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Monitoring Household Electricity Consumption Behaviour for Mining Changes
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0001-7199-8080
2019 (English)In: 3rd International Workshop on Aging, Rehabilitation and Independent Assisted Living (ARIAL), International Joint Conferenec on Artificial Intelligence (IJCAI), August 10-16, 2019, Macao, China., 2019Conference paper, Oral presentation only (Refereed)
Abstract [en]

In this paper, we present an ongoing work on using a household electricity consumption behavior model for recognizing changes in sleep patterns. The work is inspired by recent studies in neuroscience revealing an association between dementia and sleep disorders and more particularly, supporting the hypothesis that insomnia may be a predictor for dementia in older adults. Our approach initially creates a clustering model of normal electricity consumption behavior of the household by using historical data. Then we build a new clustering model on a new set of electricity consumption data collected over a predefined time period and compare the existing model with the built new electricity consumption behavior model. If a discrepancy between the two clustering models is discovered a further analysis of the current electricity consumption behavior is conducted in order to investigate whether this discrepancy is associated with alterations in the resident’s sleep patterns. The approach is studied and initially evaluated on electricity consumption data collected from a single randomly selected anonymous household. The obtained results show that our approach is robust to mining changes in the resident daily routines by monitoring and analyzing their electricity consumption behavior model.

Place, publisher, year, edition, pages
2019.
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-18651OAI: oai:DiVA.org:bth-18651DiVA, id: diva2:1350711
Conference
International Joint Conferenec on Artificial Intelligence (IJCAI)
Projects
Scalable resource-efficient systems for big data analyticsAvailable from: 2019-09-12 Created: 2019-09-12 Last updated: 2019-09-20Bibliographically approved
In thesis
1.
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CiteExportLink to record
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