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A hybrid model based on data preprocessing strategy and error correction system for wind speed forecasting

机译：一种基于数据预处理策略和风速预测纠错系统的混合模型

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摘要

Wind speed forecasting is crucial in exploiting wind energy and integrating power grid. This study presents a novel hybrid model, which includes decomposition module with real-time decomposition strategy, forecasting module and error correction module. In this model, the raw wind speed series is decomposed with empirical wavelet transform into several subseries. The Elman neural network is employed as predictor for each subseries. In addition, a new error correction system is proposed to capture the hidden information from wind speed and enhance the forecasting capability. In the error correction system, a quasi-real-time decomposition strategy is constructed to obtain errors of each subseries. The variational mode decomposition-autoregressive integrated moving average approach is built to predict the error series and complete the error correction task. Two experiments covering eight wind speed datasets and ten compared models are utilized to verify the effectiveness of the proposed model. The results show that (a) the developed error correction system is an effective way to enhance forecasting performance of the decomposition based model; (b) the error series can be effectively repaired to increase the forecasting accuracy by the combination of the variational mode decomposition method and the autoregressive integrated moving average method; (c) the proposed model outperforms the compared conventional models in short-term wind speed forecasting.
机译：风速预测在利用风能和集成电网方面至关重要。本研究提出了一种新颖的混合模型，包括具有实时分解策略，预测模块和纠错模块的分解模块。在该模型中，原始风速系列用经验小波变换分解成几个子系。 ELMAN神经网络被用作每个子媒体的预测因子。此外，提出了一种新的纠错系统来捕获来自风速的隐藏信息并提高预测能力。在纠错系统中，构建了准实时分解策略以获取每个子系列的错误。构建变分模式分解 - 自回归综合移动平均方法，以预测错误序列并完成纠错任务。利用涵盖八个风速数据集和十个比较模型的两个实验来验证所提出的模型的有效性。结果表明，（a）开发的纠错系统是增强基于分解模型的预测性能的有效方法; （b）可以有效地修复错误系列，以通过变分模块分解方法和自回转综合移动平均方法的组合增加预测精度; （c）所提出的模型在短期风速预测中优于比较的传统模型。

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