Construction of user abnormal behavior detection model based on smart charging platform for electric vehicles

As the number of users using electric vehicles continues to increase, the amount of user charging behavior data in the electric vehicle smart charging platform has also shown an explosive growth trend. By profiling users' daily charging behaviors, it is used to detect whether users' charging behaviors are abnormal and prevent the generation of behaviors such as electricity theft. In this paper, we propose a method to construct a user abnormal behavior detection model based on a smart charging platform. The aggregation validity index is constructed and used to determine the optimal classification number K value of the K-means clustering algorithm, the optimal set of user features is extracted by the redundant dynamic weight feature selection algorithm, the abnormality threshold is set, and finally the user abnormal behavior model is constructed based on softmax regression. Finally, the effectiveness of the method is demonstrated by comparison analysis.