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Where Will You Park? Predicting Vehicle Locations for Vehicle-to-Grid.

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Title: Where Will You Park? Predicting Vehicle Locations for Vehicle-to-Grid.
Authors: Shipman, Rob1 (AUTHOR) julie.waldron@nottingham.ac.uk, Waldron, Julie1 (AUTHOR), Naylor, Sophie1 (AUTHOR), Pinchin, James1 (AUTHOR), Rodrigues, Lucelia1 (AUTHOR), Gillott, Mark1 (AUTHOR)
Source: Energies (19961073). Apr2020, Vol. 13 Issue 8, p1933. 1p. 3 Charts, 9 Graphs.
Subject Terms: *Machine learning, *Electric vehicle charging stations, *Learning ability, *Vehicles
Abstract: Vehicle-to-grid services draw power or curtail demand from electric vehicles when they are connected to a compatible charging station. In this paper, we investigated automated machine learning for predicting when vehicles are likely to make such a connection. Using historical data collected from a vehicle tracking service, we assessed the technique's ability to learn and predict when a fleet of 48 vehicles was parked close to charging stations and compared this with two moving average techniques. We found the ability of all three approaches to predict when individual vehicles could potentially connect to charging stations to be comparable, resulting in the same set of 30 vehicles identified as good candidates to participate in a vehicle-to-grid service. We concluded that this was due to the relatively small feature set and that machine learning techniques were likely to outperform averaging techniques for more complex feature sets. We also explored the ability of the approaches to predict total vehicle availability and found that automated machine learning achieved the best performance with an accuracy of 91.4%. Such technology would be of value to vehicle-to-grid aggregation services. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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DbLabel: Energy & Power Source
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PubType: Academic Journal
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  Data: Where Will You Park? Predicting Vehicle Locations for Vehicle-to-Grid.
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Apr2020, Vol. 13 Issue 8, p1933. 1p. 3 Charts, 9 Graphs.
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  Data: *<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+vehicle+charging+stations%22">Electric vehicle charging stations</searchLink><br />*<searchLink fieldCode="DE" term="%22Learning+ability%22">Learning ability</searchLink><br />*<searchLink fieldCode="DE" term="%22Vehicles%22">Vehicles</searchLink>
– Name: Abstract
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  Data: Vehicle-to-grid services draw power or curtail demand from electric vehicles when they are connected to a compatible charging station. In this paper, we investigated automated machine learning for predicting when vehicles are likely to make such a connection. Using historical data collected from a vehicle tracking service, we assessed the technique's ability to learn and predict when a fleet of 48 vehicles was parked close to charging stations and compared this with two moving average techniques. We found the ability of all three approaches to predict when individual vehicles could potentially connect to charging stations to be comparable, resulting in the same set of 30 vehicles identified as good candidates to participate in a vehicle-to-grid service. We concluded that this was due to the relatively small feature set and that machine learning techniques were likely to outperform averaging techniques for more complex feature sets. We also explored the ability of the approaches to predict total vehicle availability and found that automated machine learning achieved the best performance with an accuracy of 91.4%. Such technology would be of value to vehicle-to-grid aggregation services. [ABSTRACT FROM AUTHOR]
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        Value: 10.3390/en13081933
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        Text: English
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      – SubjectFull: Learning ability
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              Text: Apr2020
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