Short-term Wind Power Prediction based on CEEMDAN-TCN

Abstract

In order to improve the accuracy of wind power prediction, a wind power prediction method based on time series decomposition and error correction is proposed in this paper. Firstly, the maximum information coefficient (MIC) method is used to select the features with strong correlation with wind power, so as to reduce the complexity of the original data; Then, according to the non-stationary characteristics of wind power, the wind power is decomposed into several stationary subsequences by using adaptive noise complete set empirical mode decomposition (CEEMDAN); Finally, the time convolution network (TCN) is used to dynamically model the multivariable time series of wind power; In order to further improve the prediction accuracy, a light quantization hoist (LightGBM) is introduced to correct the error of the prediction value. The simulation results show that the proposed method has higher short-term wind power prediction accuracy than other prediction models.