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

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...

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Autores principales:	Rob Shipman, Rebecca Roberts, Julie Waldron, Chris Rimmer, Lucelia Rodrigues, Mark Gillott
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	V2G vehicle-to-grid deep learning machine learning online machine learning coronavirus Technology T
Acceso en línea:	https://doaj.org/article/6040bb08594d4455ae4be6d1c9ee848b
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id	oai:doaj.org-article:6040bb08594d4455ae4be6d1c9ee848b
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spelling	oai:doaj.org-article:6040bb08594d4455ae4be6d1c9ee848b2021-11-11T15:57:10ZOnline Machine Learning of Available Capacity for Vehicle-to-Grid Services during the Coronavirus Pandemic10.3390/en142171761996-1073https://doaj.org/article/6040bb08594d4455ae4be6d1c9ee848b2021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/7176https://doaj.org/toc/1996-1073Vehicle-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.Rob ShipmanRebecca RobertsJulie WaldronChris RimmerLucelia RodriguesMark GillottMDPI AGarticleV2Gvehicle-to-griddeep learningmachine learningonline machine learningcoronavirusTechnologyTENEnergies, Vol 14, Iss 7176, p 7176 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	V2G vehicle-to-grid deep learning machine learning online machine learning coronavirus Technology T
spellingShingle	V2G vehicle-to-grid deep learning machine learning online machine learning coronavirus Technology T Rob Shipman Rebecca Roberts Julie Waldron Chris Rimmer Lucelia Rodrigues Mark Gillott Online Machine Learning of Available Capacity for Vehicle-to-Grid Services during the Coronavirus Pandemic
description	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.
format	article
author	Rob Shipman Rebecca Roberts Julie Waldron Chris Rimmer Lucelia Rodrigues Mark Gillott
author_facet	Rob Shipman Rebecca Roberts Julie Waldron Chris Rimmer Lucelia Rodrigues Mark Gillott
author_sort	Rob Shipman
title	Online Machine Learning of Available Capacity for Vehicle-to-Grid Services during the Coronavirus Pandemic
title_short	Online Machine Learning of Available Capacity for Vehicle-to-Grid Services during the Coronavirus Pandemic
title_full	Online Machine Learning of Available Capacity for Vehicle-to-Grid Services during the Coronavirus Pandemic
title_fullStr	Online Machine Learning of Available Capacity for Vehicle-to-Grid Services during the Coronavirus Pandemic
title_full_unstemmed	Online Machine Learning of Available Capacity for Vehicle-to-Grid Services during the Coronavirus Pandemic
title_sort	online machine learning of available capacity for vehicle-to-grid services during the coronavirus pandemic
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/6040bb08594d4455ae4be6d1c9ee848b
work_keys_str_mv	AT robshipman onlinemachinelearningofavailablecapacityforvehicletogridservicesduringthecoronaviruspandemic AT rebeccaroberts onlinemachinelearningofavailablecapacityforvehicletogridservicesduringthecoronaviruspandemic AT juliewaldron onlinemachinelearningofavailablecapacityforvehicletogridservicesduringthecoronaviruspandemic AT chrisrimmer onlinemachinelearningofavailablecapacityforvehicletogridservicesduringthecoronaviruspandemic AT luceliarodrigues onlinemachinelearningofavailablecapacityforvehicletogridservicesduringthecoronaviruspandemic AT markgillott onlinemachinelearningofavailablecapacityforvehicletogridservicesduringthecoronaviruspandemic
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Online Machine Learning of Available Capacity for Vehicle-to-Grid Services during the Coronavirus Pandemic

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