Publication:
Entropy-Based Anomaly Detection in Household Electricity Consumption

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2022-03-02
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MDPI AG
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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.
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Anomaly detection, Behavior pattern, Entropy, Household electricity consumption, Load, Forecasting
Bibliographic citation
Moure-Garrido, M., Campo, C., & Garcia-Rubio, C. (2022). Entropy-Based Anomaly Detection in Household Electricity Consumption. In Energies, 15(5), 1837-1858