A Deep Learning Framework for Day Ahead Wind Power Short-Term Prediction

Due to the increasing proportion of wind power connected to the grid, day-ahead wind power prediction plays a more and more important role in the operation of the power system. This paper proposes a day-ahead wind power short-term prediction model based on deep learning (DWT_AE_BiLSTM). Firstly, discrete wavelet transform (DWT) is used to denoise the data, then an autoencoder (AE) technology is used to extract the data features, and finally, bidirectional long short-term memory (BiLSTM) is used for prediction. To verify the effectiveness of the proposed DWT_AE_BiLSTM model, we studied three different power stations and compared their performance with the shallow neural network model. Experimental analysis shows that this model is more competitive in forecasting accuracy and stability. Compared with the BP model, the proposed model has increased by 3.86%, 3.22% and 3.42% in three wind farms, respectively.

Automated summary

This paper proposes a day-ahead wind power short-term prediction model based on deep learning (DWT_AE_BiLSTM). The model utilizes discrete wavelet transform (DWT) for denoising, autoencoder (AE) technology for feature extraction, and bidirectional long short-term memory (BiLSTM) for prediction. Experimental analysis shows that the proposed model is more competitive in forecasting accuracy and stability compared to the shallow neural network model, achieving an increase of 3.86%, 3.22%, and 3.42% in three wind farms, respectively.