Methods to Optimize Carbon Footprint of Buildings in Regenerative Architectural Design with the Use of Machine Learning, Convolutional Neural Network, and Parametric Design
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 modifyi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/13/20/5289 |
| _version_ | 1797551261999955968 |
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| author | Mateusz Płoszaj-Mazurek Elżbieta Ryńska Magdalena Grochulska-Salak |
| author_facet | Mateusz Płoszaj-Mazurek Elżbieta Ryńska Magdalena Grochulska-Salak |
| author_sort | Mateusz Płoszaj-Mazurek |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T15:42:04Z |
| format | Article |
| id | doaj.art-14cc6b82a6294a8db249946b8eea81bf |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T15:42:04Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-14cc6b82a6294a8db249946b8eea81bf2023-11-20T16:43:36ZengMDPI AGEnergies1996-10732020-10-011320528910.3390/en13205289Methods to Optimize Carbon Footprint of Buildings in Regenerative Architectural Design with the Use of Machine Learning, Convolutional Neural Network, and Parametric DesignMateusz Płoszaj-Mazurek0Elżbieta Ryńska1Magdalena Grochulska-Salak2Faculty of Architecture, Warsaw University of Technology (WUT), 00661 Warszawa, PolandFaculty of Architecture, Warsaw University of Technology (WUT), 00661 Warszawa, PolandFaculty of Architecture, Warsaw University of Technology (WUT), 00661 Warszawa, PolandThe 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.https://www.mdpi.com/1996-1073/13/20/5289life cycle assessmentparametricoptimizationartificial intelligenceAIalgorithms |
| spellingShingle | Mateusz Płoszaj-Mazurek Elżbieta Ryńska Magdalena Grochulska-Salak Methods to Optimize Carbon Footprint of Buildings in Regenerative Architectural Design with the Use of Machine Learning, Convolutional Neural Network, and Parametric Design Energies life cycle assessment parametric optimization artificial intelligence AI algorithms |
| title | Methods to Optimize Carbon Footprint of Buildings in Regenerative Architectural Design with the Use of Machine Learning, Convolutional Neural Network, and Parametric Design |
| title_full | Methods to Optimize Carbon Footprint of Buildings in Regenerative Architectural Design with the Use of Machine Learning, Convolutional Neural Network, and Parametric Design |
| title_fullStr | Methods to Optimize Carbon Footprint of Buildings in Regenerative Architectural Design with the Use of Machine Learning, Convolutional Neural Network, and Parametric Design |
| title_full_unstemmed | Methods to Optimize Carbon Footprint of Buildings in Regenerative Architectural Design with the Use of Machine Learning, Convolutional Neural Network, and Parametric Design |
| title_short | Methods to Optimize Carbon Footprint of Buildings in Regenerative Architectural Design with the Use of Machine Learning, Convolutional Neural Network, and Parametric Design |
| title_sort | methods to optimize carbon footprint of buildings in regenerative architectural design with the use of machine learning convolutional neural network and parametric design |
| topic | life cycle assessment parametric optimization artificial intelligence AI algorithms |
| url | https://www.mdpi.com/1996-1073/13/20/5289 |
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