Short-term wind power intervals prediction based on generalized morphological filter and artificial bee colony neural network

Wind power generation has the characteristics of intermittency and uncertainty. It is significant to predict intervals of short-term wind power precisely for optimizing the grid power system operation scheduling and reserve capacity. The paper proposed a simple short-term wind power intervals prediction model based on artificial bee colony-neural network (ABC-NN). A new criterion was developed in this method to get better prediction results. Then generalized morphological filter was applied in the de-noising of wind power time sequence by analyzing of wind power constitute components. The simulation results demonstrate that the new method has a better performance in wind power intervals prediction.