  In this article, a multiple split method is proposed that enables
construction of multidimensional probabilistic forecasts of a selected set of
variables. The method uses repeated resampling to estimate uncertainty of
simultaneous multivariate predictions. This nonparametric approach links the
gap between point and probabilistic predictions and can be combined with
different point forecasting methods. The performance of the method is evaluated
with data describing the German short-term electricity market. The results show
that the proposed approach provides highly accurate predictions. The gains from
multidimensional forecasting are the largest when functions of variables, such
as price spread or residual load, are considered.
  Finally, the method is used to support a decision process of a moderate
generation utility that produces electricity from wind energy and sells it on
either a day-ahead or an intraday market. The company makes decisions under
high uncertainty because it knows neither the future production level nor the
prices. We show that joint forecasting of both market prices and fundamentals
can be used to predict the distribution of a profit, and hence helps to design
a strategy that balances a level of income and a trading risk.
