Recently, an increasing number of studies have proposed various methods for predicting wind speed to overcome the difficulties caused by the irregularity and randomness of raw data in exploring renewable wind power generation. The lack of both effective data preprocessing techniques and combined forecasting strategies has hindered the development of effective and reliable forecasting systems. In this study, a novel combined forecasting framework that simultaneously considers data preprocessing, combined forecasting, and comprehensive evaluation is presented to address the drawbacks of existing methods. To eliminate noise from raw data, complete ensemble empirical mode decomposition with adaptive noise is employed to reconstruct more reliable wind speed series for forecasting. Then, a combined forecasting module, which includes three neural networks and employs a weighted combination strategy, is designed for improving the forecasting performance, and the capability of this proposed system is verified via an evaluation module. Empirical results have demonstrated that the proposed framework not only achieves both high accuracy and stability but also provides technical support for wind power system dispatch.

近来，越来越多的研究提出了各种预测风速的方法，以克服由原始数据的不规则性和随机性引起的探索可再生风力发电的困难。缺乏有效的数据预处理技术和组合的预测策略都阻碍了有效和可靠的预测系统的开发。在这项研究中，提出了一种新颖的组合预测框架，该框架同时考虑了数据预处理，组合预测和综合评估，以解决现有方法的弊端。为了从原始数据中消除噪声，采用具有自适应噪声的完整集成经验模式分解来重建更可靠的风速序列以进行预测。然后，一个组合的预测模块 包括三个神经网络并采用加权组合策略，旨在提高预测性能，并通过评估模块验证了该系统的功能。实证结果表明，提出的框架不仅实现了高精度和稳定性，而且为风电系统调度提供了技术支持。