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2013 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) (2013)
Delft
May 13, 2013 to May 16, 2013
ISBN: 978-1-4673-6465-2
pp: 538-545
R. Tolosana-Calasanz , Aragon Inst. of Eng. Res. (I3A), Univ. of Zaragoza, Zaragoza, Spain
J. A. Banares , Aragon Inst. of Eng. Res. (I3A), Univ. of Zaragoza, Zaragoza, Spain
L. Cipcigan , Sch. of Eng., Cardiff Univ., Cardiff, UK
O. Rana , Sch. of Comput. Sci. & Inf., Cardiff Univ., Cardiff, UK
P. Papadopoulos , R&D Centre, EDF Energy, London, UK
Congduc Pham , LIUPPA Lab., Univ. of Pau, Pau, France
ABSTRACT
With an increasing interest in Electric Vehicles (EVs), it is essential to understand how EV charging could impact demand on the Electricity Grid. Existing approaches used to achieve this make use of a centralised data collection mechanism - which often is agnostic of demand variation in a given geographical area. We present an in-transit data processing architecture that is more efficient and can aggregate a variety of different types of data. A model using Reference nets has been developed and evaluated. Our focus in this paper is primarily to introduce requirements for such an architecture.
INDEX TERMS
Computer architecture, Vehicles, Batteries, Demand forecasting, Quality of service, Meteorology, Companies
CITATION

R. Tolosana-Calasanz, J. A. Banares, L. Cipcigan, O. Rana, P. Papadopoulos and Congduc Pham, "A Distributed In-Transit Processing Infrastructure for Forecasting Electric Vehicle Charging Demand," 2013 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)(CCGRID), Delft, 2013, pp. 538-545.
doi:10.1109/CCGrid.2013.103
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