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Short-term Wind Power Prediction Method Based on Wavelet Packet Decomposition and Improved GRU

机译：基于小波包分解和改进GRU的风电短期预测方法

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Accurately predicting wind power in wind farms is of great significance to ensure the safe and stable operation of the power system. A novel short-term wind power forecasting method (WPD-GRU-SELU) based on wavelet packet decomposition (WPD) and improved gated recurrent unit (GRU) is proposed. Firstly, this method uses WPD to decompose the time series of wind power into several sub-sequences with different frequencies. Then the sub-sequences of different frequency components are predicted by using the improved GRU neural network, which uses the scaled exponential linear units(SELU) as the activation function to squash the hidden states to calculate the output. Finally, the output datum of GRU neural networks are reconstructed to obtain the complete wind power predicting results. Experiments illustrate that the WPD-GRU-SELU model have a more accurate forecast to the short-term wind power prediction compared with other RNN models.
机译：准确预测风电场的风电功率对确保电力系统的安全稳定运行具有重要意义。提出了一种基于小波包分解（WPD）和改进的门控循环单元（GRU）的短期风电功率预测方法（WPD-GRU-SELU）。首先，该方法使用WPD将风能的时间序列分解为多个具有不同频率的子序列。然后，使用改进的GRU神经网络预测不同频率分量的子序列，该神经网络使用缩放的指数线性单位（SELU）作为激活函数来压缩隐藏状态以计算输出。最后，重建GRU神经网络的输出数据以获得完整的风电功率预测结果。实验表明，与其他RNN模型相比，WPD-GRU-SELU模型对短期风电功率预测具有更准确的预测。

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