气候变化领域集成服务门户(CCPortal): Methods to optimize carbon footprint of buildings in regenerative architectural design with the use of machine learning; convolutional neural network; and parametric design

CCPortal

DOI	10.3390/en13205289
	Methods to optimize carbon footprint of buildings in regenerative architectural design with the use of machine learning; convolutional neural network; and parametric design
	Płoszaj-Mazurek M.; Ryńska E.; Grochulska-Salak M.
发表日期	2020
ISSN	19961073
卷号	13 期号:20
英文摘要	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. © 2020 by the authors.
英文关键词	AI; Algorithms; Artificial intelligence; Big data; Circular economy; Computer vision; GHG emissions; Life cycle assessment; Machine learning; Neural networks; Optimization; Parametric; Sustainable architecture
scopus关键词	Carbon footprint; Convolution; Convolutional neural networks; Emission control; Machine learning; Parameter estimation; Software prototyping; Turing machines; Early design phasis; Early design stages; Machine learning models; Multi Criteria Analysis; Parametric design; Parametric modeling; Regenerative designs; research methods; Architectural design
来源期刊	Energies
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/176591
作者单位	Faculty of Architecture, Warsaw University of Technology (WUT), Warszawa, 00661, Poland
推荐引用方式 GB/T 7714	Płoszaj-Mazurek M.,Ryńska E.,Grochulska-Salak M.. Methods to optimize carbon footprint of buildings in regenerative architectural design with the use of machine learning; convolutional neural network; and parametric design[J],2020,13(20).
APA	Płoszaj-Mazurek M.,Ryńska E.,&Grochulska-Salak M..(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,13(20).
MLA	Płoszaj-Mazurek M.,et al."Methods to optimize carbon footprint of buildings in regenerative architectural design with the use of machine learning; convolutional neural network; and parametric design".Energies 13.20(2020).