Research on power grid user behavior analysis method based on deep learning

The operation of power grid enterprises is constantly developing towards intelligence, and the number of smart meter users is also increasing. Behavioral analysis of power grid users can detect malicious electricity theft in a timely manner and provide data support for the overall operation of the power grid. Deep learning technology has the ability to interpret data by learning specific data sets, and it has unique technical advantages in the fields of graphics, text, audio and search. In view of the common problems of low analysis efficiency and inaccurate detection results in traditional power grid user behavior analysis methods, this paper conducts research on power grid user behavior analysis based on deep learning. A power grid user behavior analysis model combining external factors and deep learning neural networks is proposed. It has been verified that the model can effectively improve the accuracy of power grid user behavior analysis.