Supervised Non-Intrusive Load Monitoring algorithm for Electric Vehicle Identification

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Abstract

Transport sector electrification represents an increase in the number of electric vehicles (EV), producing significant variations in the distribution network dynamics. As a result, bidirectional power flow, overload and load unbalances are caused at the low voltage level due to unexpected increased load peaks. Non-intrusive load monitoring (NILM) methods have been developed as a strategy for energy management systems, applied to the customer side producing energy savings. This research presents a NILM methodology based on a low complexity conventional supervised machine learning pipeline. Our approach uses Principal Component Analysis (PCA) and Random Forest (RF) to detect the presence of a charging electric vehicle on the electricity network. By processing low sampling rate active power data, this approach provides a simple but feasible method that can be applied to smart meters. This provides useful data analysis for distribution network operators (DNO) to effectively deal with variability caused by these low carbon loads in the distribution grid. Achieving an overall efficacy of 92.68%, the proposed method can be compared with other state of the art methods developed under higher complexity techniques.
Original languageEnglish
Title of host publicationProceedings of the 2020 IEEE International Instrumentation and Measurement Technology Conference
Publisher IEEE
Number of pages6
DOIs
Publication statusPublished - 30 Jun 2020
Event2020 IEEE International Instrumentation and Measurement Technology Conference - Dubrovnik, Croatia
Duration: 25 May 202028 May 2020
Conference number: 2020
https://i2mtc2020.ieee-ims.org/

Publication series

NameProceedings of the IEEE International Instrumentation and Measurement Technology Conference
PublisherIEEE
ISSN (Electronic)2642-2077

Conference

Conference2020 IEEE International Instrumentation and Measurement Technology Conference
Abbreviated titleI2MTC
CountryCroatia
CityDubrovnik
Period25/05/202028/05/2020
Internet address

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