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Detection of Residents' Abnormal Behaviour by Analysing Energy Consumption of Individual Households
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0001-7199-8080
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.ORCID iD: 0000-0001-9947-1088
2017 (English)In: Proceedings of the 17th IEEE International Conference on Data Mining Workshops (ICDMW) / [ed] Gottumukkala, R; Ning, X; Dong, G; Raghavan, V; Aluru, S; Karypis, G; Miele, L; Wu, X, IEEE, 2017, p. 729-738Conference paper, Published paper (Refereed)
Abstract [en]

As average life expectancy continuously rises, assisting the elderly population with living independently is of great importance. Detecting abnormal behaviour of the elderly living at home is one way to assist the eldercare systems with the increase of the elderly population. In this study, we perform an initial investigation to identify abnormal behaviour of household residents using energy consumption data. We conduct an experiment in two parts, the first to identify a suitable prediction algorithm to model energy consumption behaviour, and the second to detect abnormal behaviour. This approach allows for an initial step for the elderly care that has a low cost, is easily deployable, and is non-intrusive.

Place, publisher, year, edition, pages
IEEE, 2017. p. 729-738
Series
International Conference on Data Mining Workshops, ISSN 2375-9232
Keywords [en]
Energy consumption, Predictive models, Smart meters, Correlation, Senior citizens
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-15565DOI: 10.1109/ICDMW.2017.101ISI: 000425845700096ISBN: 978-1-5386-3800-2 (print)OAI: oai:DiVA.org:bth-15565DiVA, id: diva2:1172980
Conference
IEEE International Conference on Data Mining series (ICDM), New Orleans
Projects
BigData@BTH
Funder
Knowledge Foundation, 20140032Available from: 2018-01-11 Created: 2018-01-11 Last updated: 2018-03-23Bibliographically approved

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Nordahl, ChristianPersson, MarieGrahn, Håkan
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