Anomaly electricity detection method based on entropy weight method and isolated forest algorithm
This study aims at investigating the applicability of abnormal electricity consumption data detection method, which is based on the entropy weight method and the isolated forest tree algorithm. The inaccessibility and imbalance of abnormal electricity consumption samples in actual data sets are cons...
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| Veröffentlicht in: | Frontiers in energy research Jg. 10 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Frontiers Media S.A
31.08.2022
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| Abstract | This study aims at investigating the applicability of abnormal electricity consumption data detection method, which is based on the entropy weight method and the isolated forest tree algorithm. The inaccessibility and imbalance of abnormal electricity consumption samples in actual data sets are considered by analyzing smart distribution network power consumption big data. Firstly, the users are classified by the k-means clustering algorithm, and then the characteristics of each type of user are extracted and the feature set is processed by the principal component analysis method to reduce the dimensions, followed by the entropy weight method adaptive configuration of the weight coefficients of each feature index, and finally the abnormal power consumption users are calculated based on the feature-weighted isolated forest algorithm. The algorithm verifies the real electricity consumption data of 6,445 users, and the results show that the method has a high detection accuracy, recall rate and F1 score, which is more suitable for the detection of abnormal electricity consumption in scenarios when there are complex and diverse user power consumption behaviors. |
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| AbstractList | This study aims at investigating the applicability of abnormal electricity consumption data detection method, which is based on the entropy weight method and the isolated forest tree algorithm. The inaccessibility and imbalance of abnormal electricity consumption samples in actual data sets are considered by analyzing smart distribution network power consumption big data. Firstly, the users are classified by the k-means clustering algorithm, and then the characteristics of each type of user are extracted and the feature set is processed by the principal component analysis method to reduce the dimensions, followed by the entropy weight method adaptive configuration of the weight coefficients of each feature index, and finally the abnormal power consumption users are calculated based on the feature-weighted isolated forest algorithm. The algorithm verifies the real electricity consumption data of 6,445 users, and the results show that the method has a high detection accuracy, recall rate and F1 score, which is more suitable for the detection of abnormal electricity consumption in scenarios when there are complex and diverse user power consumption behaviors. |
| Author | Jianyuan, Wang Kechen, Liu Chengcheng, Gu |
| Author_xml | – sequence: 1 givenname: Wang surname: Jianyuan fullname: Jianyuan, Wang – sequence: 2 givenname: Gu surname: Chengcheng fullname: Chengcheng, Gu – sequence: 3 givenname: Liu surname: Kechen fullname: Kechen, Liu |
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| CitedBy_id | crossref_primary_10_1007_s10489_025_06663_3 crossref_primary_10_1016_j_energy_2025_138053 crossref_primary_10_1088_1361_6579_acff35 crossref_primary_10_1049_tje2_12380 crossref_primary_10_3390_en17122988 crossref_primary_10_3390_en18143722 crossref_primary_10_1038_s41598_025_96612_4 crossref_primary_10_1109_JSEN_2024_3426090 crossref_primary_10_3390_s25175265 |
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| Title | Anomaly electricity detection method based on entropy weight method and isolated forest algorithm |
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