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Methods to Optimize Carbon Footprint of Buildings in Regenerative Architectural Design with the Use of Machine Learning, Convolutional Neural Network, and Parametric Design

Author

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  • Mateusz Płoszaj-Mazurek

    (Faculty of Architecture, Warsaw University of Technology (WUT), 00661 Warszawa, Poland)

  • Elżbieta Ryńska

    (Faculty of Architecture, Warsaw University of Technology (WUT), 00661 Warszawa, Poland)

  • Magdalena Grochulska-Salak

    (Faculty of Architecture, Warsaw University of Technology (WUT), 00661 Warszawa, Poland)

Abstract

The analyzed research issue provides a model for Carbon Footprint estimation at an early design stage. In the context of climate neutrality, it is important to introduce regenerative design practices in the architect’s design process, especially in early design phases when the possibility of modifying the design is usually high. The research method was based on separate consecutive research works–partial tasks: Developing regenerative design guidelines for simulation purposes and for parametric modeling; generating a training set and a testing set of building designs with calculated total Carbon Footprint; using the pre-generated set to train a Machine Learning Model; applying the Machine Learning Model to predict optimal building features; prototyping an application for a quick estimation of the Total Carbon Footprint in the case of other projects in early design phases; updating the prototyped application with additional features; urban layout analysis; preparing a new approach based on Convolutional Neural Networks and training the new algorithm; and developing the final version of the application that can predict the Total Carbon Footprint of a building design based on basic building features and on the urban layout. The results of multi-criteria analyses showed relationships between the parameters of buildings and the possibility of introducing Carbon Footprint estimation and implementing building optimization at the initial design stage.

Suggested Citation

  • Mateusz Płoszaj-Mazurek & Elżbieta Ryńska & Magdalena Grochulska-Salak, 2020. "Methods to Optimize Carbon Footprint of Buildings in Regenerative Architectural Design with the Use of Machine Learning, Convolutional Neural Network, and Parametric Design," Energies, MDPI, vol. 13(20), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:20:p:5289-:d:426533
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    References listed on IDEAS

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    1. Naboni, Emanuele & Natanian, Jonathan & Brizzi, Giambattista & Florio, Pietro & Chokhachian, Ata & Galanos, Theodoros & Rastogi, Parag, 2019. "A digital workflow to quantify regenerative urban design in the context of a changing climate," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    2. Pedro Nuñez-Cacho & Jaroslaw Górecki & Valentín Molina-Moreno & Francisco A. Corpas-Iglesias, 2018. "What Gets Measured, Gets Done: Development of a Circular Economy Measurement Scale for Building Industry," Sustainability, MDPI, vol. 10(7), pages 1-22, July.
    3. Elzbieta Rynska & Joanna Klimowicz & Slawomir Kowal & Krzysztof Lyzwa & Michal Pierzchalski & Wojciech Rekosz, 2020. "Smart Energy Solutions as an Indispensable Multi-Criteria Input for a Coherent Urban Planning and Building Design Process—Two Case Studies for Smart Office Buildings in Warsaw Downtown Area," Energies, MDPI, vol. 13(15), pages 1-24, July.
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    Cited by:

    1. Chauhan, Chetna & Parida, Vinit & Dhir, Amandeep, 2022. "Linking circular economy and digitalisation technologies: A systematic literature review of past achievements and future promises," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    2. Ewelina Gawell & Konrad Grabowiecki, 2021. "Modern Details in Meaningful Architecture," Sustainability, MDPI, vol. 13(24), pages 1-17, December.
    3. Mattia Manni & Franco Cotana, 2022. "Life Cycle Thinking a Sustainable Built Environment," Energies, MDPI, vol. 15(10), pages 1-2, May.
    4. Aisikaer Molake & Rui Zhang & Yihuan Zhou, 2023. "Multi-Objective Optimization of Daylight Performance and Thermal Comfort of Enclosed-Courtyard Rural Residence in a Cold Climate Zone, China," Sustainability, MDPI, vol. 15(10), pages 1-22, May.
    5. Rui Liang & Xichuan Zheng & Po-Hsun Wang & Jia Liang & Linhui Hu, 2023. "Research Progress of Carbon-Neutral Design for Buildings," Energies, MDPI, vol. 16(16), pages 1-50, August.
    6. Sultan Çetin & Catherine De Wolf & Nancy Bocken, 2021. "Circular Digital Built Environment: An Emerging Framework," Sustainability, MDPI, vol. 13(11), pages 1-34, June.
    7. Michał Pierzchalski & Elżbieta Dagny Ryńska & Arkadiusz Węglarz, 2021. "Life Cycle Assessment as a Major Support Tool within Multi-Criteria Design Process of Single Dwellings Located in Poland," Energies, MDPI, vol. 14(13), pages 1-21, June.

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