A novel hybrid model based on Bernstein polynomial with mixture of Gaussians for wind power forecasting

In recent years, the effective application of wind power forecasting in power system has been approved. However, owing to the intermittence and nonlinearity of wind power time series, accurate wind power forecasting is difficult for traditional forecasting methods. To improve the accuracy and stability of wind power forecasting, a new hybrid forecasting model is proposed in this study. The original wind power series is first decomposed into several intrinsic mode functions by complete ensemble empirical mode decomposition, and then a Bernstein polynomial forecasting model with mixture of Gaussians is constructed. Finally, a population based multi-objective state transition algorithm with parallel search mechanism is developed to optimize the parameters of the hybrid model. To verify the effectiveness of the proposed hybrid forecasting model, a large number of comprehensive experiments are carried out with wind power data from a wind farm in Xinjiang, China. The experimental results show that the proposed hybrid model has higher forecasting accuracy and stronger stability compared with other popular forecasting models.