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Detection of non-technical losses in smart meter data based on load curve profiling and time series analysis

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  • Villar-Rodriguez, Esther
  • Del Ser, Javier
  • Oregi, Izaskun
  • Bilbao, Miren Nekane
  • Gil-Lopez, Sergio

Abstract

The advent and progressive deployment of the so-called Smart Grid has unleashed a profitable portfolio of new possibilities for an efficient management of the low-voltage distribution network supported by the introduction of information and communication technologies to exploit its digitalization. Among all such possibilities this work focuses on the detection of anomalous energy consumption traces: disregarding whether they are due to malfunctioning metering equipment or fraudulent purposes, strong efforts are invested by utilities to detect such outlying events and address them to optimize the power distribution and avoid significant income costs. In this context this manuscript introduce a novel algorithmic approach for the identification of consumption outliers in Smart Grids that relies on concepts from probabilistic data mining and time series analysis. A key ingredient of the proposed technique is its ability to accommodate time irregularities – shifts and warps – in the consumption habits of the user by concentrating on the shape of the consumption rather than on its temporal properties. Simulation results over real data from a Spanish utility are presented and discussed, from where it is concluded that the proposed approach excels at detecting different outlier cases emulated on the aforementioned consumption traces.

Suggested Citation

  • Villar-Rodriguez, Esther & Del Ser, Javier & Oregi, Izaskun & Bilbao, Miren Nekane & Gil-Lopez, Sergio, 2017. "Detection of non-technical losses in smart meter data based on load curve profiling and time series analysis," Energy, Elsevier, vol. 137(C), pages 118-128.
  • Handle: RePEc:eee:energy:v:137:y:2017:i:c:p:118-128
    DOI: 10.1016/j.energy.2017.07.008
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    References listed on IDEAS

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    1. Ahmad, Tanveer, 2017. "Non-technical loss analysis and prevention using smart meters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 573-589.
    2. Beckel, Christian & Sadamori, Leyna & Staake, Thorsten & Santini, Silvia, 2014. "Revealing household characteristics from smart meter data," Energy, Elsevier, vol. 78(C), pages 397-410.
    3. Smith, Thomas B., 2004. "Electricity theft: a comparative analysis," Energy Policy, Elsevier, vol. 32(18), pages 2067-2076, December.
    4. Liu, Xiufeng & Nielsen, Per Sieverts, 2016. "A hybrid ICT-solution for smart meter data analytics," Energy, Elsevier, vol. 115(P3), pages 1710-1722.
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    Cited by:

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    2. Barja-Martinez, Sara & Aragüés-Peñalba, Mònica & Munné-Collado, Íngrid & Lloret-Gallego, Pau & Bullich-Massagué, Eduard & Villafafila-Robles, Roberto, 2021. "Artificial intelligence techniques for enabling Big Data services in distribution networks: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    3. Matthew Boeding & Kelly Boswell & Michael Hempel & Hamid Sharif & Juan Lopez & Kalyan Perumalla, 2022. "Survey of Cybersecurity Governance, Threats, and Countermeasures for the Power Grid," Energies, MDPI, vol. 15(22), pages 1-22, November.
    4. Savian, Fernando de Souza & Siluk, Julio Cezar Mairesse & Garlet, Taís Bisognin & do Nascimento, Felipe Moraes & Pinheiro, José Renes & Vale, Zita, 2021. "Non-technical losses: A systematic contemporary article review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    5. Rongheng Lin & Fangchun Yang & Mingyuan Gao & Budan Wu & Yingying Zhao, 2019. "AUD-MTS: An Abnormal User Detection Approach Based on Power Load Multi-Step Clustering with Multiple Time Scales," Energies, MDPI, vol. 12(16), pages 1-19, August.

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