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Higher order mining for monitoring district heating substations
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.
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2019 (English)In: Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 382-391Conference paper, Published paper (Refereed)
Abstract [en]

We propose a higher order mining (HOM) approach for modelling, monitoring and analyzing district heating (DH) substations' operational behaviour and performance. HOM is concerned with mining over patterns rather than primary or raw data. The proposed approach uses a combination of different data analysis techniques such as sequential pattern mining, clustering analysis, consensus clustering and minimum spanning tree (MST). Initially, a substation's operational behaviour is modeled by extracting weekly patterns and performing clustering analysis. The substation's performance is monitored by assessing its modeled behaviour for every two consecutive weeks. In case some significant difference is observed, further analysis is performed by integrating the built models into a consensus clustering and applying an MST for identifying deviating behaviours. The results of the study show that our method is robust for detecting deviating and sub-optimal behaviours of DH substations. In addition, the proposed method can facilitate domain experts in the interpretation and understanding of the substations' behaviour and performance by providing different data analysis and visualization techniques. © 2019 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2019. p. 382-391
Keywords [en]
Clustering Analysis, Data Mining, District Heating Substations, Fault Detection, Higher Order Mining, Minimum Spanning Tree, Outlier Detection, Advanced Analytics, Anomaly detection, Clustering algorithms, Data visualization, District heating, Fault tree analysis, Fiber optics, Trees (mathematics), Consensus clustering, Data analysis techniques, Heating substations, Higher-order, Minimum spanning trees, Sequential-pattern mining, Visualization technique, Cluster analysis
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-19237DOI: 10.1109/DSAA.2019.00053Scopus ID: 2-s2.0-85079289447ISBN: 9781728144931 (print)OAI: oai:DiVA.org:bth-19237DiVA, id: diva2:1394955
Conference
6th IEEE International Conference on Data Science and Advanced Analytics, DSAA, Washington DC, 5 October 2019 through 8 October 2019
Available from: 2020-02-20 Created: 2020-02-20 Last updated: 2020-02-20Bibliographically approved

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Abghari, ShahroozBoeva, VeselkaGrahn, Håkan

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