Forecasting the EV charging load based on customer profile or station measurement?

被引：112

作者：

Majidpour, Mostafa ^{[1
]}

Qiu, Charlie ^{[1
]}

Chu, Peter ^{[1
]}

Pota, Hemanshu R. ^{[2
]}

Gadh, Rajit ^{[1
]}

机构：

[1] Univ Calif Los Angeles, Smart Grid Energy Res Ctr, Los Angeles, CA USA

[2] Univ NSW, Sch Engn & Informat Technol, Canberra, ACT 2610, Australia

来源：

APPLIED ENERGY | 2016年 / 163卷

关键词：

Electric Vehicle; Privacy; Time series; Load forecasting; SMART; AGGREGATION; TUTORIAL;

D O I：

10.1016/j.apenergy.2015.10.184

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

In this paper, forecasting of the Electric Vehicle (EV) charging load has been based on two different data sets: data from the customer profile (referred to as charging record) and data from outlet measurements (referred to as station record). Four different prediction algorithms namely Time Weighted Dot Product based Nearest Neighbor (TWDP-NN), Modified Pattern Sequence Forecasting (MPSF), Support Vector Regression (SVR), and Random Forest (RF) are applied to both datasets. The corresponding speed, accuracy, and privacy concerns are compared between the use of the charging records and station records. Real world data compiled at the outlet level from the UCLA campus parking lots are used. The results show that charging records provide relatively faster prediction while putting customer privacy in jeopardy. Station records provide relatively slower prediction while respecting the customer privacy. In general, we found that both datasets generate comparable prediction error. (C) 2015 Elsevier Ltd. All rights reserved.

引用

页码：134 / 141

页数：8

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