The load forecast of electric vehicles(EVs)is the foundation of planning and scheduling of charging stations. Compared with the traditional method

the load forecast method under big data has the feature that the data to be forecast is quickly observable

real time

etc. Hence the need of the corresponding adjustments of the load forecast methods. This paper first analyzes the data demand for charging station planning and scheduling

and then the ways of main data acquisition. Based on volume

variety

velocity data

each EV's start time

duration and location for charging

it will be possible to build the load model of a single EV. Furthermore

the total charging power of a charging station can be estimated by origin-destination(OD)flow statistics or adding up all the EV loads that are connected with its related transport line and node. Finally

a case study is given around the load forecast of EV station

and the load forecasting results from different load forecasting methods are compared. This work is supported by National Natural Science Foundation of China(No. 61104090)and National Key Technologies R&D Program(No. 2013BAA01B01).