Machine learning techniques have been successfully used in probabilistic wind
power forecasting. However, the issue of missing values within datasets due to
sensor failure, for instance, has been overlooked for a long time. Although it
is natural to consider addressing this issue by imputing missing values before
model estimation and forecasting, we suggest treating missing values and
forecasting targets indifferently and predicting all unknown values
simultaneously based on observations. In this paper, we offer an efficient
probabilistic forecasting approach by estimating the joint distribution of
features and targets based on a generative model. It is free of preprocessing,
and thus avoids introducing potential errors. Compared with the traditional
"impute, then predict" pipeline, the proposed approach achieves better
performance in terms of continuous ranked probability score.

通过利用生成模型，在不需要预处理且避免引入潜在错误的情况下，同时预测基于观测值的所有未知值，提供了一种高效的概率预测方法，相比传统的先填补值再预测方法，能够在连续排名概率得分方面取得更好的性能。

处理概率风力预测中的缺失值：一种生成方法

Tackling Missing Values in Probabilistic Wind Power Forecasting: A  Generative Approach

This paper studies an adaptive approach for probabilistic wind power
forecasting (WPF) including offline and online learning procedures. In the
offline learning stage, a base forecast model is trained via inner and outer
loop updates of meta-learning, which endows the base forecast model with
excellent adaptability to different forecast tasks, i.e., probabilistic WPF
with different lead times or locations. In the online learning stage, the base
forecast model is applied to online forecasting combined with incremental
learning techniques. On this basis, the online forecast takes full advantage of
recent information and the adaptability of the base forecast model. Two
applications are developed based on our proposed approach concerning
forecasting with different lead times (temporal adaptation) and forecasting for
newly established wind farms (spatial adaptation), respectively. Numerical
tests were conducted on real-world wind power data sets. Simulation results
validate the advantages in adaptivity of the proposed methods compared with
existing alternatives.

通过离线学习和在线学习过程，本文研究了一种自适应概率性风力发电预测方法，该方法在基本预测模型中引入了元学习的内外循环更新，使其具备对不同预测任务（具有不同前导时间或位置的概率性风力发电预测）的优秀适应性。该方法在在线学习阶段与增量学习技术相结合，充分利用最新信息和基本预测模型的适应性，在不同前导时间（时间上的适应）和新建风电场的预测（空间上的适应）方面取得了良好的应用效果。通过对真实世界的风力发电数据集进行数值测试，仿真结果验证了该方法相对于现有替代方法的优势。