A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting. (1st February 2021) - da Silva, Ramon Gomes; Ribeiro, Matheus Henrique Dal Molin; Moreno, Sinvaldo Rodrigues; Mariani, Viviana Cocco; Coelho, Leandro dos Santos
Abstract: Wind energy is one of the sources which is still in development in Brazil. However, it already represents 17% of the National Interconnected System. Due to the high level of uncertainty and fluctuations in wind speed, predicting wind energy with high accuracy is challenging. In this context, this paper proposes a novel decomposition-ensemble learning approach that combines Complete Ensemble Empirical Mode Decomposition (CEEMD) and Stacking-ensemble learning (STACK) based on Machine Learning algorithms to forecast the wind energy of a turbine in a wind farm at Parazinho city, Brazil, using multi-step-ahead forecasting strategy. The approached forecasting models were k -Nearest Neighbors, Partial Least Squares Regression, Ridge Regression, Support Vector Regression, and Cubist Regression. Additionally, Box-Cox transformation, correlation matrix, and principal component analysis were used to pre-process the data. The performance of the proposed forecasting models was evaluated by using three performance metrics: mean absolute error, mean absolute percentage error, and root mean square error, and the Diebold-Mariano statistical test to evaluate the forecasting error signals. The proposed models outperform the CEEMD, STACK, and single models in all forecasting horizons, with a performance improvement that ranges 0.06%–97.53%. Indeed, the decomposition-ensemble learning model is an efficient and accurate model for wind energy forecasting. Graphical abstract: Image 1 Highlights: A novel decomposition-ensemble learning model is proposed for wind energy forecasting. CEEMD method deal with the non-linearity and non-stationarity of the time series. Different preprocessing approaches are employed to deal with the high-correlation of the system's inputs. STACK approach by divide-and-conquer scheme takes advantages of the models. Proposed model improves the accuracy of wind energy forecasting multi-step ahead.