Entropy-Based Anomaly Detection in HouseholdElectricity Consumption Articles uri icon

publication date

  • March 2022

start page

  • 1837

end page

  • 1858


  • 5


  • 15

International Standard Serial Number (ISSN)

  • 1996-1073


  • 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.


  • Telecommunications


  • anomaly detection; behavior pattern; entropy; household electricity consumption; load; forecasting