Copula-Based Aggregation and Context-Aware Conformal Prediction for Reliable Renewable Energy Forecasting

2026-01-31

AI Summary

This study addresses the challenge of constructing well-calibrated aggregate renewable energy forecasts from heterogeneous site-level probabilistic predictions provided by third parties, in the absence of a system-level model. The authors propose a novel calibration framework that uniquely integrates Copula modeling with Context-Aware Conformal Prediction (CACP): the former captures inter-site dependency structures, while the latter corrects aggregation bias. This combination enables the generation of reliable aggregate forecasts with high coverage and sharp prediction intervals—without requiring a trained system-level model. Extensive experiments on large-scale solar datasets from MISO, ERCOT, and SPP demonstrate that the proposed method significantly outperforms uncalibrated baselines, achieving near-nominal coverage while yielding substantially narrower prediction intervals.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The rapid growth of renewable energy penetration has intensified the need for reliable probabilistic forecasts to support grid operations at aggregated (fleet or system) levels. In practice, however, system operators often lack access to fleet-level probabilistic models and instead rely on site-level forecasts produced by heterogeneous third-party providers. Constructing coherent and calibrated fleet-level probabilistic forecasts from such inputs remains challenging due to complex cross-site dependencies and aggregation-induced miscalibration. This paper proposes a calibrated probabilistic aggregation framework that directly converts site-level probabilistic forecasts into reliable fleet-level forecasts in settings where system-level models cannot be trained or maintained. The framework integrates copula-based dependence modeling to capture cross-site correlations with Context-Aware Conformal Prediction (CACP) to correct miscalibration at the aggregated level. This combination enables dependence-aware aggregation while providing valid coverage and maintaining sharp prediction intervals. Experiments on large-scale solar generation datasets from MISO, ERCOT, and SPP demonstrate that the proposed Copula+CACP approach consistently achieves near-nominal coverage with significantly sharper intervals than uncalibrated aggregation baselines.

Problem

• probabilistic forecasting
• forecast aggregation
• renewable energy
• calibration
• cross-site dependence

Innovation

• Copula-based aggregation
• Context-Aware Conformal Prediction
• probabilistic forecasting
• renewable energy
• calibration