A hybrid forecasting system with complexity identification and improved optimization for short-term wind speed prediction

Accurate wind speed prediction can relieve the pressure of peak regulation and frequency modulation of the power system and improve the acceptance capacity of wind power. In order to improve the forecasting accuracy of wind power, this paper proposes a hybrid wind power forecasting system. Firstly, the energy entropy theory (EVMD) is used to determine the number of VMD decompositions to solve the problem of VMD over-decomposition; secondly, the sample entropy (SE) is utilized to identify the complexity of the intrinsic mode functions (IMFs) of EVMD, and applied different methods to forecast. In addition, improved grey wolf optimizer (IGWO) is used to optimize the parameters of the prediction method. Finally, based on the kernel density estimation (KDE), this paper proposes to construct the prediction interval using the noise signal obtained by EVMD. Under the verification of two different datasets and comparative experiments, the MAPE of the deterministic prediction results reached 3.0985% and 7.1153% respectively. The coverage rate of nondeterministic prediction under 90% confidence reaches 95% and 96.67% respectively. The results show that the prediction effect of the proposed model is significantly better than that of other models, and it can provide strong support for the smooth operation of wind farms.