Probabilistic solar forecasting: Benchmarks, post-processing, verification. (1st March 2023)

• Record Type: Journal Article
• Authors: Gneiting, Tilmann; Lerch, Sebastian; Schulz, Benedikt
• Is Part Of: Solar energy. Volume 252(2023)
• Page Start: 72
• Page End: 80
• Publication Date: 2023-03-01
• Subjects: Clear-sky index -- Empirical copula -- Isotonic distributional regression -- Neural network -- Reliability diagram -- Post-processing; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2022.12.054
• Languages: English
• ISSNs: 0038-092X

Abstract

Abstract: 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. Highlights: The future of solar forecasting is probabilistic. Isotonic distributional regression (IDR) generates probabilistic benchmark forecasts. Neural network-based post-processing yields improved probabilistic forecasts. Proper scoring rules and reliability diagrams are powerful tools for verification.