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  • 1.
    Abghari, Shahrooz
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Boeva, Veselka
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Brage, Jens
    NODA Intelligent Systems AB, SWE.
    Johansson, Christian
    NODA Intelligent Systems AB, SWE.
    District Heating Substation Behaviour Modelling for Annotating the Performance2020Inngår i: Communications in Computer and Information Science, Springer , 2020, Vol. 1168 CCIS, s. 3-11Konferansepaper (Fagfellevurdert)
    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.

  • 2. Abghari, Shahrooz
    et al.
    Boeva, Veselka
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Brage, Jens
    NODA Intelligent Systems AB, SWE.
    Johansson, Christian
    NODA Intelligent Systems AB, SWE.
    Grahn, Håkan
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Lavesson, Niklas
    J önk öping University, SWE.
    Higher order mining for monitoring district heating substations2019Inngår i: Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019, Institute of Electrical and Electronics Engineers Inc. , 2019, s. 382-391Konferansepaper (Fagfellevurdert)
    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.

    Fulltekst (pdf)
    Higher Order Mining for Monitoring DistrictHeating Substations
  • 3.
    Abghari, Shahrooz
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    García Martín, Eva
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Johansson, Christian
    NODA Intelligent Systems AB, SWE.
    Lavesson, Niklas
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Grahn, Håkan
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Trend analysis to automatically identify heat program changes2017Inngår i: Energy Procedia, Elsevier, 2017, Vol. 116, s. 407-415Konferansepaper (Fagfellevurdert)
    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.

    Fulltekst (pdf)
    fulltext
  • 4.
    Kazemi, Samira
    et al.
    Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation.
    Abghari, Shahrooz
    Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation.
    Lavesson, Niklas
    Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation.
    Johnson, Henric
    Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation.
    Ryman, Peter
    Open Data for Anomaly Detection in Maritime Surveillance2013Inngår i: Expert Systems with Applications, ISSN 0957-4174, Vol. 40, nr 14, s. 5719-5729Artikkel i tidsskrift (Fagfellevurdert)
    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.

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