Wind speed interval forecasting is being considered increasingly because it can provide more comprehensive information to address uncertainties in wind power generation. It helps ensure power quality, optimize power dispatching, and bring more economic and social benefits. Currently, the lower upper bound estimation (LUBE) approach is believed to provide outstanding performance in interval forecasting. However, considering the significant noise and irregular characteristics of wind speed, most existing LUBE models can either ineffectively learn the wind speed variation patterns behind the data or behave in a very unstable manner, leading to unsatisfactory results. Thus, this paper proposes a novel LUBE-based wind speed interval forecasting system based on an innovative parallel feature selection module for extracting the underlying vital historical variation patterns and a unique constrained LUBE training algorithm characterized by an amnesia operator to further guarantee the efficiency and stability of the LUBE training. The system effectiveness was demonstrated by performing experiments using two real datasets. The results show that the proposed system performs better than the naive, bootstrap, error analysis, and other LUBE models. It at least enhances the coverage width criterion by 1.8% and 6.8% for the two datasets, respectively.

风速区间预测越来越被考虑，因为它可以提供更全面的信息来解决风力发电的不确定性。有利于保证电能质量，优化电力调度，带来更大的经济效益和社会效益。目前，下上限估计（LUBE）方法被认为在区间预测中提供了出色的性能。然而，考虑到显着的噪声和风速的不规则特性，现有的大多数LUBE模型要么不能有效地学习数据背后的风速变化模式，要么表现得非常不稳定，导致结果不尽人意。因此，本文提出了一种新的基于 LUBE 的风速区间预测系统，该系统基于用于提取潜在重要历史变化模式的创新并行特征选择模块和以健忘算子为特征的独特约束 LUBE 训练算法，以进一步保证预测的效率和稳定性。润滑油培训。通过使用两个真实数据集进行实验证明了系统的有效性。结果表明，所提出的系统比 naive、bootstrap、错误分析和其他 LUBE 模型的性能更好。它至少将两个数据集的覆盖宽度标准分别提高了 1.8% 和 6.8%。通过使用两个真实数据集进行实验证明了系统的有效性。结果表明，所提出的系统比 naive、bootstrap、错误分析和其他 LUBE 模型的性能更好。它至少将两个数据集的覆盖宽度标准分别提高了 1.8% 和 6.8%。通过使用两个真实数据集进行实验证明了系统的有效性。结果表明，所提出的系统比 naive、bootstrap、错误分析和其他 LUBE 模型的性能更好。它至少将两个数据集的覆盖宽度标准分别提高了 1.8% 和 6.8%。