Energy-aware design
Predicting building performance from layout graphs
Jianpeng Cao (ETH Zürich)
Hang Zhang (ETH Zürich)
Anton Savov (ETH Zürich)
Daniel M. Hall (ETH Zürich)
Benjamin Dillenburger (ETH Zürich)
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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.
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