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Trend analysis to automatically identify heat program changes
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
NODA Intelligent Systems AB, SWE.
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
Vise andre og tillknytning
2017 (engelsk)Inngår i: Energy Procedia, Elsevier, 2017, Vol. 116, s. 407-415Konferansepaper, Publicerat paper (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.

sted, utgiver, år, opplag, sider
Elsevier, 2017. Vol. 116, s. 407-415
Serie
Energy Procedia, ISSN 1876-6102 ; 116
Emneord [en]
District heating, Trend analysis, Change detection, Smart automated system
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-12894DOI: 10.1016/j.egypro.2017.05.088ISI: 000406743000039OAI: oai:DiVA.org:bth-12894DiVA, id: diva2:974391
Konferanse
15th International Symposium on District Heating and Cooling (DHC2016), Seoul
Prosjekter
BigData@BTH
Forskningsfinansiär
Knowledge Foundation, 20140032
Merknad

Open access

Tilgjengelig fra: 2016-09-26 Laget: 2016-07-13 Sist oppdatert: 2018-10-12bibliografisk kontrollert
Inngår i avhandling
1. Data Modeling for Outlier Detection
Åpne denne publikasjonen i ny fane eller vindu >>Data Modeling for Outlier Detection
2018 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Karlskrona: Blekinge Tekniska Högskola, 2018
Serie
Blekinge Institute of Technology Licentiate Dissertation Series, ISSN 1650-2140 ; 4
Emneord
data modeling, cluster analysis, stream data, outlier detection
HSV kategori
Identifikatorer
urn:nbn:se:bth-16580 (URN)978-91-7295-358-1 (ISBN)
Presentation
2018-11-09, Blekinge Tekniska Högskola, Karlskrona, 10:00 (engelsk)
Opponent
Veileder
Prosjekter
Scalable resource-efficient systems for big data analytics
Forskningsfinansiär
Knowledge Foundation, 20140032
Tilgjengelig fra: 2018-10-25 Laget: 2018-10-12 Sist oppdatert: 2018-12-04bibliografisk kontrollert

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