一种综合基因算法-机器学习方法,用于优化玉林高层住宅区的形态
Juan Ren1,2, Yuan Meng3, Yu Liu4
1School of Architecture, Xi'an University of Architecture and Technology, Xi'an, Shaanxi, China.
PloS one
|September 2, 2025
概括
优化了林的建筑设计, 增加了2.57%的太阳能, 这项研究将太阳能采用与可持续高层住宅的热舒适度相结合.
科学领域:
- 可持续的建筑
- 城市气候学
- 能源效率问题
背景情况:
- 全球碳中和目标需要平衡太阳能使用和住宅舒适度.
- 林的高层住宅区面临一个矛盾:太阳能资源丰富,
- 优化建筑形态是管理太阳能增益和热舒适度的关键, 但研究有限.
研究的目的:
- 优化林高层住宅区的建筑形态参数
- 为了平衡每年的太阳辐射采集量 (SRA) 和夏季的热舒适度 (通用热气候指数 - UTCI).
- 使用先进的分析方法研究形态参数对SRA和UTCI的影响.
主要方法:
- 使用Rhino-Grasshopper开发了参数布局模型.
- 在监管约束下使用NSGA-II遗传算法进行多目标优化.
- 结合回归分析和卷积神经网络 (CNN) 来分析参数影响.
主要成果:
- 在保持舒适的夏季UTCI的同时,优化形态增加了2.57%的年SRA.
- 回归分析显示建筑长度与夏季UTCI之间存在正相关性 (r=0.73).
- 在捕捉SRA的参数影响方面,CNN发现了复杂的非线性关系,其表现优于回归.
结论:
- 优化的建筑形态有效地平衡了太阳能捕获和室外热舒适度.
- 这些发现为林和类似的气候提供了基于证据的设计指南.
- 强调将适应气候的设计策略纳入城市规划的潜力.
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