An ensemble learning model based on Bayesian model combination for solar energy prediction

Abstract

To improve the reliability of solar irradiance prediction methods, an ensemble learning method based on the Bayesian model combination has been developed in this paper for solar utilization systems. First, a novel data sampling method has been proposed, including the advantages of clustering and cross validation, which can effectively ensure that the training subsets are different from each other and can cover a variety of different meteorological samples. Second, an ensemble learning model with multiple base learners has been designed. Each training subset is utilized to train the corresponding base learner. Then, a novel Bayesian model combination strategy expands hypothesis space E on Bayesian model averaging, which is applied to frame the combination strategy based on the accuracy of each base learner on the validation set. The prediction values of multiple learners are framed through the model combination strategy. Thus, a novel ensemble learning model based on Bayesian model combination has been established. Finally, experiments are carried out and the proposed method is compared with the Artificial Neural Network (ANN), K-means (Radial Basis Function) RBF, Support Vector Machine (SVM), and Multikernel SVM. The annual average mean absolute error of the ensemble learning method based on Bayesian model combination is reduced by 0.0374 MJ × m-2 compared with the ensemble learning method. The annual average mean absolute error of the proposed method is reduced by 42.6%, 38.2%, 52%, and 48.7%, respectively, compared with ANN, K-means RBF, SVM, and Multikernel SVM. The effectiveness as well as the reliability of the proposed method in solar energy prediction have been found to perform better and have verified our approach.