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Probabilistic solar forecasting: Benchmarks, post-processing, verification
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DOI：10.1016/j.solener.2022.12.054.png)
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Abstract
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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.
Keywords:
Clear-sky index
Empirical copula
Isotonic distributional regression
Neural network
Reliability diagram
Post-processing
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