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• W4214937762 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." @default.
• W4214937762 created "2022-03-05" @default.
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• W4214937762 date "2022-03-02" @default.
• W4214937762 modified "2023-09-26" @default.
• W4214937762 title "Entropy-Based Anomaly Detection in Household Electricity Consumption" @default.
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• W4214937762 doi "https://doi.org/10.3390/en15051837" @default.
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