Integrated human-machine intelligence for EV charging prediction in 5G smart grid

Abstract

Abstract With the rapid development of the power infrastructures and the increase in the number of electric vehicles (EVs), vehicle-to-grid (V2G) technologies have attracted great interest in both academia and industry as an energy management technology in 5G smart grid. Considering the inherently high mobility and low reliability of EVs, it is a great challenge for the smart grid to provide on-demand services for EVs. Therefore, we propose a novel smart grid architecture based on network slicing and edge computing technologies for the 5G smart grid. Under this architecture, the bidirectional traffic information between smart grids and EVs is collected to improve the EV charging experience and decrease the cost of energy service providers. In addition, the accurate prediction of EV charging behavior is also a challenge for V2G systems to improve the scheduling efficiency of EVs. Thus, we propose an EV charging behavior prediction scheme based on the hybrid artificial intelligence to identify targeted EVs and predict their charging behavior in this paper. Simulation results show that the proposed prediction scheme outperforms several state-of-the-art EV charging behavior prediction methods in terms of prediction accuracy and scheduling efficiency.

Keywords

• Smart grid
• Charging behavior
• Deep learning
• Charging behavior prediction