Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods

Accurate and reliable forecasting of photovoltaic (PV) power production is crucial for grid operations, electricity markets, and energy planning, as solar systems now contribute a significant share of the electricity supply in many countries. PV power forecasts are often generated by converting forecasts of relevant weather variables to power forecasts via a model chain. The use of ensemble simulations from numerical weather prediction models results in probabilistic PV forecasts in the form of a forecast ensemble. However, weather forecasts often exhibit systematic errors that propagate through the model chain, leading to biased and/or uncalibrated PV power forecasts. These deficiencies can be mitigated by statistical post-processing. Using PV production data and corresponding short-term PV power ensemble forecasts at seven utility-scale PV plants in Hungary, we systematically evaluate and compare seven state-of-the-art methods for post-processing PV power forecasts. These include both parametric and non-parametric techniques, as well as statistical and machine learning-based approaches. Our results show that compared to the raw PV power ensemble, any form of statistical post-processing significantly improves the predictive performance reducing the mean continuous ranked probability score (CRPS) by 11.1–14.7%. Non-parametric methods outperform parametric models, with advanced nonlinear quantile regression models showing the best results. Furthermore, machine learning-based approaches surpass their traditional statistical counterparts by around 2 percentage points in terms of the improvement in mean CRPS over the raw forecasts. • Seven statistical post-processing methods are compared for ensemble PV power forecasts. • Ensemble weather forecasts are converted to PV power using physical model chains. • All methods significantly improve the reliability of the ensemble forecasts. • Statistical calibration reduces CRPS by 11.1–14.7% compared to the raw ensemble. • Quantile regression neural networks outperform distributional regression methods.