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Generative design and performance optimization of residential buildings based on parametric algorithm

机译：基于参数算法的住宅建筑物生成设计与性能优化

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As passive green design and performance optimization are very important in the early design stage of green residences to reduce building energy consumption, energy-efficient green design and artificial intelligence technology are combined in this work for the design of residential buildings. A parametric generative algorithm is developed to automatically generate design schemes of typical Chinese urban residences based on performance-oriented design flow. By summarizing the workflow of architects, the algorithm based on the technical route is as follows: 1) spatial form features extraction of residence database; 2) automatic generation and energy simulation of new design schemes; 3) evaluation and screening of generated schemes. The generative algorithm is formulated with Rhino/Grasshopper and Python. Via a residence design case in Beijing, the design scheme with the lowest cooling and heating load among 1,595 automatically generated schemes is deemed as the optimal scheme. The total load of the optimal scheme is 15.8% lower than that of the worst scheme and 4.2% lower than that of the original scheme. Therefore, the parametric generative design of residences is able to facilitate passive green design in the early design stage and enhance energy efficiency without the increase of construction costs. (c) 2021 Elsevier B.V. All rights reserved.
机译：由于被动绿色设计和性能优化在绿色居住的早期设计阶段非常重要，以降低建筑能源消耗，节能绿色设计和人工智能技术在这项工作中结合了住宅建筑的设计。开发了参数生成算法，以基于以性能为导向的设计流动自动生成典型的中国城市住宅的设计方案。通过总结架构师的工作流程，基于技术路由的算法如下：1）空间形式的特征提取居住地数据库; 2）新设计方案的自动生成和能量模拟; 3）评估和筛选产生的方案。生成算法与犀牛/蚱蜢和Python配制。通过在北京的居住设计案例，1,595之间的冷却和加热负荷的设计方案被视为最佳方案。最佳方案的总负荷低于最差方案的15.8％，低于原始方案的4.2％。因此，居住的参数生成设计能够在早期设计阶段促进被动绿色设计，并在不增加施工成本的情况下提高能效。 （c）2021 elestvier b.v.保留所有权利。

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