电力系统自动化2016，Vol.40Issue(12)：68-74,7.DOI:10.7500/AEPS20160323001
大数据背景下的充电站负荷预测方法
Load Forecasting Method for Electric Vehicle Charging Station Based on Big Data
摘要
Abstract
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.关键词
负荷预测/充电站/大数据/窗口滚动Key words
load forecasting/charging station/big data/rolling window引用本文复制引用
黄小庆,陈颉,陈永新,杨夯,曹一家,江磊..大数据背景下的充电站负荷预测方法[J].电力系统自动化,2016,40(12):68-74,7.基金项目
国家自然科学基金资助项目(61104090) （61104090）
国家科技支撑计划资助项目(2013BAA01B01)。@@@@This work is supported by National Natural Science Foundation of China(No.61104090)and National Key Technologies R&D Program(No.2013BAA01B01) （2013BAA01B01）
