Comparing Beta Regression and Quantile Regression Forests for Probabilistic Photovoltaic Energy Forecasting

This work focuses on the analysis of probabilistic power curve models that link the energy production of a photovoltaic system with the irradiance forecasts of the weather provider. It is shown that the essential information is provided by global horizontal irradiance (GHI) forecasts, which can be used to obtain simple and meaningful probabilistic models using non-parametric (quantile Regression forests) and parametric (Beta regression) methods. The second step is the analysis of the monthly performance of power curve models, to understand whether their performance can improve by taking monthly variability into account.