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Entropy-Based Anomaly Detection in Household Electricity Consumption

Author

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  • Marta Moure-Garrido

    (Department of Telematic Engineering, University Carlos III of Madrid, Avda. Universidad 30, E-28911 Leganés, Spain)

  • Celeste Campo

    (Department of Telematic Engineering, University Carlos III of Madrid, Avda. Universidad 30, E-28911 Leganés, Spain)

  • Carlos Garcia-Rubio

    (Department of Telematic Engineering, University Carlos III of Madrid, Avda. Universidad 30, E-28911 Leganés, Spain)

Abstract

Energy efficiency is one of the most important current challenges, and its impact at a global level is considerable. To solve current challenges, it is critical that consumers are able to control their energy consumption. In this paper, we propose using a time series of window-based entropy to detect anomalies in the electricity consumption of a household when the pattern of consumption behavior exhibits a change. We compare the accuracy of this approach with two machine learning approaches, random forest and neural networks, and with a statistical approach, the ARIMA model. We study whether these approaches detect the same anomalous periods. These different techniques have been evaluated using a real dataset obtained from different households with different consumption profiles from the Madrid Region. The entropy-based algorithm detects more days classified as anomalous according to context information compared to the other algorithms. This approach has the advantages that it does not require a training period and that it adapts dynamically to changes, except in vacation periods when consumption drops drastically and requires some time for adapting to the new situation.

Suggested Citation

  • Marta Moure-Garrido & Celeste Campo & Carlos Garcia-Rubio, 2022. "Entropy-Based Anomaly Detection in Household Electricity Consumption," Energies, MDPI, vol. 15(5), pages 1-21, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:5:p:1837-:d:762362
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    References listed on IDEAS

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