University of Hertfordshire

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Original languageEnglish
JournalNeural Computing and Applications
Early online date4 Jan 2021
Publication statusE-pub ahead of print - 4 Jan 2021


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.


This is a post-peer-review, pre-copyedit version of an article published in Neural Computing and Applications. The final authenticated version is available online at: Funding Information: This work was supported by the UA Doctoral Training Alliance ( ) for Energy in the UK. Publisher Copyright: © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature

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