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.

翻译：精确预测对于电网可靠运行至关重要，尤其是在风能、太阳能等可再生能源发电占比持续增长的背景下。考虑到可再生能源发电固有的不确定性和波动性，概率预测已成为制定明智运行决策的关键。然而，此类预测常存在校准问题，可能降低决策性能。基于保形预测领域的最新进展，本文引入了一种定制的校准框架，该框架采用一种新颖的加权方案构建上下文感知的校准集。通过对覆盖美国多个系统的大规模数据集进行的数值实验证明，所提框架在站点和集群层面均提升了概率预测的质量。结果表明，与现有基线方法相比，所提方法在可再生能源应用中实现了更高的预测可靠性和鲁棒性。