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High-Dimensional Energy Consumption Anomaly Detection: A Deep Learning-Based Method for Detecting Anomalies

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  • Haipeng Pan

    (School of Mechanical and Automatic, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Zhongqian Yin

    (School of Mechanical and Automatic, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Xianzhi Jiang

    (School of Mechanical and Automatic, Zhejiang Sci-Tech University, Hangzhou 310018, China)

Abstract

With the increase of energy demand, energy wasteful behavior is inevitable. To reduce energy waste, it is crucial to understand users’ electricity consumption habits and detect abnormal usage behavior in a timely manner. This study proposes a high-dimensional energy consumption anomaly detection method based on deep learning. The method uses high-dimensional energy consumption related data to predict users’ electricity consumption in real time and for anomaly detection. The test results of the method on a publicly available dataset show that it can effectively detect abnormal electricity usage behavior of users. The results show that the method is useful in establishing a real-time anomaly detection system in buildings, helping building managers to identify abnormal electricity usage by users. In addition, users can also use the system to understand their electricity usage and reduce energy waste.

Suggested Citation

  • Haipeng Pan & Zhongqian Yin & Xianzhi Jiang, 2022. "High-Dimensional Energy Consumption Anomaly Detection: A Deep Learning-Based Method for Detecting Anomalies," Energies, MDPI, vol. 15(17), pages 1-14, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:17:p:6139-:d:896108
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    References listed on IDEAS

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    1. Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2020. "Robust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree," Applied Energy, Elsevier, vol. 267(C).
    2. Rashid, Haroon & Singh, Pushpendra & Stankovic, Vladimir & Stankovic, Lina, 2019. "Can non-intrusive load monitoring be used for identifying an appliance’s anomalous behaviour?," Applied Energy, Elsevier, vol. 238(C), pages 796-805.
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    Cited by:

    1. Yuping Zou & Rui Wu & Xuesong Tian & Hua Li, 2023. "Realizing the Improvement of the Reliability and Efficiency of Intelligent Electricity Inspection: IAOA-BP Algorithm for Anomaly Detection," Energies, MDPI, vol. 16(7), pages 1-15, March.

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