Unsupervised Detection of Unusual Behaviors from Smart Home Energy Data

  • Welma PereiraEmail author
  • Alois Ferscha
  • Klemens Weigl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9693)


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.


Novelty detection Stream clustering Behavioral change Smart home energy saving 



The project PowerIT acknowledges the financial support of FFG FIT-IT under grant number 830.605.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute for Pervasive ComputingLinzAustria

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