Probabilistic wind power forecasting approaches have significantly advanced in recent decades. However, forecasters often assume data completeness and overlook the challenge of missing values resulting from sensor failures, network congestion, etc. Traditionally, this issue is addressed during the data preprocessing procedure using methods such as deletion and imputation. Nevertheless, these ad-hoc methods pose challenges to probabilistic wind power forecasting at both parameter estimation and operational forecasting stages. In this paper, we propose a resilient probabilistic forecasting approach that smoothly adapts to missingness patterns without requiring preprocessing or retraining. Specifically, we design an adaptive quantile regression model with parameters capable of adapting to missing patterns, comprising two modules. The first is a feature extraction module where weights are kept static and biases are designed as a function of missingness patterns. The second is a non-crossing quantile neural network module, ensuring monotonicity of quantiles, with higher quantiles derived by adding non-negative amounts to lower quantiles. The proposed approach is applicable to both missing-at-random and missing-not-at-random cases. Case studies demonstrate that our proposed approach achieves state-of-the-art results in terms of the continuous ranked probability score, with acceptable computational cost.

翻译：近年来，概率化风电功率预测方法取得了显著进展。然而，预测者通常假设数据完整，忽视了传感器故障、网络拥塞等导致的缺失值问题。传统上，该问题在数据预处理阶段通过删除和插补等方法处理。然而，这些临时性方法在参数估计和运行预测阶段对概率化风电功率预测提出了挑战。本文提出了一种鲁棒的概率预测方法，该方法无需预处理或重新训练即可平滑适应缺失模式。具体而言，我们设计了一种参数可自适应缺失模式的自适应分位数回归模型，包含两个模块：第一个是特征提取模块，其中权重保持静态，偏差设计为缺失模式的函数；第二个是非交叉分位数神经网络模块，确保分位数的单调性，即通过向低分位数添加非负量来推导高分位数。所提方法适用于随机缺失和非随机缺失两种情况。案例研究表明，在连续排序概率分数指标上，所提方法以可接受的计算成本取得了最先进的结果。