A Deep Learning Approach toward Energy-Effective Residential Building Floor Plan Generation

Da Wan, Xiaoyu Zhao, Wanmei Lu, Pengbo Li, Xinyu Shi and Hiroatsu Fukuda
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Da Wan: School of Architecture, Tianjin Chengjian University, Tianjin 300380, China
Xiaoyu Zhao: School of Architecture, Tianjin Chengjian University, Tianjin 300380, China
Wanmei Lu: Tianjin Architecture Design Institute Co., Ltd., Tianjin 300074, China
Pengbo Li: School of Architecture, Tianjin Chengjian University, Tianjin 300380, China
Xinyu Shi: Department of Architecture, Faculty of Environmental Engineering, The University of Kitakyushu, Kitakyushu 808-0135, Japan
Hiroatsu Fukuda: Department of Architecture, Faculty of Environmental Engineering, The University of Kitakyushu, Kitakyushu 808-0135, Japan

Sustainability, 2022, vol. 14, issue 13, 1-18

Abstract: The ability of deep learning has been tested to learn graphical features for building-plan generation. However, whether the deeper space allocation strategies can be obtained and thus reduce energy consumption has still not been investigated. In the present study, we aimed to train a neural network by employing a characterized sample set to generate a residential building floor plan (RBFP) for achieving energy reduction effects. The network is based on Pix2Pix, including two sub-models: functional segmentation layout (FSL) generation and building floor plan (BFP) generation. To better characterize the energy efficiency, 98 screened floor plans of Solar Decathlon (SD) entries were labeled as the sample set. The data augmentation method was adopted to improve the performance of the FSL sub-model after the preliminary testing. Three existing residential buildings were used as cases to observe whether the network-generated RBFP gained the effect of decreasing energy consumption with decent space allocation. The results showed that, under the same simulation settings and building exterior profile (BEP) conditions, the function arrangement of the generated scheme was more reasonable compared to the original scheme in each case. The annual total energy consumption was reduced by 13.38%, 12.74%, and 7.47%, respectively. In conclusion, trained by the sample set that characterizes energy efficiency, the RBFP generation network has a positive effect in both optimizing the space allocation and reducing energy consumption. The implemented data augmentation method can significantly improve the network’s training results with a small sample size.

Keywords: deep learning; generative design; energy-effective design; Pix2Pix; data augmentation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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