Optimal place to apply post-processing in the deterministic photovoltaic power forecasting workflow

Is the post-processing of global horizontal irradiance (GHI) forecasts necessary for issuing good photovoltaic (PV) power forecasts? Whenever this question is raised, the instinctive supposition always seems to be “yes,” because GHI is the most important weather parameter governing the amount of PV power generated, and surely, the better the GHI forecasts are, the better the PV power forecasts should result. To attend to this question more scientifically and more formally, two classic deterministic-to-deterministic post-processing methods, namely, the model output statistics and kernel conditional density estimation, are applied at various stages of PV power forecasting, resulting in four distinct workflows. These different workflows are trained and tested on three PV plants in Hungary, using data from a four-year (2017–2020) period. Both ground-based GHI and satellite-retrieved GHI are used as the “truth” with which numerical weather prediction (NWP) GHI forecasts are post-processed. A very thorough deterministic forecast verification exercise is conducted following the best practices. It is found that contrary to the common supposition, post-processing GHI only leads to marginal, if that can be quantified at all, benefits, so long as the PV power forecasts are to be post-processed. This ought to be deemed as a very important finding, as it puts into question the “GHI forecasting + post-processing + irradiance-to-power conversion” workflow that has dominated solar forecasting for decades.