Proceedings of the 2022 European Conference on Computing in Construction


Energy-aware design: predicting building performance from layout graphs

Jianpeng Cao, Hang Zhang, Anton Savov, Daniel Hall, Benjamin Dillenburger
ETH Zurich, Switzerland

DOI: 10.35490/EC3.2022.210
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.
Keywords: Graph Neural Networks, floor plan, building energy performance

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