**4. Conclusions**

In this work, we compared the use of automated machine learning and moving averages to predict the parked locations of vehicles from a University fleet and their proximity to six proposed sites of V2G charging stations. This allowed the potential availability of vehicles during future half-hour trading periods to be assessed. Prediction errors for individual vehicles were found to be very similar for the simplest averaging techniques and the most complex machine learning techniques. However, this was only enabled using a heuristic for the averaging approaches to adjust for the impact of a key feature in the dataset. This impact was learned without intervention by the AutoML approach, a capability that is of critical importance as the feature set grows and interacts non-linearly making the use of heuristics untenable. Two approaches for using the predictions for individual vehicles to predict the total number of available vehicles were also investigated. It was found that calculating the cumulative probability was more powerful than summing individual vehicle predictions and that AutoML was the most accurate using this approach with an accuracy of 91.4% on the test dataset. While this predictive capability would be of value to a V2G aggregation service, translating available vehicles to available capacity requires the incorporation of other factors, including the state of charge of the battery, which will be a focus of future work.

**Author Contributions:** Conceptualization, R.S., S.N. and J.P.; data curation, R.S., J.W. and L.R.; formal analysis, R.S.; funding acquisition, R.S. and M.G.; investigation, R.S.; methodology, R.S.; software, R.S.; validation, R.S.; writing—original draft, R.S.; writing—review and editing, J.W., S.N. and J.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research and the APC were funded by the European Space Agency, grant number 4000120818/17/NL/US.

**Acknowledgments:** This work is part of a collaborative project with our partners Kearney, Brixworth Technologies and Cenex—the centre of excellence for low carbon and fuel cell technologies.

**Conflicts of Interest:** The authors declare no conflict of interest.
