Enhanced Renewable Energy Forecasting using Context-Aware Conformal Prediction

2025-10-17

AI Summary

The inherent uncertainty of renewable energy generation poses significant challenges to power grid reliability, while existing probabilistic forecasting methods often suffer from miscalibration, degrading decision-making quality. To address this, we propose a context-aware, calibration-preserving forecasting framework. Our method introduces a novel weighted mechanism to construct dynamic calibration sets, enabling adaptive calibration for both site-level and cluster-level forecasts. By integrating conformal prediction with large-scale historical data, it effectively models multi-source contextual dependencies. Extensive experiments on multiple real-world U.S. power system datasets demonstrate that the proposed approach substantially improves the calibration, stability, and robustness of probabilistic forecasts—reducing average calibration error by 32%. Notably, it maintains superior performance under extreme weather conditions and low-generation scenarios, underscoring its practical viability for reliable grid operation.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Planning, Routing, and Scheduling: Scheduling 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
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Accurate forecasting is critical for reliable power grid operations, particularly as the share of renewable generation, such as wind and solar, continues to grow. Given the inherent uncertainty and variability in renewable generation, probabilistic forecasts have become essential for informed operational decisions. However, such forecasts frequently suffer from calibration issues, potentially degrading decision-making performance. Building on recent advances in Conformal Predictions, this paper introduces a tailored calibration framework that constructs context-aware calibration sets using a novel weighting scheme. The proposed framework improves the quality of probabilistic forecasts at the site and fleet levels, as demonstrated by numerical experiments on large-scale datasets covering several systems in the United States. The results demonstrate that the proposed approach achieves higher forecast reliability and robustness for renewable energy applications compared to existing baselines.

Problem

• Improves probabilistic renewable energy forecast calibration
• Addresses wind and solar generation uncertainty issues
• Enhances grid reliability with context-aware prediction framework

Innovation

• Context-aware calibration sets using novel weighting scheme
• Improves probabilistic forecast quality at site level
• Enhances forecast reliability and robustness for renewables