The growing penetration of intermittent, renewable generation in US power grids, especially wind and solar generation, results in increased operational uncertainty. In that context, accurate forecasts are critical, especially for wind generation, which exhibits large variability and is historically harder to predict. To overcome this challenge, this work proposes a novel Bundle-Predict-Reconcile (BPR) framework that integrates asset bundling, machine learning, and forecast reconciliation techniques. The BPR framework first learns an intermediate hierarchy level (the bundles), then predicts wind power at the asset, bundle, and fleet level, and finally reconciles all forecasts to ensure consistency. This approach effectively introduces an auxiliary learning task (predicting the bundle-level time series) to help the main learning tasks. The paper also introduces new asset-bundling criteria that capture the spatio-temporal dynamics of wind power time series. Extensive numerical experiments are conducted on an industry-size dataset of 283 wind farms in the MISO footprint. The experiments consider short-term and day-ahead forecasts, and evaluates a large variety of forecasting models that include weather predictions as covariates. The results demonstrate the benefits of BPR, which consistently and significantly improves forecast accuracy over baselines, especially at the fleet level.

翻译：间歇性可再生能源（尤其是风能和太阳能）在美国电网中的渗透率不断提高，导致运行不确定性增加。在此背景下，精准的预测至关重要，特别是对于具有较大波动性且历来难以预测的风力发电而言。为应对这一挑战，本文提出了一种新颖的"组合-预测-调和"(BPR)框架，该框架整合了资产组合、机器学习和预测调和技术。BPR框架首先学习一个中间层级结构（即组合），然后预测资产、组合和机组群层面的风电功率，最后对所有预测结果进行调和以确保一致性。这种方法有效引入了一个辅助学习任务（预测组合层面的时间序列）来帮助主要学习任务。本文还提出了新的资产组合标准，以捕捉风电时间序列的时空动态特性。基于MISO区域283个风电场的工业级数据集，开展了大量数值实验。实验涵盖短期和日前预测，并评估了多种将天气预报作为协变量的预测模型。结果表明，BPR框架能够稳定且显著地提升基准方法的预测精度，尤其在机组群层面效果尤为突出。