Dalarna University's logo and link to the university's website

du.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A Review on Data-driven Methods for Studying Hygrothermal Transfer in Building Exterior Walls
Dalarna University, School of Information and Engineering, Microdata Analysis.
Dalarna University, School of Information and Engineering, Informatics.ORCID iD: 0000-0003-3681-8173
Dalarna University, School of Information and Engineering, Informatics.ORCID iD: 0000-0003-4812-4988
Dalarna University, School of Information and Engineering, Microdata Analysis.ORCID iD: 0000-0002-3650-9162
Show others and affiliations
2023 (English)In: ICBDT '23: Proceedings of the 2023 6th International Conference on Big Data Technologies, ACM Press, 2023, p. 33-41Conference paper, Published paper (Refereed)
Abstract [en]

This review aims to comprehensively assess and synthesize the existing literature on the use of data-driven methods for studying hygrothermal transfer in building exterior walls. The review is conducted by an exhaustive search strategy to identify relevant articles from Web of Science and Scopus databases. There are 20 eligible studies included in this review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. The most used data-driven methods are traditional neural networks, such as Multi-Layer Perceptrons and 2D Convolutional Neural Networks. Results suggested that neural network models hold potential for accurately predicting hygrothermal attributes of building exteriors. However, a conspicuous gap in the literature is the absence of studies drawing direct comparisons between data-driven methodologies and conventional simulation techniques. © 2023 ACM.

Place, publisher, year, edition, pages
ACM Press, 2023. p. 33-41
Series
ACM International Conference Proceeding Series
Keywords [en]
Hygrothermal performance, Machine learning, Statistical learning, Systematic review
National Category
Energy Systems
Identifiers
URN: urn:nbn:se:du-47617DOI: 10.1145/3627377.3627409Scopus ID: 2-s2.0-85180131187OAI: oai:DiVA.org:du-47617DiVA, id: diva2:1823481
Conference
6th International Conference on Big Data Technologies, ICBDT 2023
Available from: 2024-01-02 Created: 2024-01-02 Last updated: 2024-01-02Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Zhu, YurongSong, William WeiNyberg, Roger G.Rybarczyk, Yves

Search in DiVA

By author/editor
Zhu, YurongSong, William WeiNyberg, Roger G.Rybarczyk, Yves
By organisation
Microdata AnalysisInformatics
Energy Systems

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 494 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf