A hybrid wind speed forecasting model based on a decomposition method and an improved regularized extreme learning machine

Abstract

Abstract Accurate wind speed forecasting plays a critical role in wind farm planning for economic operation of power grid. New forecasting methods which lead to better performances are urgently demanded for the wind energy management as there is no one model that is qualified in all cases. In this paper, a novel hybrid model is developed by integrating Fast Ensemble Empirical Mode Decomposition (FEEMD) and Regularized Extreme Learning Machine (RELM) optimized by Backtracking Search Algorithm (BSA) to realize the accurate short-term wind speed forecasting. This hybrid model can be partitioned into three phases: data preprocessing, training RELM by BSA, and aggregated calculation. Experiments have been conducted under different forecasting horizons and seasons with the proposed method as well as five benchmark models (RBF, GRNN, RELM, FEEMD-RELM, and BSA-RELM), respectively. Experimental results show the proposed model reduces RMSE by about 60% on average for all four cases than single RELM and it is superior to all the involved models in short-term wind speed forecasting.