VMD-HPCA-GRU ultra-short-term wind power prediction based on COOT algorithm

In order to improve the prediction accuracy of ultra-short-term wind power, a combined prediction model based on variational modal decomposition (VMD), hierarchical principal component analysis (HPCA), and gated recurrent unit (GRU) neural network optimized by COOT algorithm was proposed. Firstly, the submode number of VMD was determined by the energy difference method so that the original power sequence with strong nonlinearity was decomposed into a set of relatively stationary submodes. Secondly, the correlation degree value between high-dimensional meteorological features and power sequence was calculated by gray relation analysis, and the ranking and stratification were carried out. The first principal component of feature variables in each layer was extracted by principal component analysis (PCA) to realize the dimensionality reduction of high-dimensional meteorological features. Finally, the COOT algorithm was introduced to optimize the hyperparameters of the GRU prediction model, accelerate the model convergence speed, and improve the model prediction accuracy. Simulation analysis was carried out on the measured data of a wind farm in Guizhou Province, and the results show that compared with the prediction results of the traditional GRU model, the root mean square error, mean absolute error, and mean absolute percentage error of the proposed method are reduced by 67.41%, 72.25%, and 45.69%, respectively, and the prediction accuracy of the proposed method is higher than that of the other four combined prediction models, which effectively improves the prediction accuracy of ultra-short-term wind power.