The increasing penetration of renewable energy sources, with a notable focus on wind power, within modern electricity grids requires computationally efficient and burden-free short-term wind power forecasting models. Traditional models generating prediction intervals are trained offline and thus deployed for prediction purposes. However, this approach cannot obtain interval forecasts from the most recent wind power observations. In contrast, combining multiple regression models through ensemble learning is recognised as a successful method for improving forecasting performance. By utilising the most recent observations and exploiting the strengths of multiple regression models, this article investigates an Online Ensemble Bagging Regression (OEBR) model for generating prediction intervals. Online gradient descent based optimisation algorithms capable of adaptive-depth calculation from a stream of observations are used here to address the problems with traditional batch learning frameworks. The proposed online learning framework is evaluated against other online learning frameworks using publicly accessible datasets. The results show the proposed model competes with the compared models regarding probabilistic metrics and energy estimations and outperforms computational time requirements for the same number of observations.

中文翻译：

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使用集成 bagging 回归模型进行短期风电功率预测的概率在线学习框架


可再生能源在现代电网中的渗透率不断提高，尤其是风力发电，这需要计算高效且无负担的短期风电预测模型。生成预测区间的传统模型是离线训练的，因此可以用于预测目的。然而，这种方法无法从最近的风电观测中获得区间预报。相比之下，通过集成学习组合多元回归模型被认为是提高预测性能的成功方法。通过利用最新的观察结果并利用多元回归模型的优势，本文研究了一种用于生成预测区间的在线集成袋状回归 （OEBR） 模型。这里使用基于在线梯度下降的优化算法，能够从观察流中自适应深度计算，以解决传统批处理学习框架的问题。使用可公开访问的数据集，将拟议的在线学习框架与其他在线学习框架进行比较。结果表明，所提出的模型在概率指标和能量估计方面与比较模型竞争，并且在相同数量的观测值下优于计算时间要求。