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Analysis of persistence-based solar irradiance forecasting benchmarks

In this work we analyse a set of benchmark methods for solar irradiance forecasting based on the clear-sky index, namely, persistence, climatology, smart-persistence and convex combination (CC) of persistence and climatology. To assess the boundaries of the benchmarks, we include in the analysis a simple least squares regression technique that uses the last five past samples. For the methods that need data adjustment, we also analyze their variation with different training sets. This work is done for intra-day forecast of global solar horizontal irradiance with 10-minutes and hourly forecast horizons. Our results, obtained in the south-east region of South America known as Pampa Húmeda, confirm the benchmark recommendation, showing that the CC method is exigent for RMSD as it achieves almost the same performance of the more complex linear combination of past measurements. We also observe that the benchmarks have different behavior depending on the metric, as the most exigent benchmark for one metric is not necessarily the most exigent for another.