  By the end of 2021, the renewable energy share of the global electricity
capacity reached 38.3% and the new installations are dominated by wind and
solar energy, showing global increases of 12.7% and 18.5%, respectively.
However, both wind and photovoltaic energy sources are highly volatile making
planning difficult for grid operators, so accurate forecasts of the
corresponding weather variables are essential for reliable electricity
predictions. The most advanced approach in weather prediction is the ensemble
method, which opens the door for probabilistic forecasting; though ensemble
forecast are often underdispersive and subject to systematic bias. Hence, they
require some form of statistical post-processing, where parametric models
provide full predictive distributions of the weather variables at hand. We
propose a general two-step machine learning-based approach to calibrating
ensemble weather forecasts, where in the first step improved point forecasts
are generated, which are then together with various ensemble statistics serve
as input features of the neural network estimating the parameters of the
predictive distribution. In two case studies based of 100m wind speed and
global horizontal irradiance forecasts of the operational ensemble pre diction
system of the Hungarian Meteorological Service, the predictive performance of
this novel method is compared with the forecast skill of the raw ensemble and
the state-of-the-art parametric approaches. Both case studies confirm that at
least up to 48h statistical post-processing substantially improves the
predictive performance of the raw ensemble for all considered forecast
horizons. The investigated variants of the proposed two-step method outperform
in skill their competitors and the suggested new approach is well applicable
for different weather quantities and for a fair range of predictive
distributions.
