ANN usefulness in building enhanced with PCM: Efficacy of PCM installation location

The building walls are composed of several layers to protect the building from temperature fluctuations of the external environment. Increasing the number of layers in a building certainly strengthens the wall's ability to reduce heat transfer. But on the other hand, it increases the thickness...

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Published in:Journal of Building Engineering
Main Author: Hai T.; Said N.M.; Zain J.M.; Sajadi S.M.; Mahmoud M.Z.; Aybar H.Ş.
Format: Article
Language:English
Published: Elsevier Ltd 2022
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134707692&doi=10.1016%2fj.jobe.2022.104914&partnerID=40&md5=061733843392389b0dda5ad01ba8b8f7
id 2-s2.0-85134707692
spelling 2-s2.0-85134707692
Hai T.; Said N.M.; Zain J.M.; Sajadi S.M.; Mahmoud M.Z.; Aybar H.Ş.
ANN usefulness in building enhanced with PCM: Efficacy of PCM installation location
2022
Journal of Building Engineering
57

10.1016/j.jobe.2022.104914
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134707692&doi=10.1016%2fj.jobe.2022.104914&partnerID=40&md5=061733843392389b0dda5ad01ba8b8f7
The building walls are composed of several layers to protect the building from temperature fluctuations of the external environment. Increasing the number of layers in a building certainly strengthens the wall's ability to reduce heat transfer. But on the other hand, it increases the thickness of the walls and thus reduces the useful floor area of the building, which has no economic reason. In this study, the effect of reinforcing building walls against heat transfer using PCM was discussed. At constant PCM thickness, the installation location of this material varies from the outermost to the innermost. At the best location, the installation of PCM inside the wall/roof resulted in energy savings by 14.9 [Formula presented] 19.6 [Formula presented] and taking into account their area, for the whole building, energy-saving was 15.9 [Formula presented]. The effectiveness of ANN on PCM applications was amazing. Annual energy consumption for the building is 64.236 [Formula presented]. The neural network, by establishing a connection between the data predicts the annual energy consumption of 64.209 [Formula presented]. Therefore, the error was less than 0.04%. The monthly analysis also shows that for monthly EC, the error was less than 1.5%. © 2022 Elsevier Ltd
Elsevier Ltd
23527102
English
Article

author Hai T.; Said N.M.; Zain J.M.; Sajadi S.M.; Mahmoud M.Z.; Aybar H.Ş.
spellingShingle Hai T.; Said N.M.; Zain J.M.; Sajadi S.M.; Mahmoud M.Z.; Aybar H.Ş.
ANN usefulness in building enhanced with PCM: Efficacy of PCM installation location
author_facet Hai T.; Said N.M.; Zain J.M.; Sajadi S.M.; Mahmoud M.Z.; Aybar H.Ş.
author_sort Hai T.; Said N.M.; Zain J.M.; Sajadi S.M.; Mahmoud M.Z.; Aybar H.Ş.
title ANN usefulness in building enhanced with PCM: Efficacy of PCM installation location
title_short ANN usefulness in building enhanced with PCM: Efficacy of PCM installation location
title_full ANN usefulness in building enhanced with PCM: Efficacy of PCM installation location
title_fullStr ANN usefulness in building enhanced with PCM: Efficacy of PCM installation location
title_full_unstemmed ANN usefulness in building enhanced with PCM: Efficacy of PCM installation location
title_sort ANN usefulness in building enhanced with PCM: Efficacy of PCM installation location
publishDate 2022
container_title Journal of Building Engineering
container_volume 57
container_issue
doi_str_mv 10.1016/j.jobe.2022.104914
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134707692&doi=10.1016%2fj.jobe.2022.104914&partnerID=40&md5=061733843392389b0dda5ad01ba8b8f7
description The building walls are composed of several layers to protect the building from temperature fluctuations of the external environment. Increasing the number of layers in a building certainly strengthens the wall's ability to reduce heat transfer. But on the other hand, it increases the thickness of the walls and thus reduces the useful floor area of the building, which has no economic reason. In this study, the effect of reinforcing building walls against heat transfer using PCM was discussed. At constant PCM thickness, the installation location of this material varies from the outermost to the innermost. At the best location, the installation of PCM inside the wall/roof resulted in energy savings by 14.9 [Formula presented] 19.6 [Formula presented] and taking into account their area, for the whole building, energy-saving was 15.9 [Formula presented]. The effectiveness of ANN on PCM applications was amazing. Annual energy consumption for the building is 64.236 [Formula presented]. The neural network, by establishing a connection between the data predicts the annual energy consumption of 64.209 [Formula presented]. Therefore, the error was less than 0.04%. The monthly analysis also shows that for monthly EC, the error was less than 1.5%. © 2022 Elsevier Ltd
publisher Elsevier Ltd
issn 23527102
language English
format Article
accesstype
record_format scopus
collection Scopus
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