This letter proposes a robust ensemble learning scheme to enhance short-term prediction of wind power generation. The ensemble problem associated with pruning and combination is formulated as a worst-case robust approximation problem, taking forecast uncertainty in individual predictors into consideration. This problem is then transformed into the scaled form of the augmented Lagrangian and is solved via the alternating direction method of multipliers (ADMM). The proposed scheme can be applied to both deterministic and probabilistic forecasting. A comprehensive study is carried out to illustrate the advantage of the proposed scheme in both point and interval forecasting.

中文翻译：

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利用稳健的集合学习增强风力发电预测

这封信提出了鲁棒的整体学习方案，以增强对风力发电的短期预测。与修剪和组合相关的整体问题被公式化为最坏情况的鲁棒逼近问题，同时考虑了各个预测变量中的预测不确定性。然后将此问题转换为增幅拉格朗日的缩放形式，并通过乘数的交替方向方法（ADMM）加以解决。所提出的方案可以应用于确定性和概率性预测。进行了全面的研究，以说明该方案在点和区间预测中的优势。