  Electric vehicle (EV) charge points (CPs) can be used by aggregators to
provide frequency response (FR) services. Aggregators must have day-ahead
half-hourly forecasts of minimum aggregate vehicle-to-grid (V2G) plug-in to
produce meaningful bids for the day-ahead ancillary services market. However,
there is a lack of understanding on what features should be considered and how
complex the forecasting model should be. This paper explores the dependency of
aggregate V2G plug-in on historic plug-in levels, calendar variables, and
weather conditions. These investigations are used to develop three day-ahead
forecasts of minimum aggregate V2G plug-in during 30-minute window. A neural
network that considers previous V2G plug-in values the day before, three days
before, and seven days before, in addition to day of the week, month, and hour,
is found to be the most accurate.
