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Higher order mining for monitoring district heating substations
Department of Computer Science, Blekinge Institute of Technology, Sweden.
Department of Computer Science, Blekinge Institute of Technology, Sweden.
Noda Intelligent Systems Ab, Sweden.
Noda Intelligent Systems Ab, Sweden.
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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 (IEEE), 2019, p. 382-391, article id 8964173Conference 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. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019. p. 382-391, article id 8964173
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 and Information Sciences
Identifiers
URN: urn:nbn:se:hj:diva-47935DOI: 10.1109/DSAA.2019.00053ISI: 000540890900038Scopus ID: 2-s2.0-85079289447ISBN: 9781728144931 (print)OAI: oai:DiVA.org:hj-47935DiVA, id: diva2:1412076
Conference
6th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019, Washington, United States, 5 - 8 October, 2019
Funder
Knowledge Foundation, 20140032
Note

Funding details: Stiftelsen för Kunskaps- och Kompetensutveckling, KK, 201400

This work is part of the research project “Scalable resource-efficient systems for big data analytics“ funded by the Knowledge Foundation (grant: 20140032) in Sweden.

Available from: 2020-03-05 Created: 2020-03-05 Last updated: 2021-03-15Bibliographically approved

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Lavesson, Niklas

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