China. • Long and intensive heatwaves had the highest impact on indoor overheating. • High-rise apartments had the highest indoor overheating risk during heatwaves. More frequent and extreme heatwaves result in an increased risk of overheating in residential buildings. The utilization of heatwave weather files is fundamental in evaluating the thermal resilience of buildings. However, limited studies have focused on quantitative classification methods that consider multiple heatwave characteristics and how buildings respond to different heatwave types. Therefore, this study aims to propose a quantitative method for classifying heatwaves by considering the duration and intensity, while also providing the thresholds for duration and intensity to identify different types of heatwaves. Subsequently, the impacts of different types of heatwaves on indoor overheating was quantified and compared through simulation of indoor thermal environment in three prototype residential buildings in China. The indoor overheating risk was then evaluated based on the indoor overheating degree. The results revealed four types of heatwaves: short and moderate, short and intense, long and moderate, and long and intense. The short and moderate heatwaves represented the majority. While the long and intense heatwaves had the highest influence on indoor overheating risks. Hence, these two types of heatwaves are suggested to be considered in building resilience analysis. The proposed method demonstrated its effectiveness in classifying heatwaves based on the impact on indoor environments. [Display omitted] [ABSTRACT FROM AUTHOR]

Heterogeneity and stochasticity, two main aspects of uncertainty in occupant behavior (OB), at the urban scale are not featured in most current urban building energy modeling (UBEM) platforms, and their respective impacts on urban-scale building energy consumption remain unclear. We aimed to introduce OB uncertainty into the UBEM workflow and assess the differences in the impacts of heterogeneity and stochasticity on cooling demand. Different OB models were integrated into the cooling demand simulation of residential building stocks, considering the heterogeneity and stochasticity in both occupancy and energy-use behavior. The impacts of heterogeneity and stochasticity on urban-scale cooling demand and its applications for different purposes in UBEM are discussed. We found that stochasticity in occupancy decreased the peak cooling demand by 54%, whereas the uncertainty in air-conditioning (AC) behavior had little effect. Heterogeneity is the main reason for the diversity in cooling demand, whereas stochasticity better reflects the dynamics of cooling demand., which Occupancy and AC behavior models with higher fidelity are required to obtain results with higher spatial or temporal resolution should be selected according to the UBEM applications. The proposed approach will contribute to the development of appropriate urban-scale occupant behavior models and bridge urban mobility with occupant-centric UBEMs. • Heterogeneity and stochasticity in both occupancy and AC behavior were considered. • Different impacts of heterogeneity and stochasticity in OB on UBEM was assessed. • The sufficient fidelities for OB models for different UBEM use cases were discussed. • Insights on developing appropriate OB models in occupant-centric UBEM were provided. [ABSTRACT FROM AUTHOR]