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An Energy-Fraud Detection-System Capable of Distinguishing Frauds from Other Energy Flow Anomalies in an Urban Environment

Author

Listed:
  • Netzah Calamaro

    (Faculty of Electrical and Electronics Engineering, Tel-Aviv University, Tel-Aviv 6997801, Israel)

  • Yuval Beck

    (Faculty of Electrical and Electronics Engineering, Tel-Aviv University, Tel-Aviv 6997801, Israel)

  • Ran Ben Melech

    (Faculty of Electrical and Electronics Engineering, Tel-Aviv University, Tel-Aviv 6997801, Israel)

  • Doron Shmilovitz

    (Faculty of Electrical and Electronics Engineering, Tel-Aviv University, Tel-Aviv 6997801, Israel)

Abstract

Energy fraud detection bears significantly on urban ecology. Reduced losses and power consumption would affect carbon dioxide emissions and reduce thermal pollution. Fraud detection also provides another layer of urban socio-economic correlation heatmapping and improves city energy distribution. This paper describes a novel algorithm of energy fraud detection, utilizing energy and energy consumption specialized knowledge poured into AI front-end. The proposed algorithm improves fraud detection’s accuracy and reduces the false positive rate, as well as reducing the preliminary required training dataset. The paper also introduces a holistic algorithm, specifying the major phenomena that disguises as energy fraud or affects it. Consequently, a mathematical foundation for energy fraud detection for the proposed algorithm is presented. The results show that a unique pattern is obtained during fraud, which is independent of a reference non-fraud pattern of the same customer. The theory is implemented on real data taken from smart metering systems and validated in real life scenarios.

Suggested Citation

  • Netzah Calamaro & Yuval Beck & Ran Ben Melech & Doron Shmilovitz, 2021. "An Energy-Fraud Detection-System Capable of Distinguishing Frauds from Other Energy Flow Anomalies in an Urban Environment," Sustainability, MDPI, vol. 13(19), pages 1-38, September.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:19:p:10696-:d:643723
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    References listed on IDEAS

    as
    1. Netzah Calamaro & Avihai Ofir & Doron Shmilovitz, 2021. "Application of Enhanced CPC for Load Identification, Preventive Maintenance and Grid Interpretation," Energies, MDPI, vol. 14(11), pages 1-41, June.
    2. Ahmad, Tanveer & Chen, Huanxin & Wang, Jiangyu & Guo, Yabin, 2018. "Review of various modeling techniques for the detection of electricity theft in smart grid environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2916-2933.
    3. Fan, Cheng & Xiao, Fu & Zhao, Yang & Wang, Jiayuan, 2018. "Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data," Applied Energy, Elsevier, vol. 211(C), pages 1123-1135.
    4. Smith, Thomas B., 2004. "Electricity theft: a comparative analysis," Energy Policy, Elsevier, vol. 32(18), pages 2067-2076, December.
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    Cited by:

    1. Netzah Calamaro & Moshe Donko & Doron Shmilovitz, 2021. "A Highly Accurate NILM: With an Electro-Spectral Space That Best Fits Algorithm’s National Deployment Requirements," Energies, MDPI, vol. 14(21), pages 1-37, November.
    2. Xuesong Tian & Yuping Zou & Xin Wang & Minglang Tseng & Hua Li & Huijuan Zhang, 2022. "Improving the Efficiency and Sustainability of Intelligent Electricity Inspection: IMFO-ELM Algorithm for Load Forecasting," Sustainability, MDPI, vol. 14(21), pages 1-19, October.

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