In this research, a novel open-source methodology is presented to perform building cluster and neighborhood simulation by integrating individual archetypes building energy models (BEM). This methodology merges various building Energy Plus models employing a programming language. For the demonstration of generalized methodology, Matlab is used, however, methodology can be implemented by other programming languages such as Python. A given BEM can be employed multiple times to avoid its remodeling in a traditional cluster model setup. The proposed workflow is successfully validated for low, mid, and high-rise mixed-use building clusters with a maximum deviation of ± 2.70% in various annual performance parameters compared to the summation of individual BEM simulations. A sensitivity analysis is also performed to quantify the root mean square deviation (RMSD) between cluster and individual BEM simulations by varying building distance to height ratios along north–south and east–west axes. A significance index on a ten-point scale is proposed to evaluate the importance of cluster simulations over the individual BEM simulations (where the sum of individual BEM results can be used to replace cluster simulations). For instance, the significance index found is to be highest for high-rise building cluster, suggesting thus that building cluster simulations yield more adequate results than individual BEM simulations.

在这项研究中，提出了一种新颖的开源方法，通过集成单个原型建筑能量模型 (BEM) 来执行建筑集群和邻里模拟。这种方法融合了使用编程语言的各种建筑 Energy Plus 模型。对于通用方法的演示，使用了 Matlab，但是，可以通过 Python 等其他编程语言来实现方法。可以多次使用给定的 BEM，以避免在传统的集群模型设置中对其进行重构。与单个 BEM 模拟的总和相比，所提出的工作流程成功地验证了低、中和高层混合用途建筑群的各种年度性能参数的最大偏差为 ± 2.70%。RMSD）通过沿南北和东西轴改变建筑物距离与高度的比率，在集群和单个 BEM 模拟之间进行。提出了一个十点尺度的显着性指数来评估集群模拟对单个 BEM 模拟的重要性（其中单个 BEM 结果的总和可用于替代集群模拟）。例如，高层建筑群的显着性指数最高，这表明建筑群模拟比单个 BEM 模拟产生更充分的结果。