A short-term wind power prediction model based on CEEMD and WOA-KELM. (April 2022) - Ding, Yunfei; Chen, Zijun; Zhang, Hongwei; Wang, Xin; Guo, Ying
Abstract: Effective short-term wind power prediction is crucial to the optimal dispatching, system stability, and operation cost control of a power system. In order to deal with the intermittent and fluctuating characteristics of the wind power time series signals, a hybrid forecasting model is proposed, based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Whale Optimization Algorithm (WOA)-Kernel Extreme Learning Machine (KELM), to predict short-term wind power. Firstly, the non-stationary wind power time series was decomposed into a series of relatively stationary components by CEEMD. Then, the components were used as the training set for the KELM prediction model, in which the initial values and thresholds were optimized by WOA. Finally, the predicted output values of each component were superimposed, to obtain the final prediction of the wind power values. The experimental results show that the proposed prediction method can reduce the complexity of the prediction with a small reconstruction error. Furthermore, performance is greater, in terms of prediction accuracy and stability, with lower computational costs than other benchmark models.