A prediction model based on ikPCA-FABAS-KELM is proposed to improve the short-term wind power prediction by traditional data-driven machine learning models.First,the principal component analysis is improved and the reversible kernel Principal Component Analysis (ikPCA) is proposed to reduce the complexity of input data while ensuring data features,with the purpose to advance the model in running speed.Second,the individual attraction strategies for Firefly Algorithm (FA) are used to improve the Beetle Antennae Search (BAS) thus a FABAS algorithm is proposed.Finally,the FABAS algorithm is used to optimize the regularization parameter C and kernel parameters γ of the Kernel Extreme Learning Machine (KELM),which can reduce the impact of manual parameter setting on blind model training thus improve model prediction accuracy.The simulation results show that the proposed model effectively improves the short-term wind power prediction accuracy.