Decomposition-Selection-Ensemble Prediction System for Short-Term Wind Speed Forecasting

Wind energy is a clean, efficient, and eco-friendly energy with promising application prospects. In the wind power system, reliable wind speed forecasting is crucial as it will better integrate wind energy into the power system and boost the safe operation of power grid. Scholars have developed several wind speed prediction methods; however, these methods usually ignore the importance of sub-model selection and interval prediction. In this study, a novel decomposition-selection-ensemble prediction system, which comprises a decomposition strategy, sub-model selection, system optimization, prediction, and assessment, is proposed to simultaneously conduct point and interval prediction. The proposed system successfully integrates the merits of component models and effectively overcomes the disadvantages of traditional prediction methods. The simulation result reveals that the proposed system can assign an optimal model for each sub-series and significantly promote an improvement of wind speed forecasting ability, demonstrating its superior application value in the scheduling and management of power systems.

Automated summary

In this study, a novel wind speed prediction system is proposed, which can conduct point and interval prediction simultaneously. The system successfully integrates the merits of component models and effectively overcomes the disadvantages of traditional prediction methods. Simulation results demonstrate its important application value in the scheduling and management of power systems.