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A combined framework based on data preprocessing, neural networks and multi-tracker optimizer for wind speed prediction

机译：基于数据预处理，神经网络和多跟踪优化器的风速预测的组合框架

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摘要

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 draw-backs 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.
机译：最近，越来越多的研究提出了一种预测风速的各种方法，以克服由探索可再生风力发电的原始数据的不规则性和随机性引起的困难。缺乏有效数据预处理技术和联合预测策略阻碍了有效且可靠的预测系统的发展。在本研究中，提出了一种新的组合预测框架，即同时考虑数据预处理，组合预测和综合评估，以解决现有方法的退化。为了消除原始数据的噪声，采用具有自适应噪声的完整集合经验模式分解来重建更可靠的风速系列以进行预测。然后，设计包括三个神经网络并采用加权组合策略的组合预测模块，用于提高预测性能，并且通过评估模块验证该提出的系统的能力。经验结果表明，拟议的框架不仅实现了高精度和稳定性，而且还为风电系统调度提供了技术支持。

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