Probabilistic solar forecasting: Benchmarks, post-processing, verification

Probabilistic solar forecasts may take the form of predictive probability distributions, ensembles, quantiles, or interval forecasts. State-of-the-art approaches build on input from numerical weather prediction (NWP) models and post-processing with statistical and machine learning methods. We propose a probabilistic benchmark based on a deterministic forecast of clear-sky irradiance, introduce new methods for post-processing that merge statistical techniques with modern neural networks, discuss methods for spatio-temporal scenario forecasts, and illustrate the assessment of predictive ability via proper scoring rules and calibration checks. We expect future solar forecasting efforts to be increasingly probabilistic, and encourage continuing close interaction with operational weather prediction, where innovations based on sophisticated neural networks supplement and challenge traditional approaches.

Automated summary

Probabilistic solar forecasts can take the form of predictive probability distributions, ensembles, quantiles, or interval forecasts. The state-of-the-art approaches utilize input from numerical weather prediction models and apply statistical and machine learning methods for post-processing. This study proposes a probabilistic benchmark based on deterministic forecast and introduces new methods that merge statistical techniques with modern neural networks for post-processing.