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dc.contributor.authorPanahi, D.
dc.contributor.authorDeilami, Sara
dc.contributor.authorMasoum, Mohammad A.S.
dc.contributor.authorIslam, Syed
dc.date.accessioned2017-01-30T13:03:47Z
dc.date.available2017-01-30T13:03:47Z
dc.date.created2016-04-27T19:30:16Z
dc.date.issued2015
dc.identifier.citationPanahi, D. and Deilami, S. and Masoum, M. and Islam, S. 2015. Forecasting plug-in electric vehicles load profile using artificial neural networks, in Proceedings of the Australasian Universities Power Engineering Conference (AUPEC), Sep 27-30 2015. Wollongong, NSW: IEEE.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/28225
dc.identifier.doi10.1109/AUPEC.2015.7324879
dc.description.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.

dc.titleForecasting plug-in electric vehicles load profile using artificial neural networks
dc.typeConference Paper
dcterms.source.title2015 Australasian Universities Power Engineering Conference: Challenges for Future Grids, AUPEC 2015
dcterms.source.series2015 Australasian Universities Power Engineering Conference: Challenges for Future Grids, AUPEC 2015
dcterms.source.isbn9781479987252
curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusFulltext not available


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