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Enhancing power utilization analysis: detecting aberrant patterns of electricity consumption

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Abstract

Detection of abnormal power consumption is very important to reduce irregular power consumption and economic losses. To reduce power wastage, it is necessary to understand the power consumption habits of users and detect unusual usage behavior promptly. Researchers have developed various methods to detect abnormal electricity consumption behavior. However, it does not provide enough consideration to identify the relationships between different time stamps and multi-scale characteristics of the power utilization series. Therefore, a grey relation projection–based better clustering learning technique is proposed for assessing power consumption abnormalities. Power data from a power consumption information gathering system is analyzed with a diagnosis model for anomalous electricity use by customers constructed using big data techniques and machine learning-related algorithms. Density-based clustering is offered as a means of analyzing instances of excessive electricity use. First, consumers in the same area are grouped based on the similarities between their electricity usage patterns. Then, using density clustering, we identify the most extreme cases of electricity use amongst consumers who are otherwise comparable. Using a comparable user electricity consumption framework and the historical electricity usage approach, we can determine the degree to which these anomalies match the norm. Finally, the threshold value allows for discrimination of anomalous electricity usage with the level of comprehensive support. The proposed DBSCAN approach attains the PR-AUC values of 0.96, and AUC attains 0.911, results from simulations demonstrate that this approach can detect aberrant patterns of electricity consumption.

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Funding

Science and technology project support of State Grid Liaoning Electric Power Co., Ltd, 2022YF-106 Research and application of auxiliary inspection technology based on AI scene.

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Authors and Affiliations

  1. State Grid Jinzhou Electric Power Supply Company, Jinzhou, 121000, China

    Yong Qian, Yuetian Wang & Jing Shao

Authors
  1. Yong Qian
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  2. Yuetian Wang
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  3. Jing Shao
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Contributions

All authors agreed on the content of the study. YQ, YW, and JS collected all the data for analysis. YQ agreed on the methodology. YQ, YW, and JS completed the analysis based on agreed steps. Results and conclusions are discussed and written together. The author read and approved the final manuscript.

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Correspondence to Yong Qian.

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Qian, Y., Wang, Y. & Shao, J. Enhancing power utilization analysis: detecting aberrant patterns of electricity consumption. Electr Eng (2024). https://doi.org/10.1007/s00202-024-02306-x

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