A Monte Carlo based solar radiation forecastability estimation

Abstract

Based on the reported literature and commonly used metrics in the realm of solar forecasting, a new methodology is developed for estimating a metric called forecastability (F). It reveals the extent to which solar radiation time series can be forecasted and provides the crucial context for judging the inherent difficulty associated with a particular forecast situation. Unlike the score given by the standard smart persistence model, the F metric which is bounded between 0% and 100% is easier to interpret, hence making comparisons between forecasting studies more consistent. This approach uses the Monte Carlo method and estimates F from the standard error metric RMSE and the persistence predictor. Based on the time series of solar radiation measured at six very different locations (with optimized clear sky model) from a meteorological point of view, it is shown that F varies between 25.5% and 68.2% and that it exists a link between forecastability and errors obtained by machine learning prediction methods. The proposed methodology is validated for 3 parameters that may affect the F estimation (time horizon, temporal granularity, and solar radiation components) and for 50 time series relative to McClear web service and to the central archive of Baseline Surface Radiation Network.