Forecasting plug-in electric vehicles load profile using artificial neural networks

Delshad Panahi; Sara Deilami; Mohammad A. S. Masoum; Syed M. Islam

doi:10.1109/AUPEC.2015.7324879

Forecasting plug-in electric vehicles load profile using artificial neural networks

Delshad Panahi, Sara Deilami, Mohammad A. S. Masoum, Syed M. Islam

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

42 Citations (Scopus)

Abstract

Plug-in electric vehicles (PEVs) are becoming very popular these days and consequently, their load management will be a challenging issue for the network operators in the future. This paper proposes an artificial intelligence approach based on neural networks to forecast daily load profile of individual and fleets of randomly plugged-in PEVs, as well as the upstream distribution transformer loading. An artificial neural network (ANN) model will be developed to forecast daily arrival time (T_a) and daily travel distance (D_tr) of individual PEV using historical data collected for each vehicle in the past two years. The predicted parameters are then will be used to forecast transformer loading with PEV charging activities. The results of this paper will be very beneficial to coordination and charge/discharge management of PEVs as well as demand load management, network planning and operation proposes. Detailed simulations are presented to investigate the feasibility and accuracy of the proposed forecasting strategy.

Original language	English
Title of host publication	2015 Australasian Universities Power Engineering Conference (AUPEC)
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1-6
Number of pages	6
ISBN (Electronic)	9781479987252, 9781479987245
DOIs	https://doi.org/10.1109/AUPEC.2015.7324879
Publication status	Published - 2015
Externally published	Yes
Event	25th Australasian Universities Power Engineering Conference, AUPEC 2015 - Wollongong, Australia Duration: 27 Sept 2015 → 30 Sept 2015

Other

Other	25th Australasian Universities Power Engineering Conference, AUPEC 2015
Country/Territory	Australia
City	Wollongong
Period	27/09/15 → 30/09/15

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10.1109/AUPEC.2015.7324879

Cite this

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title = "Forecasting plug-in electric vehicles load profile using artificial neural networks",

abstract = "Plug-in electric vehicles (PEVs) are becoming very popular these days and consequently, their load management will be a challenging issue for the network operators in the future. This paper proposes an artificial intelligence approach based on neural networks to forecast daily load profile of individual and fleets of randomly plugged-in PEVs, as well as the upstream distribution transformer loading. An artificial neural network (ANN) model will be developed to forecast daily arrival time (Ta) and daily travel distance (Dtr) of individual PEV using historical data collected for each vehicle in the past two years. The predicted parameters are then will be used to forecast transformer loading with PEV charging activities. The results of this paper will be very beneficial to coordination and charge/discharge management of PEVs as well as demand load management, network planning and operation proposes. Detailed simulations are presented to investigate the feasibility and accuracy of the proposed forecasting strategy.",

author = "Delshad Panahi and Sara Deilami and Masoum, {Mohammad A. S.} and Islam, {Syed M.}",

year = "2015",

doi = "10.1109/AUPEC.2015.7324879",

language = "English",

pages = "1--6",

booktitle = "2015 Australasian Universities Power Engineering Conference (AUPEC)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "25th Australasian Universities Power Engineering Conference, AUPEC 2015 ; Conference date: 27-09-2015 Through 30-09-2015",

}

Panahi, D, Deilami, S, Masoum, MAS & Islam, SM 2015, Forecasting plug-in electric vehicles load profile using artificial neural networks. in 2015 Australasian Universities Power Engineering Conference (AUPEC). Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 1-6, 25th Australasian Universities Power Engineering Conference, AUPEC 2015, Wollongong, Australia, 27/09/15. https://doi.org/10.1109/AUPEC.2015.7324879

Forecasting plug-in electric vehicles load profile using artificial neural networks. / Panahi, Delshad; Deilami, Sara; Masoum, Mohammad A. S. et al.
2015 Australasian Universities Power Engineering Conference (AUPEC). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2015. p. 1-6.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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AU - Panahi, Delshad

AU - Deilami, Sara

AU - Masoum, Mohammad A. S.

AU - Islam, Syed M.

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AB - Plug-in electric vehicles (PEVs) are becoming very popular these days and consequently, their load management will be a challenging issue for the network operators in the future. This paper proposes an artificial intelligence approach based on neural networks to forecast daily load profile of individual and fleets of randomly plugged-in PEVs, as well as the upstream distribution transformer loading. An artificial neural network (ANN) model will be developed to forecast daily arrival time (Ta) and daily travel distance (Dtr) of individual PEV using historical data collected for each vehicle in the past two years. The predicted parameters are then will be used to forecast transformer loading with PEV charging activities. The results of this paper will be very beneficial to coordination and charge/discharge management of PEVs as well as demand load management, network planning and operation proposes. Detailed simulations are presented to investigate the feasibility and accuracy of the proposed forecasting strategy.

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Forecasting plug-in electric vehicles load profile using artificial neural networks

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