Prediction of Charging Station Load Uncertainty Based on Multi-Dimensional Quantile Spatial-Temporal Traffic Flow Forecast

The uncertainty prediction of electric vehicle charging station load plays a crucial role in ensuring the stable operation of power grids. In this paper, a charging station load uncertainty prediction method based on multi-dimensional quantile spatial-temporal traffic flow forecast is proposed. First, considering the complex spatial-temporal correlation characteristics of traffic flow, a traffic flow forecast model based on multi-dimensional spatial-temporal graph convolutional network is designed. The model can deeply explore spatial-temporal correlation characteristics and effectively capture both the static spatial correlations with fixed topological structures and the dynamic spatial correlations of real-time traffic flow. Second, taking into account the importance of prediction results under different confidence levels, this paper combines the forecast model with a weighted quantile regression model and introduces a novel weighted loss function, which can achieve traffic flow uncertainty prediction without the need to predefine the uncertainty probability density distribution and meet the differentiated requirements for uncertainty prediction in grid operation and business decision-making. Furthermore, considering the influence of factors such as road congestion and electricity prices on charging load, a charging station load uncertainty prediction model based on an enhanced queuing model is proposed, which can more accurately convert traffic flow uncertainty into load uncertainty, and thus more accurately simulate the charging station loads. Finally, the simulation results verify the effectiveness of the proposed method.