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On the non-intrusive extraction of residents’ privacy- and security-sensitive information from energy smart meters

  • S.I. : Information, Intelligence, Systems and Applications
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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.

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Acknowledgements

This work was supported by the UA Doctoral Training Alliance (https://www.unialliance.ac.uk/) for Energy in the UK.

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  1. Communications and Intelligent Systems Group, School of Engineering and Computer Science, University of Hertfordshire, Hatfield, AL10 9AB, UK

    Pascal Alexander Schirmer & Iosif Mporas

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  1. Pascal Alexander Schirmer
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Correspondence to Pascal Alexander Schirmer.

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Schirmer, P.A., Mporas, I. On the non-intrusive extraction of residents’ privacy- and security-sensitive information from energy smart meters. Neural Comput & Applic 35, 119–132 (2023). https://doi.org/10.1007/s00521-020-05608-w

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  • DOI: https://doi.org/10.1007/s00521-020-05608-w

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