Investigating the Spatiotemporal Charging Demand and Travel Behavior of Electric Vehicles Using GPS Data: A Machine Learning Approach
This addresses the challenge of managing electricity demand from EVs for power system planners, but it is incremental as it applies existing methods to new data.
The study tackled the problem of forecasting daily charging demand for electric vehicles by analyzing GPS data from both electric and conventional vehicles, finding that travel behaviors are similar and that their models can accurately predict spatiotemporal patterns.
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.