论文标题
使用GPS数据研究电动汽车的时空充电需求和旅行行为：一种机器学习方法
Investigating the Spatiotemporal Charging Demand and Travel Behavior of Electric Vehicles Using GPS Data: A Machine Learning Approach
论文作者
论文摘要
电动汽车（EV）的市场渗透不断提高可能会改变驾驶员的旅行行为，并对电力系统产生巨大的电力需求。由于电力需求取决于电动汽车的旅行行为，而电动汽车本质上是不确定的，因此对日常充电需求（CD）的预测将是一项艰巨的任务。在本文中，我们使用了来自同一城市的电动汽车和常规汽油动力车的记录的GPS数据，以调查驾驶员从传统车辆到电动汽车的旅行行为的潜在转变，并预测每日CD的时空模式。我们的分析表明，电动汽车和常规车辆的旅行行为相似。同样，预测结果表明，开发的模型可以生成每日CD的准确时空模式。
The increasing market penetration of electric vehicles (EVs) may change the travel behavior of drivers and pose a significant electricity demand on the power system. Since the electricity demand depends on the travel behavior of EVs, which are inherently uncertain, the forecasting of daily charging demand (CD) will be a challenging task. In this paper, we use the recorded GPS data of EVs and conventional gasoline-powered vehicles from the same city to investigate the potential shift in the travel behavior of drivers from conventional vehicles to EVs and forecast the spatiotemporal patterns of daily CD. Our analysis reveals that the travel behavior of EVs and conventional vehicles are similar. Also, the forecasting results indicate that the developed models can generate accurate spatiotemporal patterns of the daily CD.
