A Spatial-Temporal Forecasting Method for Electric Vehicle Charging Load Based on GDC-LSTM User Travel Characteristics Analysis

In this study, a spatio-temporal prediction model for EV charging loads is proposed based on the Graph diffusion convolution-Long short-term memory network (GDC-LSTM). This model integrates diverse sources of information, including weather conditions, day types, and temperatures. Firstly, the GDC-LSTM is employed to predict EV traffic flow data, capturing the travel patterns of EV users. Subsequently, a real-time model for EV power consumption is developed, and the floyd shortest path algorithm is optimized by introducing real-time road saturation data.. Finally, employing the Monte Carlo method, spatio-temporal distributions of various EV charging loads are forecasted and mapped onto distribution network nodes. The effectiveness of the methodology is verified by demonstrating the model's ability to predict EV travel behavior and load distribution through a case study in the main urban area of Chengdu, and evaluating the impact of load access on distribution network node voltages.