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District Heating Substation Behaviour Modelling for Annotating the Performance
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0003-3128-191x
NODA Intelligent Systems AB, SWE.
NODA Intelligent Systems AB, SWE.
2020 (English)In: Communications in Computer and Information Science, Springer , 2020, Vol. 1168 CCIS, p. 3-11Conference paper, Published paper (Refereed)
Abstract [en]

In this ongoing study, we propose a higher order data mining approach for modelling district heating (DH) substations’ behaviour and linking operational behaviour representative profiles with different performance indicators. We initially create substation’s operational behaviour models by extracting weekly patterns and clustering them into groups of similar patterns. The built models are further analyzed and integrated into an overall substation model by applying consensus clustering. The different operational behaviour profiles represented by the exemplars of the consensus clustering model are then linked to performance indicators. The labelled behaviour profiles are deployed over the whole heating season to derive diverse insights about the substation’s performance. The results show that the proposed method can be used for modelling, analyzing and understanding the deviating and sub-optimal DH substation’s behaviours. © 2020, Springer Nature Switzerland AG.

Place, publisher, year, edition, pages
Springer , 2020. Vol. 1168 CCIS, p. 3-11
Series
Communications in Computer and Information Science, ISSN 18650929
Keywords [en]
Clustering analysis, District heating, Higher order mining, Outlier detection, Benchmarking, Cluster analysis, Machine learning, Behaviour modelling, Behaviour models, Consensus clustering, Heating season, Heating substations, Performance indicators, Similar pattern, Substation models, Data mining
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:bth-19425DOI: 10.1007/978-3-030-43887-6_1Scopus ID: 2-s2.0-85083637427ISBN: 9783030438869 (print)OAI: oai:DiVA.org:bth-19425DiVA, id: diva2:1427883
Conference
19th Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019; Wurzburg; Germany; 16 September 2019 through 20 September 2019
Available from: 2020-05-03 Created: 2020-05-03 Last updated: 2020-05-03Bibliographically approved

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Abghari, ShahroozBoeva, Veselka

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