Machine Learning Based PEVs Load Extraction and Analysis

Transformation of the energy sector due to the appearance of plug-in electric vehicles (PEVs) has faced the researchers with challenges in recent years. The foremost challenge is uncertain behavior of a PEV that hinders operators determining a deterministic load profile. Load forecasting of PEVs is...

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Main Authors: Amin Mansour-Saatloo, Arash Moradzadeh, Behnam Mohammadi-Ivatloo, Ali Ahmadian, Ali Elkamel
Format: Article
Language:English
Published: MDPI AG 2020-07-01
Series:Electronics
Subjects:
Online Access:https://www.mdpi.com/2079-9292/9/7/1150
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spelling doaj-3daeedc3f41d49cb8609b48300aeb5132020-11-25T03:01:34ZengMDPI AGElectronics2079-92922020-07-0191150115010.3390/electronics9071150Machine Learning Based PEVs Load Extraction and AnalysisAmin Mansour-Saatloo0Arash Moradzadeh1Behnam Mohammadi-Ivatloo2Ali Ahmadian3Ali Elkamel4Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5166616471, IranFaculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5166616471, IranFaculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5166616471, IranDepartment of Electrical Engineering, University of Bonab, Bonab 5551761167, IranCollege of Engineering, University of Waterloo, Waterloo, ON N2L 3G1, CanadaTransformation of the energy sector due to the appearance of plug-in electric vehicles (PEVs) has faced the researchers with challenges in recent years. The foremost challenge is uncertain behavior of a PEV that hinders operators determining a deterministic load profile. Load forecasting of PEVs is so crucial in both operating and planning of the energy systems. PEV load demand mainly depends on traveling behavior of them. This paper tries to present an accurate model to forecast PEVs’ traveling behavior in order to extract the PEV load profile. The presented model is based on machine-learning techniques; namely, a generalized regression neural network (GRNN) that correlates between PEVs’ arrival/departure times and traveling behavior is considered in the model. The results show the ability of the GRNN to communicate between arrival/departure times of PEVs and the distance traveled by them with a correlation coefficient (R) of 99.49% for training and 98.99% for tests. Therefore, the trained and saved GRNN model is ready to forecast PEVs’ trip length based on training and testing with historical data. Finally, the results indicate the importance of implementing more accurate methods to predict PEVs to gain the significant advantages in the importance of electrical energy in vehicles in the years to come.https://www.mdpi.com/2079-9292/9/7/1150plug-in electric vehicle (PEV)load forecastingmachine learninggeneralized regression neural network (GRNN)
collection DOAJ
language English
format Article
sources DOAJ
author Amin Mansour-Saatloo
Arash Moradzadeh
Behnam Mohammadi-Ivatloo
Ali Ahmadian
Ali Elkamel
spellingShingle Amin Mansour-Saatloo
Arash Moradzadeh
Behnam Mohammadi-Ivatloo
Ali Ahmadian
Ali Elkamel
Machine Learning Based PEVs Load Extraction and Analysis
Electronics
plug-in electric vehicle (PEV)
load forecasting
machine learning
generalized regression neural network (GRNN)
author_facet Amin Mansour-Saatloo
Arash Moradzadeh
Behnam Mohammadi-Ivatloo
Ali Ahmadian
Ali Elkamel
author_sort Amin Mansour-Saatloo
title Machine Learning Based PEVs Load Extraction and Analysis
title_short Machine Learning Based PEVs Load Extraction and Analysis
title_full Machine Learning Based PEVs Load Extraction and Analysis
title_fullStr Machine Learning Based PEVs Load Extraction and Analysis
title_full_unstemmed Machine Learning Based PEVs Load Extraction and Analysis
title_sort machine learning based pevs load extraction and analysis
publisher MDPI AG
series Electronics
issn 2079-9292
publishDate 2020-07-01
description Transformation of the energy sector due to the appearance of plug-in electric vehicles (PEVs) has faced the researchers with challenges in recent years. The foremost challenge is uncertain behavior of a PEV that hinders operators determining a deterministic load profile. Load forecasting of PEVs is so crucial in both operating and planning of the energy systems. PEV load demand mainly depends on traveling behavior of them. This paper tries to present an accurate model to forecast PEVs’ traveling behavior in order to extract the PEV load profile. The presented model is based on machine-learning techniques; namely, a generalized regression neural network (GRNN) that correlates between PEVs’ arrival/departure times and traveling behavior is considered in the model. The results show the ability of the GRNN to communicate between arrival/departure times of PEVs and the distance traveled by them with a correlation coefficient (R) of 99.49% for training and 98.99% for tests. Therefore, the trained and saved GRNN model is ready to forecast PEVs’ trip length based on training and testing with historical data. Finally, the results indicate the importance of implementing more accurate methods to predict PEVs to gain the significant advantages in the importance of electrical energy in vehicles in the years to come.
topic plug-in electric vehicle (PEV)
load forecasting
machine learning
generalized regression neural network (GRNN)
url https://www.mdpi.com/2079-9292/9/7/1150
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AT arashmoradzadeh machinelearningbasedpevsloadextractionandanalysis
AT behnammohammadiivatloo machinelearningbasedpevsloadextractionandanalysis
AT aliahmadian machinelearningbasedpevsloadextractionandanalysis
AT alielkamel machinelearningbasedpevsloadextractionandanalysis
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