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Entropy-Based Anomaly Detection in Household Electricity Consumption

Author

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  • 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)

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.

Suggested Citation

  • Marta Moure-Garrido & Celeste Campo & Carlos Garcia-Rubio, 2022. "Entropy-Based Anomaly Detection in Household Electricity Consumption," Energies, MDPI, vol. 15(5), pages 1-21, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:5:p:1837-:d:762362
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    References listed on IDEAS

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    1. Feras Alasali & Khaled Nusair & Lina Alhmoud & Eyad Zarour, 2021. "Impact of the COVID-19 Pandemic on Electricity Demand and Load Forecasting," Sustainability, MDPI, vol. 13(3), pages 1-22, January.
    2. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    3. Aviad Navon & Ram Machlev & David Carmon & Abiodun Emmanuel Onile & Juri Belikov & Yoash Levron, 2021. "Effects of the COVID-19 Pandemic on Energy Systems and Electric Power Grids—A Review of the Challenges Ahead," Energies, MDPI, vol. 14(4), pages 1-14, February.
    4. Olamide Jogunola & Bamidele Adebisi & Khoa Van Hoang & Yakubu Tsado & Segun I. Popoola & Mohammad Hammoudeh & Raheel Nawaz, 2022. "CBLSTM-AE: A Hybrid Deep Learning Framework for Predicting Energy Consumption," Energies, MDPI, vol. 15(3), pages 1-16, January.
    5. Felipe Leite Coelho da Silva & Kleyton da Costa & Paulo Canas Rodrigues & Rodrigo Salas & Javier Linkolk López-Gonzales, 2022. "Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector," Energies, MDPI, vol. 15(2), pages 1-12, January.
    6. Yuwen Chen & José-Fernán Martínez & Pedro Castillejo & Lourdes López, 2018. "A Privacy-Preserving Noise Addition Data Aggregation Scheme for Smart Grid," Energies, MDPI, vol. 11(11), pages 1-17, November.
    7. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    8. Andrei M. Tudose & Irina I. Picioroaga & Dorian O. Sidea & Constantin Bulac & Valentin A. Boicea, 2021. "Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study," Energies, MDPI, vol. 14(13), pages 1-19, July.
    9. Fatin Samara & Bassam A. Abu-Nabah & Waleed El-Damaty & Mayyada Al Bardan, 2022. "Assessment of the Impact of the Human Coronavirus (COVID-19) Lockdown on the Energy Sector: A Case Study of Sharjah, UAE," Energies, MDPI, vol. 15(4), pages 1-19, February.
    10. Himeur, Yassine & Ghanem, Khalida & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2021. "Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives," Applied Energy, Elsevier, vol. 287(C).
    11. Jurado, Sergio & Nebot, Àngela & Mugica, Fransisco & Avellana, Narcís, 2015. "Hybrid methodologies for electricity load forecasting: Entropy-based feature selection with machine learning and soft computing techniques," Energy, Elsevier, vol. 86(C), pages 276-291.
    12. Yizhen Wang & Ningqing Zhang & Xiong Chen, 2021. "A Short-Term Residential Load Forecasting Model Based on LSTM Recurrent Neural Network Considering Weather Features," Energies, MDPI, vol. 14(10), pages 1-13, May.
    13. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    14. Shuo-Yan Chou & Anindhita Dewabharata & Ferani E. Zulvia & Mochamad Fadil, 2022. "Forecasting Building Energy Consumption Using Ensemble Empirical Mode Decomposition, Wavelet Transformation, and Long Short-Term Memory Algorithms," Energies, MDPI, vol. 15(3), pages 1-35, January.
    15. Athanasios Ioannis Arvanitidis & Dimitrios Bargiotas & Aspassia Daskalopulu & Vasileios M. Laitsos & Lefteri H. Tsoukalas, 2021. "Enhanced Short-Term Load Forecasting Using Artificial Neural Networks," Energies, MDPI, vol. 14(22), pages 1-14, November.
    16. Jong-Hyuk Im & Hee-Yong Kwon & Seong-Yun Jeon & Mun-Kyu Lee, 2019. "Privacy-Preserving Electricity Billing System Using Functional Encryption," Energies, MDPI, vol. 12(7), pages 1-15, April.
    17. Ke Yan & Xudong Wang & Yang Du & Ning Jin & Haichao Huang & Hangxia Zhou, 2018. "Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy," Energies, MDPI, vol. 11(11), pages 1-15, November.
    18. Simona-Vasilica Oprea & Adela Bâra & Florina Camelia Puican & Ioan Cosmin Radu, 2021. "Anomaly Detection with Machine Learning Algorithms and Big Data in Electricity Consumption," Sustainability, MDPI, vol. 13(19), pages 1-20, October.
    19. Cristina Hora & Florin Ciprian Dan & Gabriel Bendea & Calin Secui, 2022. "Residential Short-Term Load Forecasting during Atypical Consumption Behavior," Energies, MDPI, vol. 15(1), pages 1-15, January.
    20. Tingting Hou & Rengcun Fang & Jinrui Tang & Ganheng Ge & Dongjun Yang & Jianchao Liu & Wei Zhang, 2021. "A Novel Short-Term Residential Electric Load Forecasting Method Based on Adaptive Load Aggregation and Deep Learning Algorithms," Energies, MDPI, vol. 14(22), pages 1-21, November.
    21. Lusis, Peter & Khalilpour, Kaveh Rajab & Andrew, Lachlan & Liebman, Ariel, 2017. "Short-term residential load forecasting: Impact of calendar effects and forecast granularity," Applied Energy, Elsevier, vol. 205(C), pages 654-669.
    22. Emilio Ghiani & Marco Galici & Mario Mureddu & Fabrizio Pilo, 2020. "Impact on Electricity Consumption and Market Pricing of Energy and Ancillary Services during Pandemic of COVID-19 in Italy," Energies, MDPI, vol. 13(13), pages 1-19, July.
    23. Alexis Gerossier & Robin Girard & Alexis Bocquet & George Kariniotakis, 2018. "Robust Day-Ahead Forecasting of Household Electricity Demand and Operational Challenges," Energies, MDPI, vol. 11(12), pages 1-18, December.
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