Paper Info
Title
A Distributed In-Transit Processing Infrastructure for Forecasting Electric Vehicle Charging Demand
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.
Year
DOI
Venue
2013
10.1109/CCGrid.2013.103
Cluster, Cloud and Grid Computing
Keywords
Field
DocType
battery powered vehicles,data handling,distributed processing,load forecasting,power engineering computing,power grids,EV,centralised data collection mechanism,demand variation agnostic,distributed in-transit processing infrastructure,electricity grid,forecasting electric vehicle charging demand,geographical area,reference nets,Distributed Data Stream Processing,Electric Vehicle Demand Forecasting
Data collection,Data processing,Electricity grid,Architecture,Computer science,Electric vehicle,Load forecasting,Data type,Group method of data handling,Embedded system,Distributed computing
Conference
ISBN
Citations 
PageRank 
978-1-4673-6465-2
1
0.37
References 
Authors
0
7
Name
Order
Citations
PageRank
Rafael Tolosana-Calasanz116618.52
J. A. Bañares2878.80
Liana Cipcigan383.57
Omer F. Rana42181229.52
Panagiotis Papadopoulos5308.42
CongDuc Pham623233.56
Tolosana-Calasanz, R.710.37