Energy-aware design

Predicting building performance from layout graphs

Conference Paper (2022)
Author(s)

Jianpeng Cao (ETH Zürich)

Hang Zhang (ETH Zürich)

Anton Savov (ETH Zürich)

Daniel M. Hall (ETH Zürich)

Benjamin Dillenburger (ETH Zürich)

Affiliation
External organisation
DOI related publication
https://doi.org/10.35490/ec3.2022.210
More Info
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Publication Year
2022
Language
English
Affiliation
External organisation
Pages (from-to)
130-137
ISBN (print)
9788875902261
ISBN (electronic)
978-8-875902-26-1

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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