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2022
 Moure-Garrido, Marta;  Campo-Vázquez, Celeste;  García-Rubio, Carlos
Entropy-Based Anomaly Detection in HouseholdElectricity Consumption Journal Article 
In: Energies, vol. 15, 2022, ISSN: 1996-1073.
Abstract | Links | BibTeX | Tags: anomaly detection, behavior pattern, compromise, cynamon, entropy, household electricity consumption, load forecasting, magos
@article{campo003,

title = {Entropy-Based Anomaly Detection in HouseholdElectricity Consumption},

author = {Marta Moure-Garrido and Celeste Campo-Vázquez and Carlos García-Rubio},

doi = {https://doi.org/10.3390/en15051837},

issn = {1996-1073},

year  = {2022},

date = {2022-03-02},

urldate = {2022-03-02},

journal = {Energies},

volume = {15},

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.},

keywords = {anomaly detection, behavior pattern, compromise, cynamon, entropy, household electricity consumption, load forecasting, magos},

pubstate = {published},

tppubtype = {article}

}

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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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doi:https://doi.org/10.3390/en15051837
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