With more wind farms clustered for integration, the short-term wind speed prediction of such wind farm clusters is critical for normal operation of power systems. This paper focuses on achieving accurate, fast, and robust wind speed prediction by full use of cluster data with spatial-temporal correlation. First, weighted mean filtering (WMF) is applied to denoise wind speed data at the single-farm level. The Legendre memory unit (LMU) is then innovatively applied for the wind speed prediction, in combination with the Compensating Parameter based on Kendall rank correlation coefficient (CPK) of wind farm cluster data, to construct the multi-slice LMU (MSLMU). Finally, an innovative ensemble model WMF-CPK-MSLMU is proposed herein, with three key blocks: data pre-processing, forecasting, and multi-slice compensation. Advantages include: 1) LMU jointly models linear and nonlinear dependencies among farms to capture spatial-temporal correlations through backpropagation; 2) MSLMU enhances forecasting by using CPK-derived weights instead of random initialization, allowing spatial correlations to fully activate hidden nodes across clustered wind farms.; 3) CPK adaptively weights the compensation model in MSLMU and complements missing data spatially, to facilitate the whole model highly accurate and robust. Test results on different wind farm clusters indicate the effectiveness and superiority of proposed ensemble model WMF-CPK-MSLMU in the short-term prediction of wind farm clusters compared to the existing models.

翻译：随着更多风电场集群并网，此类风电集群的短期风速预测对电力系统的正常运行至关重要。本文重点通过充分利用具有时空相关性的集群数据，实现准确、快速且稳健的风速预测。首先，采用加权均值滤波（WMF）对单场级风速数据进行去噪处理。随后，创新性地将Legendre记忆单元（LMU）应用于风速预测，并结合基于风电场集群数据Kendall秩相关系数（CPK）的补偿参数，构建了多切片LMU（MSLMU）。最后，本文提出了一种创新的集成模型WMF-CPK-MSLMU，其包含三个关键模块：数据预处理、预测和多切片补偿。该模型的优势包括：1）LMU联合建模风电场间的线性和非线性依赖关系，通过反向传播捕捉时空相关性；2）MSLMU通过使用CPK导出的权重而非随机初始化来增强预测能力，使得空间相关性能够充分激活集群风电场间的隐藏节点；3）CPK自适应地加权MSLMU中的补偿模型，并在空间上补全缺失数据，从而提升整体模型的高精度和鲁棒性。在不同风电场集群上的测试结果表明，相较于现有模型，所提出的集成模型WMF-CPK-MSLMU在风电场集群短期预测中具有有效性和优越性。