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Data on external walls from a multi-objective simulation for cold climates

Data are related to the multi-objective optimization process applied to the building materials to obtain high energy-efficient precast walls for cold climate. The methodology has been explained on the paper entitled “High performance precast external walls for cold climate by a multi criteria method...

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Detalles Bibliográficos
Autores principales: Baglivo, Cristina, Congedo, Paolo Maria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066207/
https://www.ncbi.nlm.nih.gov/pubmed/27766289
http://dx.doi.org/10.1016/j.dib.2016.09.048
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author Baglivo, Cristina
Congedo, Paolo Maria
author_facet Baglivo, Cristina
Congedo, Paolo Maria
author_sort Baglivo, Cristina
collection PubMed
description Data are related to the multi-objective optimization process applied to the building materials to obtain high energy-efficient precast walls for cold climate. The methodology has been explained on the paper entitled “High performance precast external walls for cold climate by a multi criteria methodology” (Baglivo and Congedo, 2016) [1]. The modeFRONTIER rel.4.3 optimization tool has been used to evaluate the dynamic behaviour of the building components in accordance with the UNI EN ISO 13786:2008 and to obtain a multitude of high efficiency configurations. The results are divided into three categories thick, thin and ultra-thin precast walls, in accordance with their thicknesses. The input data are the building materials with their thermal properties, sustainability characteristics and the supply and installation costs. The output values of the simulations are adapted to the cold climate and based on thermal properties, costs and sustainability score. Several combinations of external precast walls have been shown as optimal for cold climate.
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spelling pubmed-50662072016-10-20 Data on external walls from a multi-objective simulation for cold climates Baglivo, Cristina Congedo, Paolo Maria Data Brief Data Article Data are related to the multi-objective optimization process applied to the building materials to obtain high energy-efficient precast walls for cold climate. The methodology has been explained on the paper entitled “High performance precast external walls for cold climate by a multi criteria methodology” (Baglivo and Congedo, 2016) [1]. The modeFRONTIER rel.4.3 optimization tool has been used to evaluate the dynamic behaviour of the building components in accordance with the UNI EN ISO 13786:2008 and to obtain a multitude of high efficiency configurations. The results are divided into three categories thick, thin and ultra-thin precast walls, in accordance with their thicknesses. The input data are the building materials with their thermal properties, sustainability characteristics and the supply and installation costs. The output values of the simulations are adapted to the cold climate and based on thermal properties, costs and sustainability score. Several combinations of external precast walls have been shown as optimal for cold climate. Elsevier 2016-10-05 /pmc/articles/PMC5066207/ /pubmed/27766289 http://dx.doi.org/10.1016/j.dib.2016.09.048 Text en © 2016 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Baglivo, Cristina
Congedo, Paolo Maria
Data on external walls from a multi-objective simulation for cold climates
title Data on external walls from a multi-objective simulation for cold climates
title_full Data on external walls from a multi-objective simulation for cold climates
title_fullStr Data on external walls from a multi-objective simulation for cold climates
title_full_unstemmed Data on external walls from a multi-objective simulation for cold climates
title_short Data on external walls from a multi-objective simulation for cold climates
title_sort data on external walls from a multi-objective simulation for cold climates
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066207/
https://www.ncbi.nlm.nih.gov/pubmed/27766289
http://dx.doi.org/10.1016/j.dib.2016.09.048
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