Short-Term Wind Power Prediction Model Based on Secondary Decomposition and IDBO-DABiLSTM
To improve the accuracy of wind power prediction,a new wind power prediction model based on secondary decomposition and Improved Dung Beetle Optimizer(IDBO)-Dual Attention Bidirectional Long Short-Term Memory(DABiLSTM)network is proposed for the high stochasticity and strong volatility of wind power.First,a secondary decomposition method based on the Complementary Ensemble Empirical Mode Decomposition of Adaptive Noise(CEEMDAN)and Wavelet Packet Decomposition(WPD)is proposed to disaggregate the raw wind power historical data and windspeed historical data,thereby reducing the randomness and volatility of the raw signals.Second,a DABiLSTM network model is established by incorporating feature and time attention mechanisms.This model fully explores the correlation between features and long-term dependency between time series,thereby improving the accuracy of wind power prediction.Finally,IDBO is proposed based on the golden sine algorithm to improve the rolling ball dung beetle position and enhance the local development and global exploration capabilities of the algorithm.In addition,a dynamic weighting factor is incorporated to improve the stealing cockroach position and balance the global exploration and local development capabilities of the algorithm.IDBO is employed for intelligent optimization of the network working hyperparameters of the DABiLSTM model to further enhance the prediction precision of the model.The proposed model is tested using real data from a Guizhou wind farm,and the findings demonstrate that the proposed approach can successfully increase the predictive power of the model.The Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values of the proposed model are 0.0449 and 0.0312,respectively,in single-step prediction,which is a reduction by 36.9％and 31.7％on average,respectively,compared with other models in the literature,thus showing better prediction accuracy and robustness.
wind power predictionsecondary decompositionBidirectional Long Short-Term Memory(BiLSTM)networkImproved Dung Beetle Optimizer(IDBO)attention mechanism
万方数据
维普
