hh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Large-scale monitoring of operationally diverse district heating substations: A reference-group based approach
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-2859-6155
Department of Energy and Process Engineering, Norwegian University of Science and Technology, Trondheim, Norway.ORCID iD: 0000-0003-1183-3561
Show others and affiliations
2020 (English)In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 90, article id 103492Article in journal (Refereed) Published
Abstract [en]

A typical district heating (DH) network consists of hundreds, sometimes thousands, of substations. In the absence of a well-understood prior model or data labels about each substation, the overall monitoring of such large number of substations can be challenging. To overcome the challenge, an approach based on the collective operational monitoring of each substation by a local group (i.e., the reference-group) of other similar substations in the network was formulated. Herein, if a substation of interest (i.e., the target) starts to behave differently in comparison to those in its reference-group, then it was designated as an outlier. The approach was demonstrated on the monitoring of the return temperature variable for atypical and faulty operational behavior in 778 substations associated with multi-dwelling buildings. The choice of an appropriate similarity measure along with its size k were the two important factors that enables a reference-group to effectively detect an outlier target. Thus, different similarity measures and size k for the construction of the reference-groups were investigated, which led to the selection of the Euclidean distance with = 80. This setup resulted in the detection of 77 target substations that were outliers, i.e., the behavior of their return temperature changed in comparison to the majority of those in their respective reference-groups. Of these, 44 were detected due to the local construction of the reference-groups. In addition, six frequent patterns of deviating behavior in the return temperature of the substations were identified using the reference-group based approach, which were then further corroborated by the feedback from a DH domain expert. © 2020 Elsevier Ltd

Place, publisher, year, edition, pages
Oxford: Elsevier, 2020. Vol. 90, article id 103492
Keywords [en]
District heating substations, Return temperature, Reference-group based operational monitoring, Fault detection, Outlier detection
National Category
Other Engineering and Technologies not elsewhere specified
Identifiers
URN: urn:nbn:se:hh:diva-40962DOI: 10.1016/j.engappai.2020.103492Scopus ID: 2-s2.0-85078822459OAI: oai:DiVA.org:hh-40962DiVA, id: diva2:1370681
Funder
Knowledge Foundation, 20160103Available from: 2019-11-16 Created: 2019-11-16 Last updated: 2020-03-24Bibliographically approved
In thesis
1. Towards large-scale monitoring of operationally diverse thermal energy systems with data-driven techniques
Open this publication in new window or tab >>Towards large-scale monitoring of operationally diverse thermal energy systems with data-driven techniques
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The core of many typical large-scale industrial infrastructure consists of hundreds or thousands of systems that are similar in their basic design and purpose. For instance, District Heating (DH) utilities rely on a large network of substations to deliver heat to their customers. Similarly, a factory may require a large fleet of specialized robots for manufacturing a certain product. Monitoring these systems is important for maintaining the overall efficiency of industrial operations by detecting various problems due to faults and misconfiguration. However, this can be challenging since a well-understood prior model for each system is rarely available. In most cases, each system in a fleet or network is fitted with a set of sensors to measure its state at different time intervals. Typically, a data-driven model for each system can be used for their monitoring. However, not all factors that can possibly influence the operations of each system in a fleet or network has an associated sensor. Moreover, sufficient instances of normal, atypical and faulty behavior are rarely available to train such a model. These issues can impede the effectiveness of a system level data-driven model. Alternatively, it can be assumed that since all the systems in a fleet or network are working on a similar task, they should all behave in a homogeneous manner. Any system that behaves differently from the majority is then considered as an outlier. This is referred to as the global model at the fleet or network level. While the approach is simple, it is less effective in the presence of non-stationary working conditions. Hence, both system level and global modeling approaches have their limitations. 

This thesis investigates system level and fleet or network level (global) models for large-scale monitoring, and proposes an alternative way which is referred to as a reference-group based approach. Herein, the operational monitoring of each system, referred to as a target, is delegated to a reference-group, which consists of systems experiencing a comparable operating regime along with the target. Thus, the definition of a normal, atypical or faulty operational behavior in a target system is described relative to its reference-group. In this sense, if the target system is not behaving operationally in consort with the systems in its reference-group, then it can be inferred that this is either due to a fault or because of some atypical operation arising at the target system due to its local peculiarities. The application area for these investigations is the large-scale operational monitoring of thermal energy systems: networks of district heating (DH) substations and fleets of heatpumps. The current findings indicate three advantages of a reference-group based approach. The first is that the reference operational behavior of any system in the fleet or network does not need to be predefined. The second is that it provides a basis for what a system’s operational behavior should have been and what it is. In this respect, each system in the reference-group provides an evidence about a particular behavior during a particular time period. This can be very useful when the description of a normal, atypical and faulty operational behavior is not available. The third is that it can detect potential atypical and faulty operational behavior quicker compared to global models of outlier detection at the fleet or network level.

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2019. p. 65
Series
Halmstad University Dissertations ; 65
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-40964 (URN)978-91-88749-38-3 (ISBN)978-91-88749-39-0 (ISBN)
Presentation
2019-11-26, O125, O building, Linjegatan 12, Halmstad, 13:00 (English)
Opponent
Supervisors
Funder
Knowledge Foundation
Available from: 2019-11-18 Created: 2019-11-16 Last updated: 2019-11-18Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Farouq, ShirazByttner, StefanBouguelia, Mohamed-Rafik

Search in DiVA

By author/editor
Farouq, ShirazByttner, StefanBouguelia, Mohamed-RafikNord, Natasa
By organisation
CAISR - Center for Applied Intelligent Systems Research
In the same journal
Engineering applications of artificial intelligence
Other Engineering and Technologies not elsewhere specified

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 119 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf