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

Abstract

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.