Identification of Abnormal Electricity Consumption Behavior of Low-Voltage Users in New Power Systems Based on a Combined Method

Jiaolong Gou, Xudong Niu, Xi Chen, Shuxin Dong and Jing Xin ()
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Jiaolong Gou: School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China
Xudong Niu: Department of New Quality Productive Forces, Shaanxi Transportation Holding Group Co., Ltd., Xi’an 710065, China
Xi Chen: Information and Communication Company, State Grid Shaanxi Electric Power Company Limited, Xi’an 710004, China
Shuxin Dong: School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China
Jing Xin: School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China

Energies, 2025, vol. 18, issue 10, 1-17

Abstract: With the rapid growth in low-voltage electricity demand, abnormal electricity consumption behavior is becoming more and more frequent, which not only threatens the safe and stable operation of power systems, but also causes huge economic losses. In order to effectively meet this challenge, it is of great practical significance to carry out monitoring and analysis of abnormal power consumption of low-voltage users. In this paper, a new detection model of abnormal power consumption behavior of low-voltage power users in power system based on the hybrid model, namely the K-GBDT model, is proposed. The model combines the GBDT (Gradient Boosting Decision Tree) algorithm with the KNN (K-Nearest Neighbor) algorithm, effectively leveraging the strengths of both approaches. The K-GBDT model employs a two-stage classification strategy. In the first stage, the GBDT algorithm leverages its robust feature learning and nonlinear classification capabilities to perform coarse-grained classification, extracting global patterns and categorical information. In the second stage, based on the coarse classification results from GBDT, the data are partitioned into multiple subsets, and the KNN algorithm is applied to fine classification within each subset. This hybrid approach enables the K-GBDT model to effectively integrate GBDT’s global modeling strength with KNN’s local classification advantages. Comparative experiments and practical applications of the K-GBDT model against standalone GBDT and KNN algorithms were conducted. To further validate the proposed method, a comparative analysis was conducted against the Long Short-Term Memory Autoencoder (LSTM-AE) model. The experimental results demonstrate that the proposed K-GBDT model outperforms single-algorithm models in both classification accuracy and model generalization capability, enabling more accurate identification of abnormal electricity consumption behaviors among low-voltage users. This provides reliable technical support for intelligent management in power systems.

Keywords: abnormal electricity consumption behavior; GBDT; KNN; K-GBDT (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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