Artificial Intelligence-based Real-Time Electricity Metering Data Analysis and its Application to Anti-Theft Actions

Abstract: This study focuses on the key issue of anti-stealing behavior identification in power systems, aiming to improve the security and efficiency of power energy management. Under the current background of intelligent power grid, the existence of anti-theft phenomenon not only causes serious economic losses, but also poses a threat to the stability of power grid operation. Aiming at this situation, this paper proposes a novel and effective feature extraction and optimization method, which utilizes the recursive feature elimination (rfe) technique, combined with the correlation and exclusion analysis of the features, to achieve the deep screening and dimensionality reduction of a large amount of raw data, so as to refine the core feature set that has the most differentiation for the anti-stolen power behavior. During the research process, this paper constructed a hybrid model integrating long short-term memory network (LSTM) and autoencoder. The model cleverly combines the advantages of LSTM in capturing time series dependency and the powerful ability of autoencoder in feature learning and noise reduction, and is especially designed for targeted enhancement of anti-electricity theft behaviors to achieve real-time and accurate behavior recognition. In order to verify the performance and practicality of the proposed method, this paper carries out rigorous simulation experiments and practical case studies. By comparing the classical anti-electricity theft recognition methods, the results show that the hybrid model proposed in this study exhibits significant advantages in both recognition accuracy and response speed. Whether in the simulation environment or actual application scenarios, this method can effectively identify and warn potential power theft behavior, thus providing a strong technical support for the power company’s anti-power theft management.

Kai Liu, Anlei Liu, Xun Ma and Xuchao Jia. “Artificial Intelligence-based Real-Time Electricity Metering Data Analysis and its Application to Anti-Theft Actions”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.6 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150676

@article{Liu2024,
title = {Artificial Intelligence-based Real-Time Electricity Metering Data Analysis and its Application to Anti-Theft Actions},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150676},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150676},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {6},
author = {Kai Liu and Anlei Liu and Xun Ma and Xuchao Jia}
}