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2016 | OriginalPaper | Buchkapitel

Unsupervised Detection of Unusual Behaviors from Smart Home Energy Data

verfasst von : Welma Pereira, Alois Ferscha, Klemens Weigl

Erschienen in: Artificial Intelligence and Soft Computing

Verlag: Springer International Publishing

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Abstract

In this paper the potentials of identifying unusual user behaviors and changes of behavior from smart home energy meters are investigated. We compare the performance of the classical change detection Page-Hinkley test (PHT) with a new application of a self-adaptive stream clustering algorithm to detect novelties related to the time of use of appliances at home. With the use of annotated data, the true positive rate of the clustering-based method outperformed the PHT by at least 20 %. Moreover the method was able to identify behavior changes related to time shifts and replacement of appliances. The motivation for this study is based on the need for identifying and guiding behavior changes that can reduce energy consumption, and use this knowledge in the development of systems that can raise just-in-time warnings to save energy (e.g. avoid stand-by modes), and guide sustainable behavior changes.

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Metadaten
Titel
Unsupervised Detection of Unusual Behaviors from Smart Home Energy Data
verfasst von
Welma Pereira
Alois Ferscha
Klemens Weigl
Copyright-Jahr
2016
Buch
Artificial Intelligence and Soft Computing

Print ISBN: 978-3-319-39383-4

Electronic ISBN: 978-3-319-39384-1

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-39384-1_46