Short-Term Wind Power Prediction Based on CEEMDAN and Parallel CNN-LSTM

Abstract

To improve the accuracy of short-term Wind Power Prediction (WPP), a novel short-term WPP method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Fine-To-Coarse (FTC), and parallel CNN-LSTM is proposed in this paper. In the first stage, the CEEMDAN signal decomposition method is introduced to decompose wind speed sequence in different height into IMF components and the FTC signal reconstruct method is used to reorganize IMF components into a high frequency component, a low frequency component, and a trend component. In the second stage, a novel parallel CNN-LSTM neural network architecture is proposed as WPP model, in which the input feature consists both three frequency components derived in the first stage and the original wind speed sequence in different height. The results show that the proposed method is an effective short-term WPP method which improves the prediction accuracy.