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

Mateusz Płoszaj-Mazurek, Elżbieta Ryńska and Magdalena Grochulska-Salak
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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

Energies, 2020, vol. 13, issue 20, 1-19

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.

Keywords: life cycle assessment; parametric; optimization; artificial intelligence; AI; algorithms; GHG emissions; sustainable architecture; big data; machine learning; neural networks; computer vision; circular economy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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