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dc.contributor.authorBottolfsen, Hanne Liland
dc.contributor.authorAndersen, Kamilla Heimar
dc.contributor.authorClauß, John
dc.contributor.authorSartori, Igor
dc.date.accessioned2020-10-16T07:38:24Z
dc.date.available2020-10-16T07:38:24Z
dc.date.created2020-10-15T17:03:29Z
dc.date.issued2020
dc.identifier.isbn978-82-536-1679-7
dc.identifier.issn2387-4295
dc.identifier.urihttps://hdl.handle.net/11250/2683201
dc.description.abstractGrey-box models combine a relatively simple physical description of the building with a data-driven inference of key parameters and are often used for this purpose. A challenge with grey-box models is that the model identification process requires 'rich' datasets, meaning datasets containing enough statistical variability on both heating demand and indoor temperatures. Such datasets are scarcely available, usually only from dedicated experiments in living labs or similar research facilities. This study aims to present a series of datasets that can be used for the identification of grey-box models of apartment blocks. Special test periods are simulated in IDA ICE during which representative archetypes of apartment blocks in Norway are excited with trains of heating events, Pseudo-Random Binary Sequence (PRBS), aiming at exploring a wide and rapidly changing set of indoor temperatures within and outside the thermal comfort zone.nb_NO
dc.language.isoengnb_NO
dc.publisherSINTEF Academic Pressnb_NO
dc.relation.ispartofInternational Conference Organised by IBPSA-Nordic, 13th–14th October 2020, OsloMet. BuildSIM-Nordic 2020. Selected papers
dc.relation.ispartofseriesSINTEF Proceedings;5
dc.rightsCC-BY-NC-ND*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleDatasets for grey-box model identification from representative archetypes of apartment blocks in Norwaynb_NO
dc.typeChapternb_NO
dc.typePeer reviewednb_NO
dc.typeConference objectnb_NO
dc.description.versionpublishedVersionen_US
dc.rights.holder© The 2020 authors. Published by SINTEF Academic Pressnb_NO
dc.subject.nsiVDP::Teknologi: 500nb_NO
dc.source.pagenumber301-307nb_NO
dc.identifier.cristin1839953
dc.relation.projectNorges forskningsråd: 257660en_US
cristin.ispublishedtrue
cristin.fulltextoriginal


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