2nd China International Youth Conference on Electrical Engineering (CIYCEE 2021)

To solve the problems of over-theorization and lack of real data in the current research, this paper proposes a data-driven EV charging load demand forecasting model. The model is based on analysis of residents’ travel patterns hided in EV travel data and single EV charging & discharging model considering its related characteristics. The results of a calculation example in Chengdu show that the proposed model can effectively predict the temporal and spatial distribution characteristics of EV charging load in different urban functional areas and in different time ranges. This provides a basis for the construction of charging stations and charging load management after EV have been applied in large scale. To solve the problems of over-theorization and lack of real data in the current research, this paper proposes a data-driven EV charging load demand forecasting model. The model is based on analysis of residents’ travel patterns hided in EV travel data and single EV charging & discharging model considering its related characteristics. The results of a calculation example in Chengdu show that the proposed model can effectively predict the temporal and spatial distribution characteristics of EV charging load in different urban functional areas and in different time ranges. This provides a basis for the construction of charging stations and charging load management after EV have been applied in large scale.

electric vehicle (EV); data mining; charging load forecasting; simulation