基于数学形态学聚类与果蝇优化算法的风电功率短期预测

In order to solve the problems of training data selection and parameter optimization of the prediction model, this paper introduces a method of the short-term prediction of wind power generating capacity based on mathematical morphology cluster analysis and fruit fly optimization algorithm. Mathematical morphology cluster analysis automatically divides numerical weather prediction data into several clusters according to dilation and erosion operation, and similar days with the predicted day are searched as training sample. Fruit fly optimization algorithm can determine quickly the optimization parameters of the prediction model. Simulation is performed to the wind power generation of Yilan wind farm. The results show that the method is effective and its precision is higher than that of the prediction model based on k-mean cluster analysis or particle swarm optimization algorithm. The effect of training data on model accuracy is higher than that of model optimization.