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

Author

Listed:
  • Da Wan

    (School of Architecture, Tianjin Chengjian University, Tianjin 300380, China
    Department of Architecture, Faculty of Environmental Engineering, The University of Kitakyushu, Kitakyushu 808-0135, Japan)

  • 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
    Innovation Institute for Sustainable Maritime Architecture Research and Technology (iSMART), Qingdao University of Technology, Qingdao 266061, China)

  • Hiroatsu Fukuda

    (Department of Architecture, Faculty of Environmental Engineering, The University of Kitakyushu, Kitakyushu 808-0135, Japan)

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.

Suggested Citation

  • Da Wan & Xiaoyu Zhao & Wanmei Lu & Pengbo Li & Xinyu Shi & Hiroatsu Fukuda, 2022. "A Deep Learning Approach toward Energy-Effective Residential Building Floor Plan Generation," Sustainability, MDPI, vol. 14(13), pages 1-18, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:8074-:d:853988
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    References listed on IDEAS

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    1. Junyi Wu & Shari Shang, 2020. "Managing Uncertainty in AI-Enabled Decision Making and Achieving Sustainability," Sustainability, MDPI, vol. 12(21), pages 1-17, October.
    2. Donghyun Lee & Suna Kang & Jungwoo Shin, 2017. "Using Deep Learning Techniques to Forecast Environmental Consumption Level," Sustainability, MDPI, vol. 9(10), pages 1-17, October.
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    Cited by:

    1. Yunfei Lin & Mingxing Song, 2024. "Exploring the Potential of Generative Adversarial Networks in Enhancing Urban Renewal Efficiency," Sustainability, MDPI, vol. 16(13), pages 1-15, July.
    2. Da Wan & Runqi Zhao & Sheng Zhang & Hui Liu & Lian Guo & Pengbo Li & Lei Ding, 2023. "A Deep Learning-Based Approach to Generating Comprehensive Building Façades for Low-Rise Housing," Sustainability, MDPI, vol. 15(3), pages 1-15, January.

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