A Novel Hybrid Model for Wind Speed Prediction Based on VMD and Neural Network Considering Atmospheric Uncertainties

Abstract

Considering the strong fluctuation and the nonlinearity of wind speed, and atmospheric uncertainties, wind speed prediction based on the hybrid model is presented, which is composed of the neural network, variational mode decomposition (VMD), and Lorenz disturbance. First, the VMD is used to process the data to get several intrinsic mode functions (IMFs). Second, the neural network model (NN model) can be established by these IMFs of the training set, and the validation set is used to adjust the model parameters. Subsequently, given the nonlinearity of wind speed, Lorenz disturbance is added to determine the finial model, and the best Lorenz disturbance parameter and the best Lorenz disturbance sequence can be obtained by minimizing the mean absolute error of validation set. At last, the wind speed can be forecasted by the hybrid model. Taking Sotavento wind farm in Spain as an example, the results show that, the hybrid model has stable prediction performance, and the distribution characteristics of its results are consistent with the actual wind speed. The general model only focuses on improving prediction accuracy. However, on the basis of improving the forecasting accuracy, the proposed model not only enhances the prediction stability, but also restores the characteristics of wind speed. This research work provides a more scientific basis for wind power dispatching arrangement, and it is of great significance to improve the utilization rate of wind power.