Online Machine Learning of Available Capacity for Vehicle-to-Grid Services during the Coronavirus Pandemic

Rob Shipman, Rebecca Roberts, Julie Waldron, Chris Rimmer, Lucelia Rodrigues and Mark Gillott
Additional contact information
Rob Shipman: Department of Architecture & Built Environment, University of Nottingham, Nottingham NG7 2RD, UK
Rebecca Roberts: A.T. Kearney Limited, 1-11 John Adam Street, London WC2N 6HT, UK
Julie Waldron: Department of Architecture & Built Environment, University of Nottingham, Nottingham NG7 2RD, UK
Chris Rimmer: Cenex, Holywell Building, Holywell Park, Ashby Road, Loughborough LE11 3UZ, UK
Lucelia Rodrigues: Department of Architecture & Built Environment, University of Nottingham, Nottingham NG7 2RD, UK
Mark Gillott: Department of Architecture & Built Environment, University of Nottingham, Nottingham NG7 2RD, UK

Energies, 2021, vol. 14, issue 21, 1-16

Abstract: Vehicle-to-grid services make use of the aggregated capacity available from a fleet of vehicles to participate in energy markets, help integrate renewable energy in the grid and balance energy use. In this paper, the critical components of such a service are described in the context of a commercial service that is currently under development. Key among these components is the prediction of available capacity at a future time. In this paper, we extend a previous work that used a deep learning recurrent neural network for this task to include online machine learning, which enables the network to continually refine its predictions based on observed behaviour. The coronavirus pandemic that was declared in 2020 resulted in closures of the university and substantial changes to the behaviour of the university fleet. In this work, the impact of this change in vehicles usage was used to test the predictions of a network initially trained using vehicle trip data from 2019 with and without online machine learning. It is shown that prediction error is significantly reduced using online machine learning, and it is concluded that a similar capability will be of critical importance for a commercial service such as the one described in this paper.

Keywords: V2G; vehicle-to-grid; deep learning; machine learning; online machine learning; coronavirus (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/21/7176/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/21/7176/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:21:p:7176-:d:670270

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().