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.
关键词
轻量级梯度提升机/出行链理论/充电负荷/多时间尺度/预测模型
Key words
Light Gradient Boosting Machine(LightGBM)/Travel chain theory/Charging load/Multiple time scales/Prediction model
维普
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