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Abghari, Shahrooz
Publications (3 of 3) Show all publications
Abghari, S., Boeva, V., Brage, J., Johansson, C., Grahn, H. & Lavesson, N. (2019). Higher order mining for monitoring district heating substations. In: Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019: . Paper presented at 6th IEEE International Conference on Data Science and Advanced Analytics, DSAA, Washington DC, 5 October 2019 through 8 October 2019 (pp. 382-391). Institute of Electrical and Electronics Engineers Inc.
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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
Keywords
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:nbn:se:bth-19237 (URN)10.1109/DSAA.2019.00053 (DOI)2-s2.0-85079289447 (Scopus ID)9781728144931 (ISBN)
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
Abghari, S., García Martín, E., Johansson, C., Lavesson, N. & Grahn, H. (2017). Trend analysis to automatically identify heat program changes. In: Energy Procedia: . Paper presented at 15th International Symposium on District Heating and Cooling (DHC2016), Seoul (pp. 407-415). Elsevier, 116
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2017 (English)In: Energy Procedia, Elsevier, 2017, Vol. 116, p. 407-415Conference paper, Published paper (Refereed)
Abstract [en]

The aim of this study is to improve the monitoring and controlling of heating systems located at customer buildings through the use of a decision support system. To achieve this, the proposed system applies a two-step classifier to detect manual changes of the temperature of the heating system. We apply data from the Swedish company NODA, active in energy optimization and services for energy efficiency, to train and test the suggested system. The decision support system is evaluated through an experiment and the results are validated by experts at NODA. The results show that the decision support system can detect changes within three days after their occurrence and only by considering daily average measurements.

Place, publisher, year, edition, pages
Elsevier, 2017
Series
Energy Procedia, ISSN 1876-6102 ; 116
Keywords
District heating, Trend analysis, Change detection, Smart automated system
National Category
Computer Systems
Identifiers
urn:nbn:se:bth-12894 (URN)10.1016/j.egypro.2017.05.088 (DOI)000406743000039 ()
Conference
15th International Symposium on District Heating and Cooling (DHC2016), Seoul
Projects
BigData@BTH
Funder
Knowledge Foundation, 20140032
Note

Open access

Available from: 2016-09-26 Created: 2016-07-13 Last updated: 2018-10-12Bibliographically approved
Kazemi, S., Abghari, S., Lavesson, N., Johnson, H. & Ryman, P. (2013). Open Data for Anomaly Detection in Maritime Surveillance. Expert Systems with Applications, 40(14), 5719-5729
Open this publication in new window or tab >>Open Data for Anomaly Detection in Maritime Surveillance
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2013 (English)In: Expert Systems with Applications, ISSN 0957-4174, Vol. 40, no 14, p. 5719-5729Article in journal (Refereed) Published
Abstract [en]

Maritime Surveillance has received increased attention from a civilian perspective in recent years. Anomaly detection is one of many techniques available for improving the safety and security in this domain. Maritime authorities use confidential data sources for monitoring the maritime activities; however, a paradigm shift on the Internet has created new open sources of data. We investigate the potential of using open data as a complementary resource for anomaly detection in maritime surveillance. We present and evaluate a decision support system based on open data and expert rules for this purpose. We conduct a case study in which experts from the Swedish coastguard participate to conduct a real-world validation of the system. We conclude that the exploitation of open data as a complementary resource is feasible since our results indicate improvements in the efficiency and effectiveness of the existing surveillance systems by increasing the accuracy and covering unseen aspects of maritime activities.

Place, publisher, year, edition, pages
Elsevier, 2013
Keywords
Open data, Anomaly detection, Maritime security, Maritime domain awareness
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-6807 (URN)10.1016/j.eswa.2013.04.029 (DOI)000321089200029 ()oai:bth.se:forskinfoD455168E88392FDDC1257B6200290B99 (Local ID)oai:bth.se:forskinfoD455168E88392FDDC1257B6200290B99 (Archive number)oai:bth.se:forskinfoD455168E88392FDDC1257B6200290B99 (OAI)
Available from: 2013-12-17 Created: 2013-05-05 Last updated: 2018-10-12Bibliographically approved
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