Research on Wind Power Short-Term Forecasting Method Based on Temporal Convolutional Neural Network and Variational Modal Decomposition

Abstract

Wind energy reserves are large worldwide, but their randomness and volatility hinder wind power development. To promote the utilization of wind energy and improve the accuracy of wind power prediction, we comprehensively consider the influence of wind farm environmental factors and historical power on wind power generation. This paper presents a short-term wind power prediction model based on time convolution neural network (TCN) and variational mode decomposition (VMD). First, due to the non-smooth characteristics of the wind farm environmental data, this paper uses VMD to decompose the data of each environmental variable to reduce the influence of the random noise of the data on the prediction model. Then, the modal components with rich feature information are extracted according to the Pearson correlation coefficient and Maximal information coefficient (MIC) between each modal component and the power. Thirdly, a prediction model based on TCN is trained according to the preferred modal components and historical power data to achieve accurate short-term wind power prediction. In this paper, the model is trained and tested with a public wind power dataset provided by the Spanish Power Company. The simulation results show that the model has higher prediction accuracy, with MAPE and R2 are 2.79% and 0.9985, respectively. Compared with the conventional long short-term neural network (LSTM) model, the model in this paper has good prediction accuracy and robustness.

Keywords

• wind power short-term forecasting
• temporal convolutional neural network
• variational modal decomposition
• power system