Ren Juan, Meng Yuan, Liu Yu
School of Architecture, Xi'an University of Architecture and Technology, Xi'an, Shaanxi, China.
School of Architecture, Chang'an University, Xi'an, Shaanxi, China.
PLoS One. 2025 Sep 2;20(9):e0330913. doi: 10.1371/journal.pone.0330913. eCollection 2025.
The pursuit of global carbon neutrality necessitates addressing the dual challenge of enhancing solar energy utilization while improving thermal comfort in high-rise residential areas, particularly in Yulin, northern Shaanxi, China, where abundant solar resources exist but maximizing solar acquisition often compromises summer thermal environment quality. This resource-comfort contradiction highlights the need for balanced architectural strategies in regions with pronounced seasonal variations. Building morphological parameter optimization is crucial for balancing annual solar energy capture against summer overheating risks, yet research remains insufficient. This study developed parametric layout models using Rhino-Grasshopper, considering key parameters including building length, width, height, density, floor area ratio, and south-facing angle deviation. Multi-objective optimization was conducted using NSGA-II genetic algorithm under regulatory constraints, while combining traditional regression analysis with convolutional neural networks (CNN) to investigate the influence mechanisms of these morphological parameters. Results indicate that the optimized building morphology can increase annual solar radiation acquisition (SRA) by 2.57% while maintaining comfortable summer Universal Thermal Climate Index (UTCI) values, effectively balancing solar energy capture and outdoor thermal comfort. Regression analysis revealed a positive correlation between building length and summer UTCI (r = 0.73), whereas CNN identified a negative correlation (-0.45). Both methods identified similar parameter combinations affecting SRA, with CNN demonstrating superior capability in capturing complex non-linear relationships. These findings provide evidence-based design guidelines specific to Yulin while offering implications for sustainable residential development in similar climates, advancing the integration of climate-adaptive design strategies.
追求全球碳中和需要应对双重挑战，即提高太阳能利用率，同时改善高层住宅区的热舒适度，特别是在中国陕北榆林地区，那里太阳能资源丰富，但最大化太阳能获取往往会损害夏季热环境质量。这种资源与舒适度的矛盾凸显了在季节变化明显的地区采取平衡建筑策略的必要性。建筑形态参数优化对于平衡年度太阳能捕获与夏季过热风险至关重要，但相关研究仍然不足。本研究使用Rhino-Grasshopper开发了参数化布局模型，考虑了建筑长度、宽度、高度、密度、容积率和南向角度偏差等关键参数。在监管约束下，使用NSGA-II遗传算法进行多目标优化，同时将传统回归分析与卷积神经网络（CNN）相结合，以研究这些形态参数的影响机制。结果表明，优化后的建筑形态可以在保持夏季舒适的通用热气候指数（UTCI）值的同时，将年度太阳辐射获取量（SRA）提高2.57%，有效地平衡了太阳能捕获和室外热舒适度。回归分析显示建筑长度与夏季UTCI之间呈正相关（r = 0.73），而CNN则识别出负相关（-0.45）。两种方法都识别出了影响SRA的相似参数组合，CNN在捕捉复杂非线性关系方面表现出卓越能力。这些发现为榆林提供了基于证据的设计指南，同时为类似气候条件下的可持续住宅开发提供了启示，推动了气候适应性设计策略的整合。
