Multi-step short-term wind speed forecasting based on multi-stage decomposition coupled with stacking-ensemble learning approach

• Published in: International journal of electrical power & energy systems Vol. 143; p. 108504
• Main Authors: da Silva, Ramon Gomes, Moreno, Sinvaldo Rodrigues, Ribeiro, Matheus Henrique Dal Molin, Larcher, José Henrique Kleinübing, Mariani, Viviana Cocco, Coelho, Leandro dos Santos
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2022
• Subjects: Singular spectrum analysis; Stacking-ensemble learning; Time series forecasting; Variational mode decomposition; Wind speed; Singular spectrum analysis; Time series forecasting; Variational mode decomposition; Stacking-ensemble learning; Wind speed
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2022.108504

Wind energy is an emerging source of renewable energy in Brazil. Nevertheless, it already accounts for 17% of the National Interconnected Network. Due to the great intricacy of wind speed variations, it is difficult to predict wind energy with high accuracy. This research offers, in these circumstances, an ensemble learning model based on variational mode decomposition and singular spectrum analysis in decomposition in multiple stages, using stacking-ensemble learning. The proposed model is tested and applied in short-term wind speed data from a wind farm located in Parazinho in the northeast region of Brazil, using a multi-step-ahead forecasting strategy. The selected models for forecasting were the machine learning models partial least squares regression, k-nearest neighbors, cubist regression, support vector regression, and ridge regression. The results of the study were divided into three comparative experiments: comparisons with (i) dual decomposed models, (ii) single decomposed models, and (iii) decomposed models. Concerning performance improvement, in the first experiment, the model was compared to dual decomposition models with an average performance between 3.71% and 21.38%. In the second experiment, mean performance improved between 37.18 and 52.47 percent compared to single decomposition. Lastly, the proposed model delivered, on average, 54.98% better results than the models without decomposition. In summary, all compared models in all forecasting horizons were surpassed by the proposed model, with an average improvement between 3.69 and 56.61 percent, showing that the dual decomposition ensemble learning model is an effective and accurate approach for forecasting wind speed. [Display omitted] •A novel multi-stage decomposition ensemble learning model for wind speed forecasting.•VMD and SSA composes the decomposition combined with the STACK to forecast wind speed.•The results show the proposed model’ superiority over compared approaches.