CoCopula is a framework that transforms site-level probabilistic forecasts into reliable fleet-level forecasts by combining Copula-based dependence modeling with Context-Aware Conformal Prediction. It captures cross-site correlations and mitigates aggregation-induced miscalibration, producing sharp prediction intervals with near-nominal empirical coverage. Experiments on large-scale solar generation data from MISO, ERCOT, and SPP show lower Winkler scores than competing baselines and performance comparable to directly calibrated system-level forecasts.
arXiv:2510. 15780v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI) is increasingly used to support renewable energy forecasting and grid operations.
arXiv:2609.23988v1 Announce Type: cross Abstract: Renewable forecasts are evaluated by statistical skill (e.g., CRPS), but grid operators pay for realized dispatch cost. We diagnose what drives dispa...
arXiv:2411. 12193v4 Announce Type: replace-cross Abstract: The rapid growth of distributed energy resources (DERs) presents both opportunities and operational challenges for electric grid management.
arXiv:2607. 11470v1 Announce Type: cross Abstract: 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.
arXiv:2607. 20587v1 Announce Type: cross Abstract: Modern power systems increasingly require probabilistic forecasts amid interacting uncertainties from renewable intermittency, flexible demand, market volatility, and weather-dependent generation.
arXiv:2610.27763v1 Announce Type: new Abstract: The rapid growth of distributed photovoltaic (PV) generation is increasing the demand for accurate forecasting across large numbers of geographically d...
arXiv:2606. 31804v1 Announce Type: new Abstract: Accurate energy demand forecasting is essential for the reliable operation and planning of modern sustainable energy systems.
The paper introduces Generalized Gibbs Ensemble Weighting (GGEW), a probabilistic framework that assigns weights to forecasting models using a Gibbs-style exponential transformation of normalized predictive loss. GGEW extends basic weighting through numerical stabilization, diversity-aware score corrections, and online hyperparameter adaptation, yielding variants such as Stable Gibbs weighting, Directional Gibbs-NCL, and Symmetric Gibbs-NCL. The authors evaluate GGEW on M4 competition submissions and real-world datasets (Monash Traffic, Electricity, Solar), finding that Gibbs-style adaptive weighting is competitive across various settings, though performance varies by dataset, horizon, and deployment protocol.
arXiv:2606. 08630v1 Announce Type: cross Abstract: Global wind power capacity, especially in China, is booming, with new farms spanning diverse terrains and climates.
SPACE is a conformal wrapper that creates ellipsoidal joint prediction regions for multivariate time‑series forecasts by estimating time‑local covariance directly from the current forecast sample cloud. It calibrates the region’s radius using a dynamic backward window‑selection scheme, avoiding reliance on historical residuals. Experiments on diverse datasets show that SPACE improves joint and rolling coverage, achieving better coverage‑efficiency tradeoffs than existing wrappers.
arXiv:2610.16110v1 Announce Type: new Abstract: Ultra-short-term forecasting of densely distributed photovoltaic (PV) systems remains challenging because external meteorological and satellite product...
The paper introduces STOIC, a post‑hoc residual calibration framework that enhances uncertainty quantification for energy time series over graphs. STOIC combines spatial‑temporal graph neural network forecasts with in‑context learning from tabular foundation models to estimate feature‑conditional residual quantiles, thereby capturing both sequential and relational dependencies. It is designed as a conformal‑inspired method that does not require task‑specific retraining of the calibration model.