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Open AccessArticle

AUD-MTS: An Abnormal User Detection Approach Based on Power Load Multi-Step Clustering with Multiple Time Scales

State Key Lab of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
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Energies 2019, 12(16), 3144; https://doi.org/10.3390/en12163144
Received: 18 July 2019 / Revised: 8 August 2019 / Accepted: 12 August 2019 / Published: 15 August 2019
(This article belongs to the Section Smart Grids and Microgrids)
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Abstract

With the rapid growth of Smart Grid, electricity load analysis has become the simplest and most effective way to divide user groups and understand user behavior. This paper proposes an AUD-MTS (Abnormal User Detection approach based on power load multi-step clustering with Multiple Time Scales). Firstly, we combine RBM (Restricted Boltzmann Machine) hidden feature learning with K-Means clustering to extract typical load patterns in the short-term. Secondly, time scale conversion is performed so that the analysis subject can be transformed from load pattern to user behavior. Finally, a two-step clustering in long-term is adopted to divide users from both coarse-grained and fine-grained dimensions so as to detect abnormal users referring to customized OutlierIndex. Experiments are conducted using annual 24-point power load data of American users in all states. The accuracy of clustering methods in AUD-MTS reaches 87.5% referring to the 16 commercial building types defined by the U.S. Department of Energy, which outperforms other common clustering algorithms on AMI (Advanced Metering Infrastructure). After that, the OutlierIndex score of AUD-MTS can be increased by 0.16 compared with other outlier detection algorithms, which shows that the proposed method can detect abnormal users precisely and efficiently. Furthermore, we summarized possible causes including federal holidays, climate zones and summertime that may lead to abnormal behavior changes and discussed countermeasures respectively, which accounts for 82.3% of anomalies. The rest may be potential electricity stealing users, which requires further investigation. View Full-Text
Keywords: abnormal user; outlier detection; multiple time scales; load pattern; user classification; power load abnormal user; outlier detection; multiple time scales; load pattern; user classification; power load
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MDPI and ACS Style

Lin, R.; Yang, F.; Gao, M.; Wu, B.; Zhao, Y. AUD-MTS: An Abnormal User Detection Approach Based on Power Load Multi-Step Clustering with Multiple Time Scales. Energies 2019, 12, 3144.

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