  The issue of the accuracy of wind speed/power forecasts is becoming more and
more important as wind power production continues to increase year after year.
Having accurate forecasts for the energy market clashes with intrinsic
difficulties of wind forecasts due to, e.g., the coarse resolution of Numerical
Weather Prediction models. Here, we propose a novel Ensemble Model Output
Statistics (EMOS) which accounts for nonlinear relationships between
predictands and both predictors and other weather observables used as
conditioning variables. The strategy is computationally cheap and
easy-to-implement with respect to other more complex strategies dealing with
nonlinear regressions. Our novel strategy is assessed in a systematic way to
quantify its added value with respect to ordinary, linear, EMOS strategies.
Wind speed/power forecasts over Italy from the Ensemble Prediction System (EPS)
in use at the European Centre for Medium-Range Weather Forecasts (ECMWF) are
considered for this purpose. The calibrations are based on the use of past wind
speed measurements collected by 69 SYNOP stations over Italy in the years 2018
and 2019. Our results show the key role played by conditioning variables to
disentangle the model error thus allowing a net improvement of the calibration
with respect to ordinary EMOS strategies. Finally, we have quantified the
impact of calibrated wind forecasts on the wind power forecasts finding results
of interest for the renewable energy market.
