A Multi Time Scale Prediction Model for Electric Vehicle Charging Load Based on LightGBM Algorithm and Travel Chain Theory
To improve the prediction accuracy of electric vehicle charging load,a multi time scale prediction model for electric vehicle charging load was designed based on the Lightweight Gradient Boosting Machine(LightGBM)algorithm and travel chain theory.The travel chain was used to describe the user's travel process,Monte Carlo method was used to extract the spatiotemporal data,and the probability density functions of travel and stay time in different regions was calculated.Newton method was used to divide the probability of charging at multiple time scales,clarifying the spatiotemporal distribution of driving and charging conditions.Fuzzy mathematics theorem and LightGBM were applied to classify charging load data,and a multi season and multi time prediction model were constructed.The efficient parallel computing mode of LightGBM was applied which clarified the variation pattern of charging load,and multi time scale prediction was achieved.The experimental results show that the established model has a prediction error of less than 100 kW and a prediction false alarm rate of less than 3％under different seasons and the number of electric vehicles,and can accurately display the variation pattern of charging load.
Light Gradient Boosting Machine(LightGBM)Travel chain theoryCharging loadMultiple time scalesPrediction model
万方数据
维普
