Open access
Date
2022Type
- Conference Paper
ETH Bibliography
yes
Altmetrics
Abstract
Graph Neural Networks (GNNs) have become a popular toolkit for generative floor plan design. Although design variation has improved greatly, few studies consider non- geometrical characteristics, such as building energy performance, in the generative design process. This paper presents a GNN-based approach to predict the energy performance for floor plan customization (energy-aware design). The approach lays the foundation for a performance-aware generative design using GNN. The results show that the GNN can achieve high accuracy in energy performance prediction. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000590952Publication status
publishedExternal links
Editor
Book title
Proceedings of the 2022 European Conference on Computing in ConstructionJournal / series
Computing in ConstructionVolume
Pages / Article No.
Publisher
University of TurinEvent
Subject
Graph Neural Networks; floor plan; building energy performanceOrganisational unit
09566 - Dillenburger, Benjamin / Dillenburger, Benjamin
09624 - Hall, Daniel M. (ehemalig) / Hall, Daniel M. (former)
More
Show all metadata
ETH Bibliography
yes
Altmetrics