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dc.date.accessioned2021-06-30T08:44:42Z
dc.date.available2021-06-30T08:44:42Z
dc.date.issued2021-05-20
dc.identifierdoi:10.17170/kobra-202106304177
dc.identifier.urihttp://hdl.handle.net/123456789/12949
dc.description.sponsorshipGefördert durch den Publikationsfonds der Universität Kassel
dc.language.isoeng
dc.rightsNamensnennung 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectAnnual heat load profileseng
dc.subjectCorrelationseng
dc.subjectIndustryeng
dc.subjectCommerceeng
dc.subjectk-means clusteringeng
dc.subjectStandard loaf profileseng
dc.subject.ddc330
dc.subject.ddc333
dc.titleAnnual industrial and commercial heat load profiles: modeling based on k-Means clustering and regression analysiseng
dc.typeAufsatz
dcterms.abstractAn accurate method to predict annual heat load profiles is fundamental to many studies, e.g., preliminary design or potential studies on renewable heating systems. This study presents a method to predict annual heat load profiles with a daily resolution for industry and commerce, based on an analysis of 797 natural gas load profiles (≥1.5 GWh/a). To derive heat load profiles, these natural gas load profiles are normalized and those with a potentially non-linear relationship between heat demand and natural gas consumption are excluded. The heat load profiles are clustered using the k-means algorithm according to their respective dependency on mean daily ambient temperature. The results reveal that the heat demand of most consumers is characterized by a clear dependency on mean daily ambient temperature, even in industry. The assignment of the load profiles to the clusters can be explained by the respective composition of each consumers’ heat sinks. In a regression analysis, individual regressions for each load profile are only slightly more accurate than the regressions for all load profiles assigned to one of the respective clusters. In terms of accuracy and user-friendliness, the developed cluster regression-based correlations for load profile prediction offer a significant improvement on previous methods.eng
dcterms.accessRightsopen access
dcterms.creatorJesper, Mateo
dcterms.creatorPag, Felix
dcterms.creatorVajen, Klaus
dcterms.creatorJordan, Ulrike
dc.relation.doidoi:10.1016/jecmx.2021.100085
dc.subject.swdWirtschaftger
dc.subject.swdIndustrieger
dc.subject.swdKorrelationger
dc.subject.swdWärmebedarfger
dc.subject.swdProfilger
dc.type.versionpublishedVersion
dcterms.source.identifiereissn:2590-1745
dcterms.source.journalEnergy conversion and management: Xeng
dcterms.source.volumeVolume 10
kup.iskupfalse
dcterms.source.articlenumber100085


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