eBIM-GNN : Fast and Scalable energy analysis through BIMs and Graph Neural Networks

TL;DR

This paper introduces eBIM-GNN, a fast and scalable method using BIMs and Graph Neural Networks for energy analysis, enabling efficient energy-efficient building prototypes and analysis at city scale.

Contribution

It proposes a novel approach to generate energy-efficient building prototypes using GNNs and BIMs, addressing scalability issues in urban energy analysis.

Findings

Efficient prototype generation for large-scale building analysis

Demonstrated approach on synthetic datasets

Improved speed and scalability over existing methods

Abstract

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.