Detection of stealing electricity energy based on improved fuzzy C-means clustering

Han Liu; Yunfeng Zheng; Xiaobin Li; Shuang Li; Xiangzhi Xiong

doi:10.1117/12.2685223

19 October 2023 Detection of stealing electricity energy based on improved fuzzy C-means clustering

Han Liu, Yunfeng Zheng, Xiaobin Li, Shuang Li, Xiangzhi Xiong

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127090I (2023) https://doi.org/10.1117/12.2685223
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

In this paper, a detection model of power stealing behavior based on improved fuzzy c-means clustering was proposed, which was suitable for the situation without a large number of known power stealing user samples. The model includes factor analysis, local outlier calculation based on improved fuzzy c-means clustering, model evaluation and parameter adjustment with ROC curve, and the selection of the best detection threshold. Through factor analysis, the dimension specification of user’s power consumption characteristics was carried out to improve the efficiency of model detection. The paper improved the FCM clustering algorithm with genetic simulated annealing algorithm. Then, the paper detected the user's power consumption characteristics, compared with the existing mature algorithm, the result indicates that the model has a high detection accuracy for electricity theft. The detection model can output the power consumption behavior outlier and the power stealing probability order of all the tested users. The output of the model of detection can detect the power stealing users with high precision, and the efficiency of anti-stealing work can be improve.

Citation Download Citation

Han Liu, Yunfeng Zheng, Xiaobin Li, Shuang Li, and Xiangzhi Xiong "Detection of stealing electricity energy based on improved fuzzy C-means clustering", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127090I (19 October 2023); https://doi.org/10.1117/12.2685223

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