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Charging demand of Plug-in Electric Vehicles: Forecasting travel behavior based on a novel Rough Artificial Neural Network approach

Author
Jahangir, H.; Tayarani, H.; Ahmadian, A.; Golkar, M.; Miret, J.; Tayarani, M.; Gao, H.
Type of activity
Journal article
Journal
Journal of cleaner production
Date of publication
2019-08-20
Volume
229
First page
1029
Last page
1044
DOI
10.1016/j.jclepro.2019.04.345
Repository
http://hdl.handle.net/2117/134657 Open in new window
URL
https://www.sciencedirect.com/science/article/pii/S0959652619314428 Open in new window
Abstract
The market penetration of Plug-in Electric Vehicles (PEVs) is escalating due to their energy saving and environmental benefits. In order to address PEVs impact on the electric networks, the aggregators need to accurately predict the PEV Travel Behavior (PEV-TB) since the addition of a great number of PEVs to the current distribution network poses serious challenges to the power system. Forecasting PEV-TB is critical because of the high degree of uncertainties in drivers’ behavior. Existing stu...
Citation
Jahangir, H. [et al.]. Charging demand of Plug-in Electric Vehicles: Forecasting travel behavior based on a novel Rough Artificial Neural Network approach. "Journal of cleaner production", 20 Agost 2019, vol. 229, p. 1029-1044.
Keywords
Artificial neural network, Plug-in electric vehicle, Rough neuron, Smart charging, Travel behavior
Group of research
PERC-UPC - Power Electronics Research Centre
SEPIC - Power and Control Electronics Systems

Participants

  • Jahangir, Hamidreza  (author)
  • Tayarani, Hanif  (author)
  • Ahmadian, Ali  (author)
  • Golkar, Masoud Aliakbar  (author)
  • Miret Tomas, Jaume  (author)
  • Tayarani, Mohammad  (author)
  • Gao, H. Oliver  (author)