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A Distributed In-Transit Processing Infrastructure for Forecasting Electric Vehicle Charging Demand

Tolosana-Calasanz, Rafael, Banares, Jose Angel, Cipcigan, Liana Mirela, Rana, Omer Farooq, Papadopoulos, Panagiotis and Pham, Congduc 2013. A Distributed In-Transit Processing Infrastructure for Forecasting Electric Vehicle Charging Demand. Presented at: 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing (CCGrid), Delft, Netherlands, 13-16 May 2013. Published in: Balaji, P., Epema, D. and Fahringer, T. eds. Proceedings of the 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing (CCGrid). Los Alamitos, CA: IEEE, pp. 538-545. 10.1109/CCGrid.2013.103

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Abstract

With an increasing interest in Electric Vehicles (EVs), it is essential to understand howEV 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.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Engineering
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Uncontrolled Keywords: Distributed Data Stream Processing; Electric Vehicle Demand Forecasting
Publisher: IEEE
ISBN: 9781467364652
Last Modified: 04 Jun 2017 05:08
URI: http://orca.cf.ac.uk/id/eprint/48904

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