论文标题
EBIM-GNN：通过BIM和图形神经网络快速，可扩展的能量分析
eBIM-GNN : Fast and Scalable energy analysis through BIMs and Graph Neural Networks
论文作者
论文摘要
建筑信息建模已用于分析和提高建筑物的能源效率。通过解构和改造，它在现有建筑物中表现出了巨大的希望。目前在没有能源节省的情况下建造的当前城市现在要求更好地在能源利用中变得聪明。但是，现有的生成BIMS的方法在建筑物的基础上起作用。因此，当我们扩展到更大的社区甚至整个城镇或城市时，它们的速度很慢又昂。在本文中，我们提出了一种创建原型建筑物的方法，使我们能够非常有效地匹配和生成统计信息。我们的方法为现有建筑物提出了更好的节能原型。现有建筑物被识别并位于3D点云中。我们对合成数据集进行实验，以证明我们的方法的工作。
Building Information Modeling has been used to analyze as well as increase the energy efficiency of the buildings. It has shown significant promise in existing buildings by deconstruction and retrofitting. Current cities which were built without the knowledge of energy savings are now demanding better ways to become smart in energy utilization. However, the existing methods of generating BIMs work on building basis. Hence they are slow and expensive when we scale to a larger community or even entire towns or cities. In this paper, we propose a method to creation of prototype buildings that enable us to match and generate statistics very efficiently. Our method suggests better energy efficient prototypes for the existing buildings. The existing buildings are identified and located in the 3D point cloud. We perform experiments on synthetic dataset to demonstrate the working of our approach.
