TY - GEN
T1 - Energy-aware design
T2 - European Conference on Computing in Construction, EC3 2022
AU - Cao, Jianpeng
AU - Zhang, Hang
AU - Savov, Anton
AU - Hall, Daniel M.
AU - Dillenburger, Benjamin
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - GNN
KW - spatial layout
KW - building energy performance
UR - http://www.scopus.com/inward/record.url?scp=85165954544&partnerID=8YFLogxK
U2 - 10.35490/ec3.2022.210
DO - 10.35490/ec3.2022.210
M3 - Conference contribution
SN - 9788875902261
T3 - Proceedings of the European Conference on Computing in Construction
SP - 130
EP - 137
BT - Proceedings of the 2022 European Conference on Computing in Construction
A2 - Tagliabue, Lavinia Chiara
A2 - Hall, Daniel M.
A2 - Chassiakos, Athanasios
A2 - Nikolić, Dragana
A2 - Soman, Ranjith
PB - European Council on Computing in Construction (EC3)
CY - Ixia, Rhodes, Greece
Y2 - 24 July 2022 through 26 July 2022
ER -