Short-term load prediction of electric vehicle charging stations based on conditional generative adversarial networks

In order to solve the problems of high average absolute error and long time consumption in traditional forecasting methods, a short-term load prediction method of electric vehicle charging stations based on conditional generative adversarial networks is proposed. This method involves the analysis of the initial charging time, initial state of charge, and battery characteristics of electric vehicles. Based on the analysis results, a conditional generative adversarial networks (CGAN) model is constructed to anticipate the short-term load of electric vehicle charging stations. In the CGAN model, the charging start time, initial state of charge, and battery characteristics of electric vehicles serve as conditional values. Through training, the model learns the relationship between these conditions and the target, generating accurate load forecasting results. Experimental findings reveal that the proposed method boasts a maximum average absolute error of merely 1.4% and a minimum prediction time of just 1.26 seconds, thus demonstrating its practicality.