**2. Materials and Methods**

In this work, we used 42 weeks of historical data from a fleet of 48 vehicles belonging to the University of Nottingham that was collected using the Trakm8 telematics service [16] deployed in those vehicles. We investigated the use of automated machine learning [17] (AutoML) that has the potential to broaden the use of machine learning within the energy domain by automating the time-consuming workflow and allowing the rapid exploration of a range of industry-standard algorithms. This technique was compared with two averaging techniques: a simple cumulative moving average (CMA) and an exponential moving average (EMA) that weights recent data more strongly. We assessed the ability of the three approaches to predict the availability of individual vehicles and the total available vehicles in future half-hour periods, i.e., potential trading windows.
