Wind predictions include quantified confidence scores
Modern wind and weather prediction frameworks frequently incorporate probabilistic modeling, conformal prediction, and quantile regression to explicitly quantify uncertainty and confidence scores.
The retrieved literature consistently shows that advanced wind, solar, and weather forecasting models incorporate uncertainty quantification, confidence intervals, and probabilistic prediction frameworks to evaluate reliability.
Zhong X, Chen L, Li H, Buizza R, Liu J, Feng J, Zhu Z, Fan X, Dai K, Luo JJ, Wu J, Lu B. FuXi-ENS: A machine learning model for efficient and accurate ensemble weather prediction.. 2025. https://doi.org/10.1126/sciadv.adu2854
FuXi-ENS applies machine learning for ensemble weather prediction to quantify forecast uncertainty and provide probabilistic weather predictions.
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Sun S, Chen M, Mo M, Yan X, Xiong Z, Hu Y, Zhan Y. An Uncertainty-Aware Temporal Transformer for Probabilistic Interval Modeling in Wind Power Forecasting.. 2026. https://doi.org/10.3390/s26072072
This study presents an uncertainty-aware temporal transformer framework for wind power forecasting that provides simultaneous point and interval forecasts with statistical confidence.
Elmunim NA, Khlifi MA, Aldawsari MA, Algarni F, Albalawi A, Ismail A, Hassan BM. Enhancing wind and solar energy forecasting through time-series feature engineering and ensemble machine learning.. 2026. https://doi.org/10.1038/s41598-026-49373-7
This study uses bootstrapped confidence intervals and cross-validation stability analysis to quantify uncertainty in renewable energy forecasts.
Nthangeni RI, Sigauke C, Ravele T, Tshisikhawe T. Enhancing Short-Term Wind Energy Forecasting with XGBoost and Conformal Prediction for Robust Uncertainty Quantification. 2026. https://doi.org/10.20944/preprints202601.1804.v1
This paper presents probabilistic wind energy forecasting using quantile regression averaging combined with a conformal prediction modelling framework for uncertainty quantification.
Li C, Dai J, Zhu S, Guo R, Liu Z, Jiang Y, Tian S, Wen Y. Credible capacity evaluation of virtual power plants considering wind and PV uncertainties.. 2025. https://doi.org/10.1038/s41598-025-26037-6
This paper compares probabilistic forecasting methods to quantify wind variability, utilizing metrics such as empirical coverage and confidence intervals.
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