基于膨胀腐蚀聚类方法的风电功率预测

A new clustering method based on dilation and erosion is proposed and UCI (University of California Irvine) data set is used to carry out experimental simulation to prove the feasibility of this method. Then this clustering method is used to classify NWP (numerical weather prediction) information in wind power prediction, selecting the historical day data of the same type as the forecast day data as the training sample, and the generalized regression neural network is used to predict the power and compare with the direct prediction method. The simulation results show that the re-prediction has higher prediction accuracy after classification of historical day data based on dilation and erosion clustering analysis.