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• 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]

Assessing the urban-scale building photovoltaic (PV) potential is important for designing urban environments, retrofitting existing structures, or integrating PVs with grids. However, few studies have considered high-temporal-resolution simulations, the facade PV potential, and a comprehensive PV model simultaneously; thus, the overall accuracy of the estimation of PV potential may be limited. Therefore, this study developed an integrated framework to assess the urban-scale PV potential of rooftops and facades at high spatiotemporal resolution. The proposed approach integrates an anisotropic sky diffuse model, a vector-based shading calculation method, and a temperature-related PV performance model. The annual PV potential and spatial/temporal characteristics were analyzed in a case study of over 170,000 buildings in Beijing. The results showed that the estimated rooftop PV power generation was 7.55 TWh/y, whereas the facade PV power generation was 18.07 TWh/y, which was 239% of the rooftop PV yield. The integrated model estimated PV yield with higher accuracy than the simplified models by depicting more details. The proposed approach can be applied to the large-scale assessment of future energy systems with increasing penetration of PVs, and the results can support effective policies for the integration of PVs into the built environment in dense cities. [Display omitted] • High-accuracy models of sky diffusion, shading, and PV performance were integrated. • Facade PV potential was equivalent to 239% of the rooftop PV potential in Beijing. • Shading affects less on rooftops, but reduces up to 15% of facade PV yield in blocks. • PV potential on facades is greater downtown than in suburbs, while similar on roofs. [ABSTRACT FROM AUTHOR]

• Categorized buildings by their operational similarities using smart meter data. • Showed the necessity of operation-based categorization for building benchmarking. • Both categorization and benchmarking results outperformed two baselines. • The proposed framework led to great energy saving potential. Current building energy benchmarking systems categorize buildings into peer groups by static characteristics such as climate zones and building types, which cannot account for the huge variation in building operations. Grouping buildings with diverse operations for benchmarking could result in misleading results. The smart meters provide an opportunity to feature the dynamic characteristics of building operations, but proper data mining techniques are needed to use the data for benchmarking. Accordingly, this paper proposes a framework that makes use of the time-series energy consumption data to categorize buildings by their operations and conduct energy benchmarking within each category. The proposed framework is based on 3-step K-means clustering and consists of two main parts: (1) Operation quantification, and (2) Building categorization and benchmarking. The framework was tested on a dataset of 81 buildings in Singapore. Two baseline methods were also implemented for comparison. The results show that the proposed framework successfully categorized the buildings by their operational similarities and made a significant impact on the energy benchmarking results. Further, the superiority of operation-based energy benchmarking is manifested by investigating two typical buildings where the proposed framework disagreed with the baselines. It is necessary to integrate building operations in energy benchmarking so that the energy performance is evaluated more precisely and higher energy saving potential can be uncovered. [ABSTRACT FROM AUTHOR]