Charging load prediction method of shared vehicles based on data analysis of spatiotemporal characteristic variables

The large-scale application of shared vehicles will bring new challenges to power grid operation and charging facility planning. At present, the research on the prediction method of shared vehicle charging load is not thorough enough, therefore, a load prediction method based on data analysis of spatiotemporal characteristic variables is proposed. Through data mining, a two-dimensional dynamic traffic behavior model supported by spatiotemporal characteristic variables is constructed. In order to explore the characteristics of continuous charging and centralized charging of shared vehicles, two charging scenarios of continuous char-ging and centralized charging are set up, based on which, the charging behavior model is established. The Monte Carlo method is used to simulate the traffic-charging behavior of shared vehicles, and the predictive results of charging load at different times and areas are calculated, and the influence of the load on the power grid is analyzed. Simulation analysis results show that the spatiotemporal characteristic variables of interaction can reasonably describe the characteristics of time-space two-dimensional uncertain changes of shared vehicles, and the proposed method can make scientific prediction on the randomly dispersed shared vehicle charging loads, providing an effective basis for the formulation of load management strategies for power grids and shared vehicle users. © 2019, Electric Power Automation Equipment Press. All right reserved.