Schedulable capacity forecasting for electric vehicles based on big data analysis

作     者：Meiqin MAO Shengliang ZHANG Liuchen CHANG Nikos D.HATZIARGYRIOU 

作者机构：Research Center for Photovoltaic System EngineeringSchool of Electrical Engineering and AutomationHefei University of TechnologyHefei 230009China University of New BrunswickFrederictonNB E3B 5A3Canada National Technical University of Athens15780 AthensGreece 

出 版 物：《Journal of Modern Power Systems and Clean Energy》 (现代电力系统与清洁能源学报（英文）)

年 卷 期：2019年第7卷第6期

页      面：1651-1662页

核心收录：

学科分类：12[管理学] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 08[工学] 0807[工学-动力工程及工程热物理] 082302[工学-交通信息工程及控制] 0823[工学-交通运输工程] 

基　　金：supported by National Natural Science Foundation of China(No.51577047) International Collaboration Project supported by Bureau of Science and Technology,Anhui Province(No.1604b0602015). 

主　　题：Electric vehicle(EV) Schedulable capacity Machine learning Big data Multi-time scale 

摘      要：Fast and accurate forecasting of schedulable capacity of electric vehicles(EVs)plays an important role in enabling the integration of EVs into future smart grids as distributed energy storage systems.Traditional methods are insufficient to deal with large-scale actual schedulable capacity data.This paper proposes forecasting models for schedulable capacity of EVs through the parallel gradient boosting decision tree algorithm and big data analysis for multi-time scales.The time scale of these data analysis comprises the real time of one minute,ultra-short-term of one hour and one-day-ahead scale of 24 hours.The predicted results for different time scales can be used for various ancillary services.The proposed algorithm is validated using operation data of 521 EVs in the field.The results show that compared with other machine learning methods such as the parallel random forest algorithm and parallel k-nearest neighbor algorithm,the proposed algorithm requires less training time with better forecasting accuracy and analytical processing ability in big data environment.