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A Study of Electricity Theft Detection Method Based on Anomaly Transformer

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Big Data (BigData 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2005))

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Abstract

Electricity theft not only disrupts normal electricity consumption but also poses a significant security threat to the power system. The widespread deployment of smart meters has led to the collection of massive amounts of electricity consumption data, which can help identify electricity theft. However, the challenge of detecting electricity theft is heightened by the category imbalance in the electricity consumption data collected. In this study, we address this problem by using ADASYN resampling technology to balance data categories, and then develop a model based on Anomaly Transformer (AT) to identify electricity theft by analyzing historical data that deviates from normal patterns following a theft. The model uses an attention mechanism to calculate and extract the series-association between power consumption data streams, and a Gaussian kernel to calculate the priori-association of the relative temporal distance between power consumption data points and their neighbors. We validate the proposed model using the SGCC dataset, and our experimental results demonstrate high accuracy, precision, F1-score, and AUC values.

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Author information

Authors and Affiliations

  1. Shihezi University, Shihezi, 832003, China

    Shufen Chen, Yikun Yang, Shuaiying You & Zhigang Li

  2. **njiang Tianfu Information Co., Ltd, Shihezi, 832003, China

    Wenbin Chen

Authors
  1. Shufen Chen
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  2. Yikun Yang
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  3. Shuaiying You
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  4. Wenbin Chen
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  5. Zhigang Li
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Corresponding author

Correspondence to Zhigang Li .

Editor information

Editors and Affiliations

  1. University of Science and Technology of China, Hefei, China

    Enhong Chen

  2. Nan**g University, Nan**g, China

    Yang Gao

  3. Macquarie University, Sydney, NSW, Australia

    Longbing Cao

  4. Nan**g University of Posts and Telecommunications, Nan**g, China

    Fu ** Cui

  5. Nan**g University, Nan**g, China

    Rong Gu

  6. Taiyuan University of Technology, Taiyuan, China

    Li Wang

  7. Shenzhen University, Shenzhen, China

    Laizhong Cui

  8. Nan**g Normal University, Nan**g, China

    Wanqi Yang

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Chen, S., Yang, Y., You, S., Chen, W., Li, Z. (2023). A Study of Electricity Theft Detection Method Based on Anomaly Transformer. In: Chen, E., et al. Big Data. BigData 2023. Communications in Computer and Information Science, vol 2005. Springer, Singapore. https://doi.org/10.1007/978-981-99-8979-9_13

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  • DOI: https://doi.org/10.1007/978-981-99-8979-9_13

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8978-2

  • Online ISBN: 978-981-99-8979-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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