Abstract
Abstract
This study examines the transferability of a passive shading optimization approach under extreme-heat conditions in Southwest China. A university-building prototype serves as the geometric reference, and its envelope and operation settings are then adapted for Chengdu and Chongqing in accordance with the relevant building standards. The workflow combines weather-data preprocessing, a simplified overhang-based shading representation, dynamic thermal-response calculation, and differential-evolution-based parameter search. To separate routine and extreme-condition performance, the analysis was organized into three parts: an annual benchmark under typical meteorological year (TMY) conditions, a 2023 extreme-summer scenario comparison, and a Monte Carlo robustness analysis. Under the Southwest China cases, the optimized scheme reduced cooling energy use by approximately 43–45% and peak load by more than 50% in the reported scenarios. The Orlando reference scenario showed a smaller improvement, indicating that the performance gain depended strongly on climate context and baseline envelope characteristics. The Monte Carlo analysis further showed that the optimized scheme remained superior to the baseline across the sampled perturbation cases, with lower-tail energy-saving performance remaining above 40%. The retained annual, extreme-summer, and perturbation results all point in the same direction: the workflow is more useful as a comparative stress-testing tool than as a substitute for measured building validation. The study does not claim full real-world validation: the weather-adjustment procedure is treated as a scenario-construction assumption, and the simplified shading model does not fully represent façade-specific projection geometry.
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@article{Long2026Passive,
title = {Passive Shading Optimization for University Buildings in Southwest China: A Simulation-Based Transferability Assessment with Heatwave Stress Testing},
author = {Xiaojie Long and Rui Yuan and Chengxiang Yan and G. Zhao and Weizheng Zhang and Intan Bayani Zakaria},
journal = {Urban and Building Science},
year = {2026},
doi = {10.53941/ubs.2026.100022},
url = {https://doi.org/10.53941/ubs.2026.100022}
}
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