 A hybrid wind power forecasting approach based on Bayesian model averaging and ensemble learningGang Wang, Ru Jia, Jinhai Liu and Huaguang Zhang Renewable Energy, 2020, vol. 145, issue C, 2426-2434 Abstract: In this paper, a hybrid wind power forecasting approach based on Bayesian model averaging and Ensemble learning (BMA-EL) is proposed. Firstly, SOM clustering and K-fold cross-validation are used to generate multiple sets of the training subsets with the same distribution from the training set of meteorological data to increase the difference of the input samples of the base learners. These training subsets are imported into three base learners, i.e. BPNN, RBFNN, and SVM, to train the model. Then, the BMA combining strategy is trained based on the outputs of the three base learners on the validation set. Finally, the test set is combined by the BMA through the outputs of the three base learners to obtain the WPF results. By comparing the simulation error and curve between the base learner and other literature approaches, our proposed method can accurately and reliably forecast the wind power outputs under different meteorological conditions, with higher precision and reliability. Keywords: Wind power forecasting; Bayesian model averaging; Ensemble learning; Neural network; Hybrid methodology (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:145:y:2020:i:c:p:2426-2434 DOI: 10.1016/j.renene.2019.07.166 Access Statistics for this article Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides More articles in Renewable Energy from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.