Statistical appliance inference in the smart grid by machine learning

Zeki Bilgin, Emrah Tomur, Mehmet Akif Ersoy, Elif Ustundag Soykan

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

4 Citations (Scopus)

Abstract

Smart Grid has been attracting more interest than ever thanks to emergence of enabling technologies such as 5G and IoT. Yet, there are some long-standing privacy concerns about revealing habits and lifestyles of people from fine-grained power consumption data collected through smart meters. In this context, the contribution of this work is twofold: First, we empirically demonstrate how appliance-level fine-grained power consumption data can reveal households' routines simply using probability density estimations derived from consumption data without requiring any complex analysis. Second, we point out that appliance types can be identified in a targeted house using circuit-level consumption data of other houses. We show how machine learning can be used maliciously to realize this threat in an automatic manner and achieve high success rate even with limited amount of training data on the public REDD dataset. In addition, we provide discussions on possible countermeasures against the threats examined in this study.

Original languageEnglish
Title of host publication2019 IEEE 30th International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC Workshops 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538693582
DOIs
Publication statusPublished - Sep 2019
Event30th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC Workshops 2019 - Istanbul, Turkey
Duration: 8 Sep 2019 → …

Publication series

Name2019 IEEE 30th International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC Workshops 2019

Conference

Conference30th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC Workshops 2019
Country/TerritoryTurkey
CityIstanbul
Period8/09/19 → …

Keywords

  • Appliance Identification and Inference
  • Machine Learning
  • Privacy
  • Smart Grid
  • Statistical Load Signature

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