LSTM Model Combined with Rolling Empirical Mode Decomposition and Sample Entropy Reconstruction for Short-Term Wind Speed Forecasting

This research introduces a new hybrid forecasting approach based on a rolling decomposition–merging–prediction framework integrating Empirical Mode Decomposition (EMD), sample entropy, and Long Short-Term Memory (LSTM) to further enhance the accuracy of wind speed predictions. To avoid the information leakage issue caused by decomposing wind speed data, a rolling EMD method is applied to the framework to ensure that the data points to be predicted are excluded from the decomposition process. The input speed data of the prediction model are then decomposed into a series of Intrinsic Mode Functions (IMFs) and a residual component, capturing the local variation characteristics of the wind speed data. Next, the sample entropy method is employed to calculate the entropy values of these components, which are then reclassified and aggregated into three components based on their calculated entropy values, corresponding to high, medium, and low frequencies. The three reconstructed components are then employed as input features in an LSTM model for wind speed prediction. To demonstrate the effectiveness of the proposed model, experiments using three different datasets were conducted with wind speed data collected from a wind farm. The statistical experimental results indicate that the proposed EMD-LSTM achieves improvements in metrics of MAE, RMSE, and MAPE by at least 3.64%, 7.25%, and 5.02%, respectively, compared to other methods across the evaluated test datasets. Furthermore, the Wilcoxon test results provide additional evidence, confirming that the EMD-LSTM model exhibits a statistically significant advantage in prediction performance over the ARIMA, GRU, and SVM models.