*3.4. Discussion*

The learning approaches explored in this work ranged from the simplest averaging techniques to complex machine learning models. However, their performance on the defined task was comparable. This relative equality could be explained by examining the nature of the dataset and the potential patterns of vehicle behaviour that the machine learning approaches had the potential to learn. A University tends to work on annual patterns as it moves through the various terms and holidays. However, as the training set was exclusively drawn from a single year, any annual patterns could not be learned by the machine learning models. This left two other key features that could potentially be utilised, term and holiday. The CMA and EMA averaging techniques did not consider the term, and yet their performance was equivalent during both periods, and thus this feature had little impact on overall vehicle behaviour. In contrast, the holiday feature did impact vehicle behaviour, which was successfully learned by the AutoML model, resulting in improved performance over the averaging techniques. However, the impact was clear and consistent, and, therefore, a simple heuristic was sufficient to compensate for it within the CMA and EMA models.

There was little scope, therefore, for the machine learning approaches to improve over the simple averaging techniques. This, however, would not always be the case. There are many other features that have the potential to impact vehicle behaviour. For the University fleet, these include University open days, special events, weather events and local traffic conditions. Creation of a successful predictive model for vehicle availability is thus not likely to be a one-off event but rather an iterative process where initially available data is used to produce a first model iteration that is retrained and updated as new data becomes available and its performance analysed. For example, observation of periods of significant deviation between actual and predicted availability may allow the identification of events that need to be accommodated within the model. For the examples above, new features may be added to the dataset to identify open days and special events, allowing any associated impact on vehicle behaviour to be learned. Links to live weather and traffic services may also be established so that the impact of various conditions can be accommodated in the data and influence the predictions that are made. As the complexity of the feature set grows and these features interact non-linearly, the impact of individual features will be less easily identifiable, and, therefore, attempting to accommodate them through use of heuristics in the averaging approaches will quickly become untenable. Machine learning approaches can more easily accommodate such complexity and are, therefore, likely to outperform the averaging approaches as a V2G service develops. However, this will require defining the features that have an impact on vehicle predictability and discovering where the relevant data can be found (e.g., labelling of workplace-specific holidays may require parsing events from work calendars, manual input from fleet owners, etc.).

The need to continually iterate and refine the models is also required to enable adaptation to changes in vehicle behaviour. Although the behaviour of the fleet considered in this work was relatively regular, changes would occur over time in response to changes in the way the broader organisation operates, for example. Such changes in schedule would also be apparent for non-fleet users, where they might be more pronounced given that there is likely to be greater flexibility in drivers' schedules. The EMA model used in this work weighted recent data more strongly than historical data to help adapt to changes. However, online or continual learning would also be required for machine learning models to adapt to such concept drift [27].

Analysis of individual vehicles allowed the identification of candidate vehicles for V2G. A "sweet spot" of vehicles was identified that satisfied several enabling requirements: (a) they were available, i.e., parked next to a charge point for a significant amount of time; (b) they were predictable, i.e., errors were low; (c) average daily mileage requirements were relatively low, thus providing spare capacity; (d) they were stationary for at least one extended period, thus allowing the battery to be replenished. Such analysis is of value to a fleet that is considering moving to electric vehicles and the use of V2G services by supporting the prioritisation of vehicles to transition and informing the required capacity of batteries, for example. It is also of importance to assess the number of charge points that are required in each proposed location; even if parked locations can be reliably predicted, this is of little value if all the vehicles cannot find a compatible grid connection. Knowledge of individual vehicles is also important during the operation of a V2G service. It may not be possible to assume the use of a vehicle even if it is plugged in and available as it may be necessary for individuals to receive and accept offers to participate in a given V2G opportunity [28], an issue that may be particularly pertinent for non-fleet users. For non-homogeneous populations of vehicles and batteries, it may also be necessary to target users based on the specific capabilities of their vehicles, such as battery capacity. Such socio-technical considerations have not been widely considered in work to date [29], and more research is required.

In many cases, it will be more beneficial to consider the population of available vehicles rather than individual vehicles. The analysis conducted in this paper showed that considering the cumulative likelihood of vehicle availability was more accurate than making predictions for each vehicle individually, which was especially the case for AutoML. To participate in grid services, the most important thing an aggregator needs to predict is the total capacity available to it at a given time, and the specific vehicles contributing to that capacity may be of lesser concern. However, there are a number of other factors that must be considered when translating vehicle availability to actual available capacity. Chief among these is the battery state of charge, which must be sufficient to enable V2G services while allowing a vehicle to continue operating in its primary role as a form of transport. In this work, the average daily mileage was calculated, which allowed likely available surplus capacity to be assessed for given battery capacity. Such high-level analysis may broadly enable a V2G service; however, more detailed analysis of the historical state of charge and incorporation of such data into the learning algorithm would be of great value to optimise the service. This is particularly true for vehicle populations with larger or less consistent daily mileage, where the explicit state of charge data may be essential to calculating whether a vehicle can participate in a V2G event while retaining enough charge for its next journey. As V2G services develop, such data will be generated as vehicles plug into compatible charge points, which can be used to further refine the models and enable finer-grained capacity predictions.
