Improving solar radiation forecasts using advanced statistical post-processing methods
Summary
The increased usage of solar energy places additional importance on forecasts of solar radiation. Solar
panel power production is primarily driven by the amount of solar radiation and it is therefore important to have accurate forecasts of solar radiation. Accurate forecasts that also give information on the
forecast uncertainties can help users of solar energy make better solar radiation based decisions related
to stability of the electrical grid. To achieve accurate forecasts of global radiation together with information about the uncertainty, we apply statistical post-processing techniques to the deterministic forecast
from the Numerical Weather Prediction model HARMONIE, which runs at KNMI. We use regression
based methods that determine relationships between observations of global radiation (made within the
Netherlands network of automatic weather stations) and forecasts of various meteorological variables
from HARMONIE. Those relationships are used to produce probabilistic forecasts of global radiation.
We compare parametric methods that make assumptions on the distribution of the global radiation
and non-parametric methods without any assumptions on the distribution. We find that both types of
methods are able to generate probabilistic forecasts that improve the raw global radiation forecast from
HARMONIE according to the root mean squared error (on the median) and the potential economic
value. We also compare the different regression methods using various scoring metrics like the continuous ranked probability skill score, the Brier skill score and reliability diagrams. We find that quantile
regression and generalized random forests generally perform best and the artificial neural network worst.
Additionally we show that the non-parametric approaches use information from all predictors, whereas
the parametric approaches look only at a limited number of predictors.