Publication: Entropy-Based Anomaly Detection in Household Electricity Consumption
Loading...
Identifiers
Publication date
2022-03-02
Defense date
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI AG
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.
Description
Keywords
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