带有多切片补偿模型的Legendre记忆单元用于基于风电场集群数据的短期风速预测
Legendre Memory Unit with A Multi-Slice Compensation Model for Short-Term Wind Speed Forecasting Based on Wind Farm Cluster Data
AI总结：
本文提出一种基于多切片补偿模型的Legendre记忆单元，用于提升风电场集群的短期风速预测精度。
摘要：
随着更多风电场集群化并网，对这类风电场集群的短期风速预测对于电力系统正常运行至关重要。本文旨在通过充分利用具有时空相关性的集群数据，实现准确、快速和鲁棒的风速预测。首先，对单个风电场的风速数据应用加权均值滤波（WMF）以去噪。然后，创新性地将Legendre记忆单元（LMU）应用于风速预测，并结合基于Kendall等级相关系数的补偿参数（CPK）对风电场集群数据进行补偿，构建多切片LMU（MSLMU）。最后，提出了一种创新的集成模型WMF-CPK-MSLMU，包含三个关键模块：数据预处理、预测和多切片补偿。优势包括：1）LMU通过反向传播联合建模农场之间的线性和非线性依赖关系，以捕捉时空相关性；2）MSLMU通过使用CPK衍生的权重代替随机初始化，增强预测能力，使空间相关性能够完全激活集群风电场中的隐藏节点；3）CPK适应性地对MSLMU中的补偿模型进行加权，并在空间上补充缺失数据，以使整个模型高度准确和鲁棒。在不同风电场集群上的测试结果表明，所提出的集成模型WMF-CPK-MSLMU在风电场集群的短期预测中比现有模型更有效和优越。
英文摘要：
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.
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