  Weather forecasts from numerical weather prediction models play a central
role in solar energy forecasting, where a cascade of physics-based models is
used in a model chain approach to convert forecasts of solar irradiance to
solar power production, using additional weather variables as auxiliary
information. Ensemble weather forecasts aim to quantify uncertainty in the
future development of the weather, and can be used to propagate this
uncertainty through the model chain to generate probabilistic solar energy
predictions. However, ensemble prediction systems are known to exhibit
systematic errors, and thus require post-processing to obtain accurate and
reliable probabilistic forecasts. The overarching aim of our study is to
systematically evaluate different strategies to apply post-processing methods
in model chain approaches: Not applying any post-processing at all;
post-processing only the irradiance predictions before the conversion;
post-processing only the solar power predictions obtained from the model chain;
or applying post-processing in both steps. In a case study based on a benchmark
dataset for the Jacumba solar plant in the U.S., we develop statistical and
machine learning methods for post-processing ensemble predictions of global
horizontal irradiance and solar power generation. Further, we propose a neural
network-based model for direct solar power forecasting that bypasses the model
chain. Our results indicate that post-processing substantially improves the
solar power generation forecasts, in particular when post-processing is applied
to the power predictions. The machine learning methods for post-processing
yield slightly better probabilistic forecasts, and the direct forecasting
approach performs comparable to the post-processing strategies.
