Energy-aware design

Predicting building performance from layout graphs

Conference paper (2022)

Authors

Jianpeng Cao ETH Zürich

Hang Zhang ETH Zürich

Anton Savov ETH Zürich

Daniel M. Hall ETH Zürich

Benjamin Dillenburger ETH Zürich

Affiliation

External organisation

GNN Building energy performance Spatial layout

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Published Date

2022

Language

English

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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.