Abstract: Comprehensively considering the characteristics of wind power series and the multi-dimensional meteorological data,a dimensionality reduction method of elastic net improved kernel principal component analysis（EN-SKPCA） is proposed. The dimensionality of meteorological factors is reduced and expressed as a regression optimization problem. The added elastic network penalty solve the problem that the KPCA reconstruction principal component is difficult to explain. The flower pollination algorithm（FPA）is proposed to optimize the long-short-term memory neural network（LSTMNN）prediction. The model can automatically select the best super parameters and reduce the randomness caused by the empirical setting of parameters. The method solves the influence of abrupt weather and improves the prediction accuracy. The superiority of this method is proved by the experiment on the measured data of Mahuangshan No.1 wind farm in Ningxia in 2017.