Online machine learning of available capacity for vehicle-to-grid services during the coronavirus pandemic

Shipman, Rob; Roberts, Rebecca; Waldron, Julie; Rimmer, Chris; Rodrigues, Lucelia; Gillott, Mark

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.

Citation

Shipman, R., Roberts, R., Waldron, J., Rimmer, C., Rodrigues, L., & Gillott, M. (2021). Online machine learning of available capacity for vehicle-to-grid services during the coronavirus pandemic. Energies, 14(21), Article 7176. https://doi.org/10.3390/en14217176

• Journal Article Type: Article
• Acceptance Date: Oct 26, 2021
• Online Publication Date: Nov 1, 2021
• Publication Date: Nov 1, 2021
• Deposit Date: Nov 3, 2021
• Publicly Available Date: Nov 3, 2021
• Journal: Energies
• Electronic ISSN: 1996-1073
• Publisher: MDPI AG
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 21
• Article Number: 7176
• DOI: https://doi.org/10.3390/en14217176
• Keywords: Energy (miscellaneous); Energy Engineering and Power Technology; Renewable Energy, Sustainability and the Environment; Electrical and Electronic Engineering; Control and Optimization; Engineering (miscellaneous)
• Public URL: https://nottingham-repository.worktribe.com/output/6608171
• Publisher URL: https://www.mdpi.com/1996-1073/14/21/7176