eBIM-GNN : Fast and Scalable energy analysis through BIMs and Graph Neural Networks
This work addresses the need for scalable energy efficiency improvements in existing urban buildings, though it appears incremental as it builds on existing BIM and GNN methods.
The paper tackles the problem of slow and expensive energy analysis for large-scale building communities by proposing a method to create prototype buildings for efficient matching and statistics generation, demonstrating its approach on a synthetic dataset.
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.