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dc.contributor.authorKudela, Liborcs
dc.contributor.authorChýlek, Radomírcs
dc.contributor.authorPospíšil, Jiřícs
dc.date.accessioned2021-01-12T15:54:59Z
dc.date.available2021-01-12T15:54:59Z
dc.date.issued2020-12-02cs
dc.identifier.citationENERGIES. 2020, vol. 13, issue 23, p. 1-12.en
dc.identifier.issn1996-1073cs
dc.identifier.other166351cs
dc.identifier.urihttp://hdl.handle.net/11012/195829
dc.description.abstractModern control strategies for district-level heating and cooling supply systems pose a difficult challenge. In order to integrate a wide range of hot and cold sources, these new systems will rely heavily on accumulation and much lower operating temperatures. This means that predictive models advising the control strategy must take into account long-lasting thermal effects but must not be computationally too expensive, because the control would not be possible in practice. This paper presents a simple but powerful systematic approach to reducing the complexity of individual components of such models. It makes it possible to combine human engineering intuition with machine learning and arrive at comprehensive and accurate models. As an example, a simple steady-state heat loss of buried pipes is extended with dynamics observed in a much more complex model. The results show that the process converges quickly toward reasonable solutions. The new auto-generated model performs 5 x 10(4) times faster than its complex equivalent while preserving essentially the same accuracy. This approach has great potential to enhance the development of fast predictive models not just for district heating. Only open-source software was used, while OpenModelica, Python, and FEniCS were predominantly used.en
dc.formattextcs
dc.format.extent1-12cs
dc.format.mimetypeapplication/pdfcs
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofENERGIEScs
dc.relation.urihttps://www.mdpi.com/1996-1073/13/23/6381cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectdistrict heatingen
dc.subjectmachine learningen
dc.subjectoptimizationen
dc.subjectmodellingen
dc.subjectdynamicsen
dc.subjectpipesen
dc.subjectsmart systemsen
dc.titleEfficient Integration of Machine Learning into District Heating Predictive Modelsen
thesis.grantorVysoké učení technické v Brně. Fakulta strojního inženýrství. EÚ-odbor energetického inženýrstvícs
sync.item.dbidVAV-166351en
sync.item.dbtypeVAVen
sync.item.insts2021.01.22 16:56:11en
sync.item.modts2021.01.22 16:15:53en
dc.coverage.issue23cs
dc.coverage.volume13cs
dc.identifier.doi10.3390/en13236381cs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1996-1073/cs
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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Creative Commons Attribution 4.0 International
Except where otherwise noted, this item's license is described as Creative Commons Attribution 4.0 International