Identification of Charging Behavior for Electric Bicycles based on Supervised Fisher Classifier-Reference-Cited by-同舟云学术

Identification of Charging Behavior for Electric Bicycles based on Supervised Fisher Classifier

Published:2021-12-23 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE Sustainable Power and Energy Conference (iSPEC)
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Author:

Zhou Yu¹,Tang Yi²,Zhang Xuecen³,Mu Zhuowen¹,Gao Fan¹,Yi Yongxian¹,Li Yue¹

Affiliation:

1. State Grid Jiangsu Electric Power Co., LTD. State Grid Corporation of China,Nanjing,China

2. Southeast University,School of Electrical Engineering,Nanjing,China

3. Southeast University,School of Cyber Science and Engineering,Nanjing,China

Funder

State Grid Jiangsu Electric Power

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9735429/9735430/09736023.pdf?arnumber=9736023

Reference13 articles.

1. Load identification of household appliances based on instantaneous load characteristics;qu;Trans China Electrotechnical Society,2015

2. Nonintrusive load event detection method based on state feature clustering;zhou;Transactions of China Electrotechnical Society,2020

3. Analysis and research on electric bicycle charging behavior based on data drive;xu;Zhejiang University Science,2020

4. Non-intrusive charging identification method for electric bicycles based on feature selection and incremental learning;shi;Automation of Electric Power Systems,2021

5. Feature extraction based hellinger distance algorithm for non-intrusive aging load identification in residential buildings

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