In order to improve the accuracy of traditional data-driven machine learning models in short-term wind power prediction, a short-term wind power prediction model based on ikPCA-FABAS-KELM is proposed. Firstly, an improvement is made to principal component analysis, proposing reversible kernel principal component analysis (ikPCA), which reduces the complexity of input data while ensuring data features, in order to improve the running speed of the model; Secondly, the introduction of individual attraction strategies for fireflies improves the Tenebrio algorithm and proposes the FABAS algorithm; Finally, the FABAS algorithm is used to optimize the regularization parameter C and kernel parameters γ of the kernel limit learning machine, reducing the impact of manual parameter setting on blind model training and improving model prediction accuracy. The simulation results show that the proposed prediction model effectively improves the prediction accuracy of traditional models.