基于Copula的聚合与上下文感知的符合预测用于可靠的可再生能源预测
CoCopula: Conformal Calibration and Copula Aggregation for Reliable Renewable Energy Forecasting
AI总结：
本文提出基于Copula和上下文感知符合预测的聚合方法，用于在无法训练系统级模型时实现可靠可再生能源预测。
摘要：
可再生能源渗透率的快速增长加剧了对可靠概率预测的需求，以支持电网在聚合（舰队或系统）层面的操作。然而，在实践中，系统运营商往往缺乏访问舰队级概率模型的途径，转而依赖第三方提供者提供的站点级预测。从这些输入构建一致且校准的舰队级概率预测仍然具有挑战性，因为存在复杂的跨站点依赖性和聚合引起的不准确。本文提出了一种校准的概率聚合框架，该框架可以直接将站点级概率预测转换为可靠的舰队级预测，在系统级模型无法训练或维护的情况下。该框架整合了基于Copula的依赖性建模与上下文感知的符合预测（CACP）以在聚合层面纠正不准确。这种结合使依赖性感知的聚合得以实现，同时提供有效的覆盖并保持尖锐的预测区间。在大规模太阳能发电数据集上进行的实验表明，所提出的Copula+CACP方法在覆盖方面始终接近名义水平，其区间显著比未校准的聚合基线更尖锐。
英文摘要：
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. Motivated by a real-world operational challenge identified through collaboration with an industry partner, this paper presents a probabilistic aggregation and calibration framework that directly converts site-level probabilistic forecasts into reliable fleet-level forecasts. The paper introduces \method~ (\textbf{Co}nformal Calibration and \textbf{Copula} Aggregation), which integrates Copula-based dependence modeling to capture cross-site correlations followed by Context-Aware Conformal Prediction (CACP) to mitigate miscalibration at the aggregated level. This combination enables dependence-aware aggregation while improving empirical coverage and maintaining sharp prediction intervals. Experiments on large-scale solar generation datasets from three system operators in the United States including MISO, ERCOT, and SPP demonstrate that the proposed \method{} approach achieves near-nominal empirical coverage and consistently lower Winkler scores than competing aggregation-based baselines across different target coverage levels. Importantly, \method{} achieves performance comparable to directly calibrated system-level forecasts, demonstrating its practical value when dedicated fleet-level forecasting models are unavailable.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
