A Short-term Wind Power Prediction Framework based on Two-layer Decomposition and the Combination of Ensemble Model and Deep Network

Abstract

The time series of wind power is influenced by many external factors, showing strong volatility and randomness. Aiming at the problem of low prediction accuracy of wind power time series, this paper proposes a short-term wind power prediction framework based on two-layer decomposition and the combination of ensemble model and deep network, which is composed of complete ensemble empirical mode decomposition (CEEMD), sample entropy (SE), stacking ensemble, linear regression (LR), variational mode decomposition (VMD), long short term memory (LSTM) and multi-layer perceptron (MLP). Firstly, CEEMD is used to decompose the time series of wind power into different modes and then SE is used for reconstruction. Secondly, different models are applied to predict the different reconstruction components and select the optimal model. Subsequently, VMD is used to decompose the partially decomposed reconstruction components and a combined prediction model of stacking ensemble and LSTM is established. Finally, in order to further improve the prediction accuracy, MLP is applied to correct the error and the corrected error is superimposed with the prediction results and other reconstruction components to obtain the final predicted value. The simulation results show that the accuracy and effectiveness of this model is superior to the traditional model and the prediction accuracy of short-term wind power time series is improved effectively.