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District Heating Substation Behaviour Modelling for Annotating the Performance
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0002-3010-8798
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 / [ed] Cellier, P, Driessens, K, Springer , 2020, Vol. 1168, 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, p. 3-11
Series
Communications in Computer and Information Science, ISSN 1865-0929
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_1ISI: 000718590300001Scopus 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
Part of project
Bigdata@BTH- Scalable resource-efficient systems for big data analytics, Knowledge Foundation
Funder
Knowledge Foundation, 20140032Available from: 2020-05-03 Created: 2020-05-03 Last updated: 2021-12-03Bibliographically approved
In thesis
1. Data Mining Approaches for Outlier Detection Analysis
Open this publication in new window or tab >>Data Mining Approaches for Outlier Detection Analysis
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Outlier detection is studied and applied in many domains. Outliers arise due to different reasons such as fraudulent activities, structural defects, health problems, and mechanical issues. The detection of outliers is a challenging task that can reveal system faults, fraud, and save people's lives. Outlier detection techniques are often domain-specific. The main challenge in outlier detection relates to modelling the normal behaviour in order to identify abnormalities. The choice of model is important, i.e., an unsuitable data model can lead to poor results. This requires a good understanding and interpretation of the data, the constraints, and requirements of the domain problem. Outlier detection is largely an unsupervised problem due to unavailability of labeled data and the fact that labeled data is expensive. 

In this thesis, we study and apply a combination of both machine learning and data mining techniques to build data-driven and domain-oriented outlier detection models. We focus on three real-world application domains: maritime surveillance, district heating, and online media and sequence datasets. We show the importance of data preprocessing as well as feature selection in building suitable methods for data modelling. We take advantage of both supervised and unsupervised techniques to create hybrid methods. 

More specifically, we propose a rule-based anomaly detection system using open data for the maritime surveillance domain. We exploit sequential pattern mining for identifying contextual and collective outliers in online media data. We propose a minimum spanning tree clustering technique for detection of groups of outliers in online media and sequence data. We develop a few higher order mining approaches for identifying manual changes and deviating behaviours in the heating systems at the building level. The proposed approaches are shown to be capable of explaining the underlying properties of the detected outliers. This can facilitate domain experts in narrowing down the scope of analysis and understanding the reasons of such anomalous behaviours. We also investigate the reproducibility of the proposed models in similar application domains.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2020. p. 251
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 9
Keywords
outlier detection, data modelling, machine learning, clustering analysis, data stream mining
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:bth-20454 (URN)9789172954090 (ISBN)
Public defence
2020-12-01, J1630, Karlskrona, 13:00 (English)
Opponent
Supervisors
Funder
Knowledge Foundation, 20140032
Available from: 2020-10-16 Created: 2020-10-12 Last updated: 2020-12-14Bibliographically approved

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Abghari, ShahroozBoeva, Veselka

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