AUD-MTS: An Abnormal User Detection Approach Based on Power Load Multi-Step Clustering with Multiple Time Scales
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). Fir...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-08-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/12/16/3144 |
| _version_ | 1797999144961310720 |
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| author | Rongheng Lin Fangchun Yang Mingyuan Gao Budan Wu Yingying Zhao |
| author_facet | Rongheng Lin Fangchun Yang Mingyuan Gao Budan Wu Yingying Zhao |
| author_sort | Rongheng Lin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T10:59:56Z |
| format | Article |
| id | doaj.art-afc3fa438a774b3292836a7fd62f7354 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T10:59:56Z |
| publishDate | 2019-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-afc3fa438a774b3292836a7fd62f73542022-12-22T04:28:40ZengMDPI AGEnergies1996-10732019-08-011216314410.3390/en12163144en12163144AUD-MTS: An Abnormal User Detection Approach Based on Power Load Multi-Step Clustering with Multiple Time ScalesRongheng Lin0Fangchun Yang1Mingyuan Gao2Budan Wu3Yingying Zhao4State Key Lab of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaState Key Lab of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaState Key Lab of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaState Key Lab of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaState Key Lab of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaWith 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.https://www.mdpi.com/1996-1073/12/16/3144abnormal useroutlier detectionmultiple time scalesload patternuser classificationpower load |
| spellingShingle | Rongheng Lin Fangchun Yang Mingyuan Gao Budan Wu Yingying Zhao AUD-MTS: An Abnormal User Detection Approach Based on Power Load Multi-Step Clustering with Multiple Time Scales Energies abnormal user outlier detection multiple time scales load pattern user classification power load |
| title | AUD-MTS: An Abnormal User Detection Approach Based on Power Load Multi-Step Clustering with Multiple Time Scales |
| title_full | AUD-MTS: An Abnormal User Detection Approach Based on Power Load Multi-Step Clustering with Multiple Time Scales |
| title_fullStr | AUD-MTS: An Abnormal User Detection Approach Based on Power Load Multi-Step Clustering with Multiple Time Scales |
| title_full_unstemmed | AUD-MTS: An Abnormal User Detection Approach Based on Power Load Multi-Step Clustering with Multiple Time Scales |
| title_short | AUD-MTS: An Abnormal User Detection Approach Based on Power Load Multi-Step Clustering with Multiple Time Scales |
| title_sort | aud mts an abnormal user detection approach based on power load multi step clustering with multiple time scales |
| topic | abnormal user outlier detection multiple time scales load pattern user classification power load |
| url | https://www.mdpi.com/1996-1073/12/16/3144 |
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