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Trend analysis to automatically identify heat program changes
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
NODA Intelligent Systems AB, SWE.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
Show others and affiliations
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. Vol. 116, p. 407-415
Series
Energy Procedia, ISSN 1876-6102 ; 116
Keywords [en]
District heating, Trend analysis, Change detection, Smart automated system
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:bth-12894DOI: 10.1016/j.egypro.2017.05.088ISI: 000406743000039OAI: oai:DiVA.org:bth-12894DiVA, id: diva2:974391
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
In thesis
1. Data Modeling for Outlier Detection
Open this publication in new window or tab >>Data Modeling for Outlier Detection
2018 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis explores the data modeling for outlier detection techniques in three different application domains: maritime surveillance, district heating, and online media and sequence datasets. The proposed models are evaluated and validated under different experimental scenarios, taking into account specific characteristics and setups of the different domains.

Outlier detection has been 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 modeling the normal behavior in order to identify abnormalities. The choice of model is important, i.e., an incorrect choice of data model can lead to poor results. This requires a good understanding and interpretation of the data, the constraints, and the requirements of the problem domain. Outlier detection is largely an unsupervised problem due to unavailability of labeled data and the fact that labeled data is expensive.

We have studied and applied a combination of both machine learning and data mining techniques to build data-driven and domain-oriented outlier detection models. We have shown the importance of data preprocessing as well as feature selection in building suitable methods for data modeling. We have taken advantage of both supervised and unsupervised techniques to create hybrid methods. For example, we have proposed a rule-based outlier detection system based on open data for the maritime surveillance domain. Furthermore, we have combined cluster analysis and regression to identify manual changes in the heating systems at the building level. Sequential pattern mining for identifying contextual and collective outliers in online media data have also been exploited. In addition, we have proposed a minimum spanning tree clustering technique for detection of groups of outliers in online media and sequence data. The proposed models have been 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 behaviors. We have also investigated the reproducibility of the proposed models in similar application domains.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2018
Series
Blekinge Institute of Technology Licentiate Dissertation Series, ISSN 1650-2140 ; 4
Keywords
data modeling, cluster analysis, stream data, outlier detection
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-16580 (URN)978-91-7295-358-1 (ISBN)
Presentation
2018-11-09, Blekinge Tekniska Högskola, Karlskrona, 10:00 (English)
Opponent
Supervisors
Projects
Scalable resource-efficient systems for big data analytics
Funder
Knowledge Foundation, 20140032
Available from: 2018-10-25 Created: 2018-10-12 Last updated: 2018-12-04Bibliographically approved

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Publisher's full texthttp://www.sciencedirect.com/science/article/pii/S1876610217322956

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Abghari, ShahroozGarcía Martín, EvaLavesson, NiklasGrahn, Håkan

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