Machine learning to support multi-vector energy provisioning in transport

Rana, Omer F. ORCID: https://orcid.org/0000-0003-3597-2646 and Venkatakrishnan, Venkat 2026. Machine learning to support multi-vector energy provisioning in transport. Sharma, Ashish, Potoglou, Dimitris, Miranda, Fabio, Radcliffe, Jonathan and Anderies, John M., eds. Clean Energy and Equitable Transportation Solutions, IGI Global, pp. 191-200. (10.4018/979-8-3373-9908-9.ch011)

Full text not available from this repository.

Abstract

Monitoring and computational infrastructure can be distributed across different layers of transport systems – from vehicles to data centres that support traffic monitoring and planning. Understanding how this computational infrastructure can be harnessed to determine demand patterns, availability profiles of energy sources and market mechanisms that support the use of such analysis remain important challenges. This approach can take into consideration the availability of data communications networks (with varying capacity and latency) to enable data sharing. The benefit of utilising a machine learning approach deployed across an edge-cloud systems infrastructure is proposed – with benefits and limitations across urban and rural environments. We describe emerging AI technologies that can used in this context, such as real time analysis, use of Large Language Models (LLMs), hardware accelerators for machine learning algorithms that can be hosted in proximity to energy sources.

Item Type:	Book Section
Date Type:	Publication
Status:	Published
Schools:	Schools > Computational & Mathematical Sciences Schools > Computer Science & Informatics
Publisher:	IGI Global
ISBN:	9798337399089
Last Modified:	07 Sep 2026 15:45
URI:	https://orca.cardiff.ac.uk/id/eprint/188965

Actions (repository staff only)

Edit Item