Discriminant analysis of user stealing electricity behavior based on Levenberg Marquardt neural network model

Zheng Zhu; Lei Yu; Chao Jiang; Yukun Xu

doi:10.1117/12.2684776

19 October 2023 Discriminant analysis of user stealing electricity behavior based on Levenberg Marquardt neural network model

Zheng Zhu, Lei Yu, Chao Jiang, Yukun Xu

Author Affiliations +

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127091F (2023) https://doi.org/10.1117/12.2684776
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

The operation status of the power distribution network is mainly reflected by the line loss and line loss ratio, which are reduced to facilitate the efficient use of electrical energy. The user electricity theft behaviors are harmful and are considered to be important aspects affecting the line loss rate. The identification of electricity stealing users is conducive to reducing the losses of power companies. In this paper, the user electricity consumption behavior evaluation index system is constructed by combining the causes of the user’s electricity consumption, line loss, and abnormal. The index system can dynamically reflect the power consumption behavior of users and help the fine management of power system. An anti-theft system based on Levenberg Marquardt (LM) neural network model is proposed to identify the user stealing electricity behavior. The 4-month electricity consumption data of 138 users of State Grid Shanghai Electric Power Company is taken as a research object. The classical decision tree model Classification and Regression Tree (CART) is compared, and the results showed that the accuracy of LM neural network model is as high as 97.2%, and it has more advantages in terms of training speed and computational accuracy. This study is beneficial for power enterprises to accurately and quickly find suspected users of electricity theft, , so as to reduce the loss of power energy and the economic loss of electric power enterprises, and promote the further improvement and development of the electric power system

Citation Download Citation

Zheng Zhu, Lei Yu, Chao Jiang, and Yukun Xu "Discriminant analysis of user stealing electricity behavior based on Levenberg Marquardt neural network model", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127091F (19 October 2023); https://doi.org/10.1117/12.2684776

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KEYWORDS

Neural networks

Power consumption

Artificial neural networks

Data modeling

Education and training

Evolutionary algorithms

Power grids

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