Ultra-short term wind prediction with wavelet transform, deep belief network and ensemble learning.

• Models uniting wavelet transform, deep learning and ensemble learning were proposed. • Tree-based models facilitate the prediction of DBN. • Feature extension of DBN improve the prediction accuracy of tree-based models. • After extraction, bagging and boosting algorithm vary in accuracy and stability. The utilization of wind power is influenced by the fluctuation of the wind, to further strengthen the prediction accuracy of wind speed, two novel hybrid models uniting signal processing, deep learning and ensemble learning were proposed. Firstly, the wind speed series was disaggregated by the wavelet transform (WT). Then to enhance the forecasting precision of the subseries, the deep belief network (DBN) was applied to extract the high dimensional features. Besides, to overcome the limitation of the conventional DBN, the forecasts for each subseries processed by DBN were executed by the light gradient boosting machine (LGBM) and the random forest (RF). Some experiments have been accomplished, where the promotion of high dimensional feature extraction through DBN was explored. Meanwhile, the development of forecasting accuracy by applying tree-based models was confirmed, and the differences between these two hybrid models were discussed. It is shown that: (1) In comparison with the persistence method, the Elman neural network (ENN), DBN, LGBM, and RF, the hybrid models show a great boost in prediction accuracy. (2) The high dimensional feature extraction through DBN is in favor of improving the predicting accuracy of tree-based models, and tree-based models would facilitate the prediction. (3) Between the proposed models, the hybrid model integrated with RF slightly outperforms the other with LGBM in prediction accuracy, but the one with LGBM gets more stable predictions. [ABSTRACT FROM AUTHOR]