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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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 |
work_keys_str_mv |
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