Title: Boosting Renewable Energy Predictions with Context-Aware Conformal Prediction

Abstract:

As the integration of renewable energy sources expands into the power grid, artificial intelligence (AI) plays an increasingly pivotal role in forecasting and operational management. With higher renewable penetration rates, the ability to generate reliable probabilistic forecasts has become critical for navigating uncertainty and facilitating risk-informed decision-making. However, existing forecasting models frequently struggle with miscalibration, a challenge exacerbated by fluctuating weather patterns, temporal variations, and diverse operational environments.

In practical applications, renewable energy predictions are often sourced from third-party vendors or independent systems. Due to constraints on computational resources or restricted access to proprietary model architectures, retraining these external models is typically not an option. This limitation underscores the demand for efficient, model-agnostic techniques capable of enhancing forecast accuracy post-generation.

To address this gap, this study introduces Context-Aware Conformal Prediction (CACP), a novel framework designed to calibrate renewable energy forecasts. The core innovation of CACP lies in its calibration process, which employs a weighting mechanism. This mechanism prioritizes historical data points that closely resemble the specific conditions of the current forecast target. Consequently, the method generates adaptive prediction intervals that accurately capture local uncertainty profiles, all without needing direct access to or retraining of the original forecasting model.

The efficacy of CACP was evaluated using a comprehensive dataset from the National Renewable Energy Laboratory (NREL) regarding day-ahead solar forecasting. The study encompassed multiple systems, including MISO, ERCOT, and SPP. The findings indicate that CACP significantly enhances the balance between reliability and efficiency at both individual site and broader system levels. When compared to NREL’s baseline forecasting model and other standard conformal prediction approaches, CACP demonstrated superior performance. These outcomes highlight CACP’s potential as a practical, reliability-focused enhancement layer for AI-driven renewable energy forecasting and operational support systems.

Source: arXiv Generated at: 2026-06-03 00:00:00 UTC