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Realising the potential of rich energy datasets

Ellis, Robert Joseph (2017)
Ph.D. thesis, University of Birmingham.

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Abstract

In the last twenty years the availability of vast amounts of data has enabled industries to gain insight into numerous aspects of their operation whose trends were previously unknown. The result is an unprecedented ability to predict operational needs, to evaluate performance of individuals or assets and prepare such industries for uncertainties. The rail industry currently produces large amounts of data that are, in many cases, not used to their full potential.
The first case study demonstrates a novel method to identify and cluster distinct driver styles in use on a DC rail network. Using the optimal driver styles identified, improved ‘driver cultures’ were designed that are shown to provide up to 10% energy savings without the need for expensive in cab driver advisory systems.
The second case study details data taken from a full fleet that were used to develop a statistical method to identify the minimum amount of vehicles that required energy metering whilst still providing an accurate mean energy consumption estimate. The identification of this minimum amount was then used to validate the fleet size intended for partial fleet metering options for UK rail networks.

Type of Work:Ph.D. thesis.
Supervisor(s):Hillmansen, Stuart and Tricoli, Pietro
School/Faculty:Colleges (2008 onwards) > College of Engineering & Physical Sciences
Department:School of Electronic, Electrical and Systems Engineering
Additional Information:

Publications arising from thesis:

Observations of train control performance on a camshaft-operated DC electrical multiple unit. Robert Ellis, Paul Weston, Edward Stewart, Stuart Hillmansen, Pietro Tricoli, Clive Roberts, Ian Jones. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit. Vol 230, Issue 4, pp. 1184 - 1201.
http://dx.doi.org/10.1177/0954409715589618


Subjects:QA75 Electronic computers. Computer science
QA76 Computer software
TF Railroad engineering and operation
Institution:University of Birmingham
ID Code:7461
This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder.
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