Mining residential household information from low-resolution smart meter data

Francesco Fusco, Michael Wurst, Jiwon Yoon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

The implementation of electricity smart meters has raised a number of privacy concerns, related to all sorts of information about the nature of the residents that could be inferred from readings of the power consumption. In this paper we attempt to classify households according to different classes, ranging from the presence of kids and of specific appliances to the employment status and education level of the residents. We apply a wide range of features and classification methods and measure the achievable accuracy. It is shown that, at a time resolution of 30 minutes, only a few of the investigated problems give a satisfactorily accuracy, while most of them would require a higher sampling frequency that is not practical for smart meters.

Original languageEnglish
Title of host publicationICPR 2012 - 21st International Conference on Pattern Recognition
Pages3545-3548
Number of pages4
Publication statusPublished - 2012 Dec 1
Event21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, Japan
Duration: 2012 Nov 112012 Nov 15

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Other

Other21st International Conference on Pattern Recognition, ICPR 2012
CountryJapan
CityTsukuba
Period12/11/1112/11/15

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • Cite this

    Fusco, F., Wurst, M., & Yoon, J. (2012). Mining residential household information from low-resolution smart meter data. In ICPR 2012 - 21st International Conference on Pattern Recognition (pp. 3545-3548). [6460930] (Proceedings - International Conference on Pattern Recognition).