Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.4316
DC FieldValueLanguage
dc.contributor.authorJohn, Daniel-
dc.contributor.authorKaltschmitt, Martin-
dc.date.accessioned2022-04-27T06:24:21Z-
dc.date.available2022-04-27T06:24:21Z-
dc.date.issued2022-04-02-
dc.identifierdoi: 10.3390/en15072607-
dc.identifier.citationEnergies 15 (7): 2607 (2022)de_DE
dc.identifier.issn1996-1073de_DE
dc.identifier.urihttp://hdl.handle.net/11420/12389-
dc.description.abstractThis study aims to develop a controller to operate an energy system-consisting of a photovoltaic thermal (PVT) system combined with a heat pump, using the reinforcement learning approach to minimize the operating costs of the system. For this, the flow rate of the cooling fluid pumped through the PVT system is controlled. This flow rate determines the temperature increase of the cooling fluid while reducing the temperature of the PVT system. The heated-up cooling fluid is used to improve the heat pump’s coefficient of performance (COP). For optimizing the operation costs of such a system, first an extensive simulation model has been developed. Based on this technical model, a controller has been developed using the reinforcement learning approach to allow for a cost-efficient control of the flow rate. The results show that a successfully trained control unit based on the reinforcement learning approach can reduce the operating costs with an independent validation dataset. For the case study presented here, based on the implemented methodological approach, including hyperparameter optimization, the operating costs of the investigated energy system can be reduced by more than 4% in the training dataset and by close to 3% in the validation dataset.-
dc.description.abstractThis study aims to develop a controller to operate an energy system-consisting of a photovoltaic thermal (PVT) system combined with a heat pump, using the reinforcement learning approach to minimize the operating costs of the system. For this, the flow rate of the cooling fluid pumped through the PVT system is controlled. This flow rate determines the temperature increase of the cooling fluid while reducing the temperature of the PVT system. The heated-up cooling fluid is used to improve the heat pump’s coefficient of performance (COP). For optimizing the operation costs of such a system, first an extensive simulation model has been developed. Based on this technical model, a controller has been developed using the reinforcement learning approach to allow for a cost-efficient control of the flow rate. The results show that a successfully trained control unit based on the reinforcement learning approach can reduce the operating costs with an independent validation dataset. For the case study presented here, based on the implemented methodological approach, including hyperparameter optimization, the operating costs of the investigated energy system can be reduced by more than 4% in the training dataset and by close to 3% in the validation dataset.en
dc.description.sponsorshipDeutsche Forschungsgemeinschaft (DFG)de_DE
dc.language.isoende_DE
dc.publisherMultidisciplinary Digital Publishing Institutede_DE
dc.relation.ispartofEnergiesde_DE
dc.rightsCC BY 4.0de_DE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/de_DE
dc.subjectPVTde_DE
dc.subjectreinforcement learningde_DE
dc.subjectsolar-assisted heat pumpde_DE
dc.subjectcontrol approachesde_DE
dc.subjectoperating cost analysisde_DE
dc.subject.ddc600: Technikde_DE
dc.subject.ddc620: Ingenieurwissenschaftende_DE
dc.titleControl of a PVT-heat-pump-system based on reinforcement learning : operating cost reduction through flow rate variationde_DE
dc.typeArticlede_DE
dc.date.updated2022-04-11T13:59:16Z-
dc.identifier.doi10.15480/882.4316-
dc.type.diniarticle-
dcterms.DCMITypeText-
tuhh.identifier.urnurn:nbn:de:gbv:830-882.0180932-
tuhh.oai.showtruede_DE
tuhh.abstract.englishThis study aims to develop a controller to operate an energy system-consisting of a photovoltaic thermal (PVT) system combined with a heat pump, using the reinforcement learning approach to minimize the operating costs of the system. For this, the flow rate of the cooling fluid pumped through the PVT system is controlled. This flow rate determines the temperature increase of the cooling fluid while reducing the temperature of the PVT system. The heated-up cooling fluid is used to improve the heat pump’s coefficient of performance (COP). For optimizing the operation costs of such a system, first an extensive simulation model has been developed. Based on this technical model, a controller has been developed using the reinforcement learning approach to allow for a cost-efficient control of the flow rate. The results show that a successfully trained control unit based on the reinforcement learning approach can reduce the operating costs with an independent validation dataset. For the case study presented here, based on the implemented methodological approach, including hyperparameter optimization, the operating costs of the investigated energy system can be reduced by more than 4% in the training dataset and by close to 3% in the validation dataset.de_DE
tuhh.publisher.doi10.3390/en15072607-
tuhh.publication.instituteUmwelttechnik und Energiewirtschaft V-9de_DE
tuhh.identifier.doi10.15480/882.4316-
tuhh.type.opus(wissenschaftlicher) Artikel-
dc.type.driverarticle-
dc.type.casraiJournal Article-
tuhh.container.issue7de_DE
tuhh.container.volume15de_DE
dc.relation.projectOpen-Access-Publikationskosten / 2022-2024 / Technische Universität Hamburg (TUHH)-
dc.rights.nationallicensefalsede_DE
dc.identifier.scopus2-s2.0-85128010290de_DE
tuhh.container.articlenumber2607de_DE
local.status.inpressfalsede_DE
local.type.versionpublishedVersionde_DE
local.funding.infoFunded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Projektnummer 491268466 and the Hamburg University of Technology (TUHH) in the funding programme Open Access Publishing.de_DE
item.fulltextWith Fulltext-
item.openairetypeArticle-
item.grantfulltextopen-
item.cerifentitytypePublications-
item.creatorGNDJohn, Daniel-
item.creatorGNDKaltschmitt, Martin-
item.creatorOrcidJohn, Daniel-
item.creatorOrcidKaltschmitt, Martin-
item.languageiso639-1en-
item.mappedtypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
crisitem.author.deptUmwelttechnik und Energiewirtschaft V-9-
crisitem.author.deptUmwelttechnik und Energiewirtschaft V-9-
crisitem.author.orcid0000-0002-9607-6573-
crisitem.author.orcid0000-0002-9106-6499-
crisitem.author.parentorgStudiendekanat Verfahrenstechnik-
crisitem.author.parentorgStudiendekanat Verfahrenstechnik-
crisitem.funder.funderid501100001659-
crisitem.funder.funderrorid018mejw64-
crisitem.project.funderDeutsche Forschungsgemeinschaft (DFG)-
crisitem.project.funderid501100001659-
crisitem.project.funderrorid018mejw64-
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