  Inspired from recent insights into the common ground of machine learning,
optimization and decision-making, this paper proposes an easy-to-implement, but
effective procedure to enhance both the quality of renewable energy forecasts
and the competitive edge of renewable energy producers in electricity markets
with a dual-price settlement of imbalances. The quality and economic gains
brought by the proposed procedure essentially stem from the utilization of
valuable predictors (also known as features) in a data-driven newsvendor model
that renders a computationally inexpensive linear program. We illustrate the
proposed procedure and numerically assess its benefits on a realistic case
study that considers the aggregate wind power production in the Danish DK1
bidding zone as the variable to be predicted and traded. Within this context,
our procedure leverages, among others, spatial information in the form of wind
power forecasts issued by transmission system operators (TSO) in surrounding
bidding zones and publicly available in online platforms. We show that our
method is able to improve the quality of the wind power forecast issued by the
Danish TSO by several percentage points (when measured in terms of the mean
absolute or the root mean square error) and to significantly reduce the
balancing costs incurred by the wind power producer.
