Recurrent neural network wind power prediction based on variational modal decomposition improvement

Abstract

In order to avoid the problem that the traditional recurrent neural network (RNN) wind power prediction model cannot take into account both the law of wind power variation and the impact of sudden change factors, this paper proposes an improved cyclic neural network wind power prediction model based on variational modal decomposition (VMD). The VMD algorithm is used to decompose the output power of wind power into different frequency components and analyze the impact of different frequency components on the prediction model. Combined with the feature extraction ability of the neural network, it can reduce the impact of abrupt abnormal data on the prediction results and improve the real-time prediction accuracy of wind power. According to the historical data of an actual wind farm, the results show that the accuracy of the wind power prediction model based on VMD and the recurrent neural network is more than 85%, which is superior to the traditional RNN and the standard long short term memory wind power prediction model.