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Monitoring Household Electricity Consumption Behaviour for Mining Changes
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.ORCID-id: 0000-0001-7199-8080
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.ORCID-id: 0000-0003-3128-191x
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.ORCID-id: 0000-0001-9947-1088
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
2019 (Engelska)Konferensbidrag, Enbart muntlig presentation (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
2019.
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:bth-18651OAI: oai:DiVA.org:bth-18651DiVA, id: diva2:1350711
Konferens
3rd International Workshop on Aging, Rehabilitation and Independent Assisted Living (ARIAL), International Joint Conferenec on Artificial Intelligence (IJCAI), August 10-16, 2019, Macao, China.
Projekt
Scalable resource-efficient systems for big data analyticsTillgänglig från: 2019-09-12 Skapad: 2019-09-12 Senast uppdaterad: 2019-10-17Bibliografiskt granskad
Ingår i avhandling
1. Data-Driven Techniques for Modeling and Analysis of User Behavior
Öppna denna publikation i ny flik eller fönster >>Data-Driven Techniques for Modeling and Analysis of User Behavior
2019 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Karlskrona: Blekinge Tekniska Högskola, 2019
Serie
Blekinge Institute of Technology Licentiate Dissertation Series, ISSN 1650-2140 ; 15
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:bth-18667 (URN)978-91-7295-391-8 (ISBN)
Handledare
Tillgänglig från: 2019-11-15 Skapad: 2019-09-18 Senast uppdaterad: 2019-12-18Bibliografiskt granskad

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Personposter BETA

Boeva, VeselkaGrahn, HåkanNetz Persson, Marie

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Av författaren/redaktören
Nordahl, ChristianBoeva, VeselkaGrahn, HåkanNetz Persson, Marie
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Datavetenskap (datalogi)

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