To accurately and reliably estimate the solar irradiance, a random forest algorithm was proposed based on the Bayesian model combination for solar irradiance prediction. Firstly, the K-means clustering and K-fold cross validation were introduced to generate multiple training subsets so as to increase the diversity of training subsets and to ensure uniform sampling. Secondly, the random forests were defined as base learners to establish an ensemble learning prediction model，with each training subset being used to train the corresponding individual random forest. Then, according to the prediction performance of each individual random forest on the verification set, the Bayesian model combination algorithm was applied to formulate the combination strategy. The prediction values of individual random forest on the test set were fused to the final output through the model combination strategy. Finally, the proposed method was applied to solve the solar irradiance prediction problem. Simulation experiments were carried out by measured meteorological data. Other four kinds of prediction methods were also introduced to establish the contrast experiments，and the accuracy and reliability of the proposed method in the solar irradiance prediction were verified by comparison results.