Short-term Wind Power Forecasting Based on VMD-GRAU
The existing short-term wind power prediction models are difficult to accurately capture the nonlin-ear mapping relationship between wind power historical monitoring data and wind power and adapt to the en-vironmental changes in wind speed,which lead to poor prediction results.In view of these problems,a wind power prediction model was designed based on Variational Mode Decomposition(VMD)and Recurrent Neu-ral Network(RNN).The model first performed VMD of the wind power data to avoid the problems of mode mixing and false peaks,and proposed the Gated Recurrent Attention Unit(GRAU)model,which designed an attention gate to enhance the ability of RNN to capture the important sequential features and robustness.An error correction module was designed to reduce the effects of stochasticity and volatility in wind power pre-diction,and finally a sparse regularization term was added in the loss function to prevent the model from be-ing overfitted.The experimental results showed that the proposed VMD-GRAU model achieved the average values of 0.022,0.016,and 0.995 in terms of Mean Absolute Error(MAE),Root Mean Square Error(RMSE),and R2 score,respectively.The model proposed in this study improved the MAE by 26％-31％compared with GRU and Transformer models and had good generalization performance in different environ-ments.
wind power predictionvariational mode decompositionattention mechanismregularization
万方数据
