Rapid urbanization has exacerbated the urban heat island effect, significantly increasing cooling energy demand. Daytime radiative cooling (RC) offers a promising zero-energy passive cooling strategy; however, urban-scale evaluations of RC potential and the specific influence of urban morphology remain scarce. This study integrates a physical RC model with the local climate zone (LCZ) framework to simulate 10,549 buildings across 85 LCZ models in Shenzhen, China. To improve spatial generalization, we developed rapid machine learning assessment models evaluated via 5-fold spatial cross-validation. Additionally, ensemble SHapley Additive exPlanations (SHAP) analysis was utilized to quantify the impacts of meteorological and morphological parameters. The results demonstrate that CatBoost and XGBoost are the optimal algorithms for predicting roof and facade RC energy savings, achieving average test-set R² values of 0.888 and 0.854, respectively. SHAP analysis revealed that meteorological conditions (primarily solar irradiance and wind speed) dictate performance, contributing 66.0% and 65.1% to roof and facade RC. Urban morphology acts as a major constraint; specifically, compact high-rise zones severely hinder facade cooling potential due to mutual shading. Finally, the annual theoretical RC energy-saving potential for roofs and facades in Shenzhen is estimated at 372.9 GWh and 446.8 GWh, respectively. Combined, this 819.8 GWh capacity could offset approximately 0.833% of the city’s 2019 total electricity consumption. This research provides urban planners with quantitative, transferable insights for mitigating energy demands in the built environment.

The increasing integration of renewable energy sources highlights the urgent need for grid flexibility, with buildings serving as key controllable loads. In this context, accurately quantifying building flexibility is essential for enabling effective demand-side management and ensuring reliable grid operations. However, several challenges hinder this quantification. To address these issues, this study proposes a comprehensive flexibility quantification framework. First, a novel RC-Mapping model incorporating an Enumerate-Comparison Method is proposed. The RC-Mapping model can capture the thermal behavior of both the building and the air conditioning system, while the Enumerate-Comparison Method can initialize state parameters in the RC-Mapping model. Compared with the conventional approach, as validated by the experiment, the proposed method can substantially improve RMSE for indoor temperature prediction from 0.542 ℃ to 0.266 ℃, and the MAPE for flexibility quantification from 27.58% to 10.98%. Second, the study introduces the power reduction-duration curve and temperature variation curves to characterize flexibility from both grid and building perspectives. Specifically, based on the analysis of the power reduction-duration curve, this study provides a systematic analysis of four sources of flexibility and their underlying mechanisms, including the thermal storage of the building, the thermal storage of the HVAC system, the increase of coefficient of performance (COP), and the reduction in cooling load. Finally, the study investigates the impact of uncertainties in COP and internal heat gains on flexibility quantification. According to the result, it is recommended to slightly underestimate the COP and overestimate the internal heat gain schedule to improve the accuracy of flexibility quantification.

Climate change presents a major threat to the built environment and therefore requires reliable future climate data for building performance simulation (BPS). The implementation of advanced statistical downscaling methods remains difficult in BPS studies because specific historical weather data and complex implementation procedures are usually requested. The current statistical downscaling methods that are frequently used in BPS analysis were rarely validated against measurements to see if ongoing climate change process and weather extremes can be represented. This paper presents a new Distribution Adjusted Temporal Mapping (DATM) technique for downscaling future hourly weather data from the monthly GCM (Global Climate Model) data with Typical Meteorological Year (TMY) data being the baseline. The proposed method involves fitting probability distributions to TMY data for each climate variable, modifying these distributions according to the projected monthly changes from GCMs, and then mapping the future hourly weather data from the adjusted distributions. DATM is compared with the “morphing” technique for various climate variables and locations, and is validated against ten years onsite measured hourly weather data from 2015 to 2024. The outcomes reveal that DATM outperforms the morphing method in temperature downscaling in terms of reproducing climate variabilities and extreme events. For relative humidity and wind speed, DATM is slightly better in capturing the full range of variables even though both methods have their limitations. For solar radiation, DATM can reflect realistic peak solar radiation prediction in future climate downscaling. It also shows better performance in capturing the changes in temperature variability and extremes that are essential for the overall building resilience analysis. The results of both methods depend on climate zones and variables, which underlines the necessity of considering regional factors in climate data preprocessing. With climate change affecting the built environment, the proposed method in this research offers BPS researchers a more reliable way of evaluating future building performance under future emission scenarios.