Prediction of Subjective Charging Behavior of Electric Vehicles Based on Improved Deep Residual Network

Situational awareness of electric vehicle charging behavior is an important prerequisite for active distribution network to realize charging demand analysis and controllable resource regulation. However, the subjective difference and randomness of charging behavior greatly affect the accuracy of charging demand analysis. In this regard, this paper proposes a method for predicting the subjective charging behavior of electric vehicles based on deep residual networks. Firstly, the K-means clustering algorithm is used to obtain the non-differentiated typical charging behavior. Secondly, a differentiated subjective behavior characteristic model is constructed with multi-dimensional influencing factors as input and charging behavior characteristics as output of the model. The input and output are correlated, and the non-linear related parts are deep knowledge mined and non-linear curve fit through the deep residual network. Taking a Chinese electric vehicle data set as an example for simulation verification, the results show that the proposed method can effectively distinguish the linear and nonlinear relationships between multi-dimensional factors and charging behavior characteristics, and has high-precision charging behavior prediction capabilities.