Probabilistic solar forecasting: Benchmarks, post-processing, verification
In one sentence. Proposes a probabilistic clear-sky benchmark and neural post-processing methods for NWP-based solar irradiance forecasts, with proper scoring and calibration checks.
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.
Labels
- Topic
- Post-processing & calibration, Probabilistic & ensemble forecasting
- Type
- Review, survey or tutorial
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days
- Domain
- Regional
- Data
- NWP analysis or forecasts (IFS, GFS, ...)
- Status
- Research
- Identifiers
- DOI:10.1016/j.solener.2022.12.054 OpenAlex:W4320168704
- Added
- 2026-10-22
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