Reliable uncertainty quantification is essential for integrating solar and wind generation into modern power systems, where operators must weigh risk rather than act on point forecasts alone. Existing probabilistic methods, however, often either lack finite-sample validity or require per-site recalibration, so a single model rarely transfers across the diverse climates of a dispersed generation fleet. This paper proposes a heteroscedastic, asymmetric, group-conditional split-conformal framework built on a bootstrap-diverse XGBoost ensemble, producing prediction intervals that adapt in width to local difficulty while retaining distribution-free coverage guarantees. A single fixed specification, with no per-site or per-horizon tuning, is evaluated across four climatologically distinct sites spanning both hemispheres, at horizons of 1 to 12 hours, for both solar irradiance and wind speed. The framework holds near-nominal coverage on both targets and reduces the Interval Score by up to 35% relative to competitive baselines, with the calibration and sharpness of its intervals shown to be properties of the method rather than of site-specific tuning.
Renewable-generation forecasting is a probabilistic time-series problem in which point accuracy alone is insufficient for operational decision-making. This study proposes a physics-informed Bayesian framework for multi-horizon forecasting of solar, wind, and total renewable generation. The task is formulated as a 15-di...
Assem Shayakhmetova, N. Tasbolatuly, Guldana Taganova et al.· Mathematics· 0 citations
The high-frequency variability of solar irradiance, driven by complex cloud–aerosol–radiation interactions, remains a formidable challenge for solar grid integration. Despite extensive research, eliminating the phase lag between forecasts and observations—often called the “persistence hurdle”—remains difficult. Accor...
SPACE is proposed, a conformal wrapper for sample-generating multivariate forecasters that consistently brings realized joint and rolling coverage closer to the nominal target, achieving superior coverage-efficiency tradeoffs relative to competing wrappers.
Baishi Li, Kelvin J. L. Koa, Ke-Wei Huang· 1 citation
This study develops a hierarchical ensemble that combines temporal neural models, historical analogs, state climatology, and gradient-boosted trees to support horizon-specific combination as a useful forecasting strategy while showing that its advantage over strong individual models depends on the dataset and evaluatio...
Yu-Qing Xu, Li-Guo Zhou, Ze-Hua Sun et al.· 0 citations
Accurately predicting fast solar wind conditions is challenging, as uncertainties are large and unquantified by traditional single-value prediction models. In particular, the risks of high-speed solar wind streams (HSSs), which can cause damage to technological infrastructure, cannot be reliably assessed without probab...
Daniel F. Collin, Y. Shprits, Luca Chiarabini et al.· 0 citations
Quantile Dynamically-Tuned Adaptive Conformal Inference (QDtACI) is proposed, a model-agnostic conformal calibration layer that constructs finite-sample intervals around any VaR forecast, using only the return series and the forecast itself, whose reliability tracks the quality of the underlying forecast.
