Electric Vehicle Identification in Low-Sampling Non-Intrusive Load Monitoring Systems Using Machine Learning-Reference-Cited by-同舟云学术

Electric Vehicle Identification in Low-Sampling Non-Intrusive Load Monitoring Systems Using Machine Learning

Published:2023-09-24 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Smart Cities Conference (ISC2)
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Author:

Khaleghian Seyedmehdi¹,Tran Toan¹,Cho Jin²,Harris Austin¹,Sartipi Mina¹

Affiliation:

1. University of Tennessee at Chattanooga,Tennessee,USA

2. Yale University,New Haven,Connecticut,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10293179/10293210/10293461.pdf?arnumber=10293461

Reference17 articles.

1. Training-free nonintrusive load monitoring of electric vehicle charging with low sampling rate;zhang;IECON 2014 – 40th Annual Conference of the IEEE Industrial Electronics Society,0

2. Nonintrusive load monitoring (NILM) performance evaluation

3. Feature-based clustering for electricity use time series data;räsänen;International Conference on Adaptive and Natural Computing Algorithms,0

4. Characteristic-Based Clustering for Time Series Data

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