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Profiling of household residents’ electricity consumption behavior using clustering analysis
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0003-3128-191x
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0001-9947-1088
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
2019 (English)In: Lect. Notes Comput. Sci., Springer Verlag , 2019, p. 779-786Conference paper, Published paper (Refereed)
Abstract [en]

In this study we apply clustering techniques for analyzing and understanding households’ electricity consumption data. The knowledge extracted by this analysis is used to create a model of normal electricity consumption behavior for each particular household. Initially, the household’s electricity consumption data are partitioned into a number of clusters with similar daily electricity consumption profiles. The centroids of the generated clusters can be considered as representative signatures of a household’s electricity consumption behavior. The proposed approach is evaluated by conducting a number of experiments on electricity consumption data of ten selected households. The obtained results show that the proposed approach is suitable for data organizing and understanding, and can be applied for modeling electricity consumption behavior on a household level. © Springer Nature Switzerland AG 2019.

Place, publisher, year, edition, pages
Springer Verlag , 2019. p. 779-786
Series
Lecture Notes in Computer Science ; 11540
Keywords [en]
Ambient Assisted Living, Non-intrusive remote monitoring, Assisted living, Clustering analysis, Clustering techniques, Electricity-consumption, Household level, Number of clusters, Remote monitoring, Electric power utilization
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-18593DOI: 10.1007/978-3-030-22750-0_78Scopus ID: 2-s2.0-85068459816ISBN: 9783030227494 (print)OAI: oai:DiVA.org:bth-18593DiVA, id: diva2:1349333
Conference
International Conference on Computational Science, ICCS, Faro, Algarve, 12 June 2019 through 14 June 2019
Available from: 2019-09-09 Created: 2019-09-09 Last updated: 2019-10-17Bibliographically approved
In thesis
1. Data-Driven Techniques for Modeling and Analysis of User Behavior
Open this publication in new window or tab >>Data-Driven Techniques for Modeling and Analysis of User Behavior
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Our society is becoming more digitalized for each day. Now, we are able to gather data from individual users with higher resolution than ever. With the increased amount of data on an individual user level, we can analyze their behavior. This is of interest in many different domains, for example service providers wanting to improve their service for their customers. If they know how their service is used, they have more insight in how they can improve. But, it also imposes additional difficulties. When we reach the individual user, the irregularities in the regular behavior makes it harder to model the normal behavior.

In this thesis, we explore data-driven techniques to model and analyze user behaviors. We aim to evaluate existing as well as develop novel technologies to identify approaches that are suitable for use on an individual user level. We use both supervised and unsupervised learning methods to model the user behavior and evaluate the approaches on real world electricity consumption data.

Firstly, we analyze household electricity consumption data and investigate the use of regression to model the household's behavior. We identify consumption trends, how data granularity affects modeling, and we show that regression is a viable approach to model user behavior. Secondly, we use clustering analysis to profile individual households in terms of their electricity consumption. We compare two dissimilarity measures, how they affect the clustering analysis, and we investigate how the produced clustering solutions differ. Thirdly, we propose a sequential clustering algorithm to model evolving user behavior. We evaluate the proposed algorithm on electricity consumption data and show how the produced model can be used to identify and trace changes in the user's behavior. The algorithm is robust to evolving behaviors and handles both dynamic and incremental aspects of streaming data.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2019
Series
Blekinge Institute of Technology Licentiate Dissertation Series, ISSN 1650-2140 ; 15
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-18667 (URN)978-91-7295-391-8 (ISBN)
Supervisors
Available from: 2019-11-15 Created: 2019-09-18 Last updated: 2019-11-21Bibliographically approved

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Nordahl, ChristianBoeva, VeselkaGrahn, HåkanNetz Persson, Marie

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