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

TL;DR

This paper introduces a novel framework combining copula-based dependence modeling with context-aware conformal prediction to produce reliable, well-calibrated probabilistic forecasts for aggregated renewable energy systems from heterogeneous site-level inputs.

Contribution

It presents a new aggregation method that effectively captures cross-site dependencies and corrects calibration issues without needing system-level models.

Findings

Achieves near-nominal coverage in large-scale solar datasets.

Produces significantly sharper prediction intervals than baseline methods.

Demonstrates effectiveness across multiple regional datasets.

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.