Using Smart Meter Data and Machine Learning to Identify Residential Light-duty Electric Vehicles

被引：0

作者：

Lin, Alec Zhixiao ^{[1
]}

James, Anthony ^{[2
]}

机构：

[1] Southern Calif Edison, Next Gen Data Engn & Adv Analyt, Rosemead, CA 91770 USA

[2] Southern Calif Edison, Asset Strategy & Planning, Adv Technol Labs, Westminster, CA USA

来源：

2022 IEEE CONFERENCE ON TECHNOLOGIES FOR SUSTAINABILITY (SUSTECH) | 2022年

关键词：

Electric Vehicles; Home Charging; Machine Learning; Feature Generation; Ensemble Method;

D O I：

10.1109/SusTech53338.2022.9794221

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

The growing adoption of electric vehicles (EVs) poses new challenges to power grids. To upgrade the grids with the increasing demand from charging EVs and from the change in customers consumption behaviors, utilities need to know where EV customers are. However, ownerships of EVs are not always known to utilities. This paper presents a methodology on how to use advanced metering infrastructure (AMI) data and apply machine learning to identify residential customers with EVs. It focuses on such aspects as how to perform sampling to reduce effects of external factors associated with other high-usage home appliances, how to create and evaluate variables for enhancing modeling, and how to apply the ensemble method to arrive at the estimation or forecasting needed for grid enhancement.

引用

页码：245 / 251

页数：7

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