Ping Fang

TL;DR: Through the application on three datasets collected from Sotavento Galicia with different prediction horizons and comparison with six related models, it is attested that the proposed hybrid prediction model is effective and suitable for multi-step short-term wind speed forecasting.

TL;DR: The experimental results illustrate that the data preprocessing strategy integrating TVF-EMD and FE-based subseries aggregation contributes to balancing forecasting performance and timing computation properly and the application of REC possesses positive effects on further compensating the ultimate forecasting results.

TL;DR: A novel hybrid framework consisting of variational mode decomposition (VMD), phase space reconstruction (PSR), improved beetle antenna search (BAS) and Volterra series model is established for multi-step ahead short-term wind speed forecasting and the prediction precision is significantly improved and the proposed DEBAS algorithm achieves the best performance.

TL;DR: A blended approach incorporating time varying filtering-based empirical mode decomposition, phase space reconstruction, and no negative constraint theory-based multi-model fusion and hierarchy-based merged optimization algorithm is proposed to enhance the wind speed forecasting accuracy.

TL;DR: Wang et al. as mentioned in this paper proposed a compositive architecture incorporating three modules: data preprocessing, several individual predictors and Volterra multi-model fusion with enhanced multi-objective optimization algorithm.