Abstract:The electric vehicle (EV) charging load has strong randomness. It is affected by battery capacity and user behavior. To effectively forecast the temporal distribution of charging load, this paper proposes a charging load interval forecasting method that takes into account conflicting evaluation indicators. First, this method analyzes the temporal correlation between daytime charging loads and constructs a feature set for charging load forecasting using strongly correlated historical daily charging load data. Next, the Warped Gaussian Process (WGP) method is adopted, and multiple covariance functions are combined to construct multiple charging load interval forecasting models. To solve the problem of conflicts in multiple index and extreme errors in selecting only the optimal forecasting model, the area grey correlation decision-making method is applied. Conduct a comprehensive evaluation of each model taking into account the conflict of evaluation index, and obtain the area grey correlation closeness of each model. Construct an EV charging load combination interval forecasting model based on the proximity degree of area grey correlation. The experimental results show that the method proposed in this article can obtain more accurate and higher coverage charging load forecasting intervals.