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Outlier Detection in Buildings’ Power Consumption Data Using Forecast Error

Author

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  • Gustavo Felipe Martin Nascimento

    (Univ. Grenoble Alpes, CNRS, Grenoble INP, G2Elab, F-38000 Grenoble, France
    Department of Electrical and Electronic Engineering, Universidade Federal de Santa Catarina, Florianópolis 88040-900, Brazil)

  • Frédéric Wurtz

    (Univ. Grenoble Alpes, CNRS, Grenoble INP, G2Elab, F-38000 Grenoble, France)

  • Patrick Kuo-Peng

    (Department of Electrical and Electronic Engineering, Universidade Federal de Santa Catarina, Florianópolis 88040-900, Brazil)

  • Benoit Delinchant

    (Univ. Grenoble Alpes, CNRS, Grenoble INP, G2Elab, F-38000 Grenoble, France)

  • Nelson Jhoe Batistela

    (Department of Electrical and Electronic Engineering, Universidade Federal de Santa Catarina, Florianópolis 88040-900, Brazil)

Abstract

Buildings play a central role in energy transition, as they were responsible for 67.8% of the total consumption of electricity in France in 2017. Because of that, detecting anomalies (outliers) is crucial in order to identify both potential opportunities to reduce energy consumption and malfunctioning of the metering system. This work aims to compare the performance of several outlier detection methods, such as classical statistical methods (as boxplots) applied to the actual measurements and to the difference between the measurements and their predictions, in the task of detecting outliers in the power consumption data of a tertiary building located in France. The results show that the combination of a regression method, such as random forest, and the adjusted boxplot outlier detection method have promising potential in detecting this type of data quality problem in electricity consumption.

Suggested Citation

  • Gustavo Felipe Martin Nascimento & Frédéric Wurtz & Patrick Kuo-Peng & Benoit Delinchant & Nelson Jhoe Batistela, 2021. "Outlier Detection in Buildings’ Power Consumption Data Using Forecast Error," Energies, MDPI, vol. 14(24), pages 1-15, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:24:p:8325-:d:699468
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    References listed on IDEAS

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    3. 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).
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    Cited by:

    1. Haicheng Ling & Pierre-Yves Massé & Thibault Rihet & Frédéric Wurtz, 2023. "Realistic Nudging through ICT Pipelines to Help Improve Energy Self-Consumption for Management in Energy Communities," Energies, MDPI, vol. 16(13), pages 1-24, July.
    2. Matthias Gross & Marco Sonnberger, 2022. "Making the Most of Failure and Uncertainty: Welcome Surprises and Contingency in Energy Transition Research," Energies, MDPI, vol. 15(18), pages 1-3, September.
    3. Gustavo Felipe Martin Nascimento & Frédéric Wurtz & Patrick Kuo-Peng & Benoit Delinchant & Nelson Jhoe Batistela, 2022. "Quantifying Compressed Air Leakage through Non-Intrusive Load Monitoring Techniques in the Context of Energy Audits," Energies, MDPI, vol. 15(9), pages 1-24, April.
    4. Changzhi Li & Dandan Liu & Mao Wang & Hanlin Wang & Shuai Xu, 2023. "Detection of Outliers in Time Series Power Data Based on Prediction Errors," Energies, MDPI, vol. 16(2), pages 1-19, January.

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