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Performance comparison of ANNs model with VMD for short-term wind speed forecasting

机译：ANN <？show [AQ =“” ID =“ Q1]”？>模型与VMD的性能比较，用于短期风速预测

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With the integration of wind energy into electricity grids, it is becoming increasingly important to obtain accurate wind speed forecasts, and accurate wind speed forecasts are necessary to schedule power system. In this study, an artificial neural networks (NNs) model with a variational mode decomposition (VMD) for a short-term wind speed forecasting was presented. To reduce the non-stationary of wind speed time series, the historical wind speed was decomposed into different intrinsic mode functions (IMFs) by a VMD. The back-propagation NN with Levenberg–Marquardt was adopted to build sub-models according to the different characteristic of each IMF. The sub-models corresponding to different IMFs were superposed to obtain wind speed-forecasting models. In the experiment, the proposed forecasting model was compared with an NN with wavelet decomposition and empirical mode decomposition. The performance was evaluated based on three metrics, namely maximum absolute error, root mean square error and the correlation coefficient. The comparison results indicate that significant improvements in forecasting accuracy were obtained with the proposed forecasting models compared with other forecasting methods.
机译：随着将风能集成到电网中，获得准确的风速预测变得越来越重要，准确的风速预测对于调度电力系统是必要的。在这项研究中，提出了一种人工神经网络（NNs）模型，该模型具有变分模式分解（VMD），可用于短期风速预测。为了减少风速时间序列的非平稳性，VMD将历史风速分解为不同的固有模式函数（IMF）。根据每个IMF的不同特征，采用Levenberg-Marquardt的反向传播NN来构建子模型。将对应于不同IMF的子模型进行叠加以获得风速预测模型。在实验中，将所提出的预测模型与带有小波分解和经验模式分解的神经网络进行了比较。基于三个指标评估性能，即最大绝对误差，均方根误差和相关系数。比较结果表明，与其他预测方法相比，所提出的预测模型在预测准确性上有显着提高。

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