Reliable wind energy forecasting is crucial for the stable operation of power grids. This paper proposes a regional wind power probabilistic forecasting model comprising an improved kernel density estimation (IKDE), regular vine copulas, and ensemble learning. The IKDE is firstly used to generate the margin probability density function (PDF) of each wind farm and the KDE bandwidth is optimized via the golden-section search algorithm to obtain the best possible prediction. Then, several dependence structures are formulated by building different regular vine copulas based on multiple criteria, and all the dependence structures work together with marginal PDF to generate respective joint distribution functions. Finally, ensemble learning is applied to combine all the joint distribution functions and establish an ultimate distribution function. Furthermore, a novel multi-distribution mega-trend-diffusion (MD-MTD) with parametric optimization is proposed to improve the prediction when the data are insufficient. The results of comparative evaluations conducted on datasets from eight wind farms indicate that the proposed model outperforms existing models in wind power generation prediction. Specifically, the proposed model can reliably forecast power generation for an entire region for the next 24 h with only three months of historical data. In contrast, most benchmark models require a year of data.

可靠的风能预测对于电网的稳定运行至关重要。本文提出了一种区域风电概率预测模型，该模型包括改进的核密度估计 (IKDE)、常规藤蔓联结和集成学习。首先使用 IKDE 生成每个风电场的边际概率密度函数 (PDF)，并通过黄金分割搜索算法优化 KDE 带宽以获得最佳预测。然后，通过基于多个标准构建不同的规则藤蔓 copula 来制定几个依赖结构，并且所有依赖结构与边际 PDF 一起工作以生成各自的联合分布函数。最后，应用集成学习将所有联合分布函数组合起来，建立一个最终的分布函数。此外，提出了一种具有参数优化的新型多分布大趋势扩散（MD-MTD），以在数据不足时改进预测。对来自八个风电场的数据集进行的比较评估结果表明，所提出的模型在风力发电预测方面优于现有模型。具体来说，所提出的模型只需三个月的历史数据就可以可靠地预测整个地区未来 24 小时的发电量。相比之下，大多数基准模型需要一年的数据。对来自八个风电场的数据集进行的比较评估结果表明，所提出的模型在风力发电预测方面优于现有模型。具体来说，所提出的模型只需三个月的历史数据就可以可靠地预测整个地区未来 24 小时的发电量。相比之下，大多数基准模型需要一年的数据。对来自八个风电场的数据集进行的比较评估结果表明，所提出的模型在风力发电预测方面优于现有模型。具体来说，所提出的模型只需三个月的历史数据就可以可靠地预测整个地区未来 24 小时的发电量。相比之下，大多数基准模型需要一年的数据。