Artificial intelligence (AI) is increasingly used to support renewable energy forecasting and grid operations. As renewable penetration grows, reliable probabilistic forecasting is becoming essential for managing uncertainty and supporting risk-aware operational decision-making. However, these forecasts often suffer from miscalibration due to temporal variability, changing weather conditions, and heterogeneous operating regimes. In many real-world settings, renewable energy forecasts are provided by external sources, vendors, or independently trained systems, making retraining infeasible because of limited model access or computational constraints. This creates a need for efficient and model-agnostic methods that can improve forecast reliability after they are produced. This paper presents Context-Aware Conformal Prediction (CACP), a framework for calibrating renewable energy forecasts. The proposed method relies on a weighting mechanism during the calibration procedure which assigns higher weights to historical observations that are more similar to the target forecasting condition. This enables adaptive prediction intervals that reflect local uncertainty regimes without requiring access to, or retraining of, the underlying forecasting model. Experiments are performed on a large-scale dataset from National Renewable Energy Laboratory (NREL) day-ahead solar forecasting, covering multiple systems including MISO, ERCTO, and SPP. The results show that CACP improves the reliability-efficiency tradeoff at both site and system levels compared to NREL's base forecasting model and the other conformal prediction baselines. These results suggest that CACP can serve as a practical reliability-enhancement layer for trustworthy AI-enabled renewable energy forecasting and operational decision support.

翻译：人工智能（AI）正日益广泛应用于可再生能源预测与电网运行支持。随着可再生能源渗透率持续增长，可靠的概率预测对于管理不确定性、支持风险感知运行决策至关重要。然而，由于时间变异性、天气条件变化及异构运行工况的影响，这些预测往往存在校准偏差。在诸多实际场景中，可再生能源预测由外部数据源、供应商或独立训练的系统提供，受限于模型访问权限或计算资源约束，重新训练往往不可行。这催生了对高效且模型无关方法的需求，以在预测生成后提升其可靠性。本文提出上下文感知共形预测（CACP）框架，用于校准可再生能源预测。该方法在校准过程中引入加权机制，赋予与目标预测条件更相似的历史观测数据更高权重，从而在无需访问底层预测模型或对其进行重新训练的前提下，生成反映局部不确定性状态的自适应预测区间。基于美国国家可再生能源实验室（NREL）日前太阳能预测的大规模数据集（覆盖MISO、ERCOT与SPP等多个系统）开展实验。结果表明，相较于NREL基础预测模型及其他共形预测基线方法，CACP在站点级和系统级均提升了可靠性-效率权衡性能。这些发现表明，CACP可作为实用可靠性增强层，支撑可信AI驱动的可再生能源预测与运行决策支持。