Entropy-Based Anomaly Detection in Household Electricity Consumption

Marta Moure-Garrido, Celeste Campo and Carlos Garcia-Rubio
Additional contact information
Marta Moure-Garrido: Department of Telematic Engineering, University Carlos III of Madrid, Avda. Universidad 30, E-28911 Leganés, Spain
Celeste Campo: Department of Telematic Engineering, University Carlos III of Madrid, Avda. Universidad 30, E-28911 Leganés, Spain
Carlos Garcia-Rubio: Department of Telematic Engineering, University Carlos III of Madrid, Avda. Universidad 30, E-28911 Leganés, Spain

Energies, 2022, vol. 15, issue 5, 1-21

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; entropy; household electricity consumption; load forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/5/1837/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/5/1837/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:5:p:1837-:d:762362

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().