Wind Speed Prediction Method Based on Combined Deep Learning Model - Zeng, Chuxi; Xia, Zhipnig; Zhou, Yihuan; Li, Xi (2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP), 2024)
The necessity of precise wind speed forecasts is crucial for integrating wind turbines into microgrids. This study introduces a hybrid forecast approach that initially optimizes the key factors of variational modal decomposition (VMD) through the whale optimization algorithm (WOA). Then decomposes the initial wind speed data into intrinsic mode functions (IMFs) using the WOA-VMD method. Secondly, the Pearson correlation coefficient is conducted between IMFs and the initial wind speed data, classifying the IMFs into two groups according to their correlation coefficients. IMFs closely correlated are forecasted utilizing a temporal convolutional network (TCN), while those less correlated are forecasted via a gate recurrent unit (GRU). Finally, the forecasting outcomes from each category are then aggregated to formulate the ultimate forecast results. The hybrid WOA-VMD-GRU-TCN model is tested on actual data from six wind farms across China, the results demonstrate that the proposed model markedly enhances forecast precision, minimizes wind speed prediction errors, and exhibits superior robustness compared to alternative methods.