Short-term wind speed prediction: Hybrid of ensemble empirical mode decomposition, feature selection and error correction

Accurately forecasting wind speed is a critical mission for the exploitation and utilization of wind power. To improve the prediction accuracy, the nonlinearity and nonstationarity embedded in wind speed time series should be reduced. Because the subseries has less nonlinearity and nonstationarity after decomposition, the decomposition-based forecasting methods are widely adopted to provide the higher predictive accuracy. However, latest studies showed the real time decomposition-based forecasting methods could be worse than the single forecasting models. The aim of this study is to improve the performance of the real time decomposition-based forecasting method after the factors attributed to its unsatisfactory performance are uncovered. In this paper, the feature selection and error correction are adopted in the real time decomposition-based forecasting method to enhance the prediction accuracy. In the proposed method, the raw wind speed time series is decomposed into a number of different subseries by ensemble empirical mode decomposition; then two feature selection methods including kernel density estimation-based Kullback-Leibler divergence and energy measure are used to reduce the disturbance of illusive components; further the least squares support vector machine is adopted to establish the one-step ahead forecasting models for the remaining subseries; finally, the hybrid of least squares support vector machine and generalized auto-regressive conditionally heteroscedastic model is introduced to correct resulting error component if its inherent correlation and heteroscedasticity cannot be neglected. Based on two sets of measured data, the results of this study show that: (1) the real time decomposition-based method may be ineffective in practice; (2) both the feature selection and error correction can improve forecasting performance in comparison with the real time decomposition-based method; (3) compared with other involved methods, the proposed hybrid method has the satisfactory performance in both accuracy and stability. (C) 2017 Elsevier Ltd. All rights reserved.