An adaptive dynamic short-term wind speed forecasting model using secondary decomposition and an improved regularized extreme learning machine

Accurate and reliable multi-step wind speed forecasting is extremely crucial for the economic and safe operation of power systems. A novel dynamic hybrid model, which combines an adaptive secondary decomposition (ASD), a leave-one-out cross-validation-based regularized extreme learning machine (LRELM) and the backtracking search algorithm (BSA), is proposed to mitigate the practical difficulties of the traditional decomposition-ensemble forecasting models (DEFMs) through adaptive dynamic decomposing and modeling when new data is added. The new ASD method, which fuses ensemble empirical mode decomposition (EEMD), adaptive variational mode decomposition (AVMD) with sample entropy (SE), is developed for smoothing the raw series to reduce computational time as well as enhance generalization and stability of forecasting models. BSA is employed to optimize LRELM to overcome the drawback of instability. To validate its efficacy, the proposed model and thirteen benchmark models are compared by diverse lead-time forecasting of several real cases. Comprehensive comparisons with a coherent set of indices suggest that the proposed model is an effective and powerful tool for short-term wind speed forecasting not only from the perspective of reliability and sharpness but also from the view of overall skills. (C) 2018 Elsevier Ltd. All rights reserved.