Determine the heat demand of existing buildings with machine learning

dc.contributor.authorHofmann, Joachim Werner
dc.contributor.authorAmoser, Christian
dc.contributor.authorGeissler, Achim
dc.contributor.authorHall, Monika
dc.date.accessioned2023-12-07T13:47:30Z
dc.date.available2023-12-07T13:47:30Z
dc.date.issued2023-12-01
dc.description.abstractThe renovation rate of existing buildings plays a major role in the Swiss Energy Strategy 2050+. To increase this rate, there must be a simple and cost-effective method to determine the heat demand of existing buildings. In this paper, the generation of such a method, based on the Swiss cantonal building energy certificate (GEAK) database with the help of machine learning (ML), is studied. The aim of the project was to develop a ML model which allows the heat demand of existing buildings to be determined quickly with a minimal set of parameters. The comparison of the GEAK building envelope class for single family houses calculated with the new ML model and the original GEAK classes shows that approximately 62 % have the same class, 32 % differ by one class and 6 % by two classes. The ML model is a good starting point for further refinements and developments.
dc.description.urihttps://iopscience.iop.org/issue/1742-6596/2600/3
dc.identifier.doi10.1088/1742-6596/2600/3/032013
dc.identifier.issn1742-6588
dc.identifier.issn1742-6596
dc.identifier.urihttps://irf.fhnw.ch/handle/11654/38816
dc.identifier.urihttps://doi.org/10.26041/fhnw-5920
dc.issue032013
dc.language.isoen
dc.publisherIOP Publishing
dc.relation.ispartofJournal of Physics: Conference Series
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectMachine learning
dc.subjectDeep neural network
dc.subjectHead demand
dc.subject.ddc624 - Ingenieurbau und Umwelttechnik
dc.titleDetermine the heat demand of existing buildings with machine learning
dc.type01A - Beitrag in wissenschaftlicher Zeitschrift
dc.volume2600
dspace.entity.typePublication
fhnw.InventedHereYes
fhnw.LegalEntity.authorInstitut Nachhaltigkeit und Energie am Bau, Hochschule für Architektur, Bau und Geomatik FHNW
fhnw.ReviewTypeAnonymous ex ante peer review of a complete publication
fhnw.affiliation.hochschuleHochschule für Architektur, Bau und Geomatikde_CH
fhnw.affiliation.institutInstitut Nachhaltigkeit und Energie am Baude_CH
fhnw.openAccessCategoryGreen
fhnw.pagination1-7
fhnw.publicationStatePublished
fhnw.specialIssueCISBAT 2023
relation.isAuthorOfPublicationba0d0430-a777-415e-b67c-0f090e0d55ba
relation.isAuthorOfPublicationdff0779c-e35e-4e1d-97ed-b41a2b6ae1ac
relation.isAuthorOfPublication29755986-0864-4ca5-92db-f08f187d444b
relation.isAuthorOfPublication.latestForDiscoveryba0d0430-a777-415e-b67c-0f090e0d55ba
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