On the Non-Intrusive Extraction of Residents’ Privacy and Security Sensitive Information from Energy Smart Meters

Pascal Schirmer, Iosif Mporas

Research output: Contribution to journalArticlepeer-review

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Abstract

Energy smart meters have become very popular in monitoring and smart energy management applications. However, the acquired measurements except the energy consumption information may also carry information about the residents’ daily routine, preferences and profile. In this article, we investigate the potential of extracting information from smart meters related to residents’ security- and privacy-sensitive information. Specifically, using methodologies for load demand prediction, non-intrusive load monitoring and elastic matching, evaluation of extraction of information related to house occupancy, multimedia watching detection, socioeconomic and health profiling of residents was performed. The evaluation results showed that the aggregated energy consumption signals contain information related to residents’ privacy and security, which can be extracted from the smart meter measurements.
Original languageEnglish
JournalNeural Computing and Applications
Early online date4 Jan 2021
DOIs
Publication statusE-pub ahead of print - 4 Jan 2021

Keywords

  • Consumer privacy
  • Home security
  • Non-intrusive load monitoring
  • Smart meters

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