Multivariate short-term wind speed prediction based on PSO-VMD-SE-ICEEMDAN two-stage decomposition and Att-S2S

Abstract

To counter the challenges posed by the unpredictability of wind velocities on wind energy production, a wind speed prediction model combining chaotic mapping-based particle swarm optimization with variational modal decomposition (CPSO-VMD), sample entropy (SE), improved completely integrated empirical modal decomposition with adaptive noise (ICEEMDAN) two-layer decomposition, and attention mechanism-based sequence to sequence (Att-S2S) method is proposed. Firstly, the Lasso method is employed to filter the features that significantly contribute to the wind speed data, fully considering hidden relevant information and eliminating redundant data to improve the prediction accuracy; Secondly, CPSO optimized by introducing chaotic mapping determines the parameter pairs for VMD. This facilitates an adaptive VMD breakdown of the wind speed sequence, aiding in noise removal and selection of the intrinsic mode function (IMF) with the highest sample entropy for further decomposition using ICEEMDAN. In light of this, a sequence-to-sequence method based on the attention mechanism is suggested, which may highlight the influence features' effects on IMF; Finally, the proposed model is implemented at both the Paso Robles wind farm and the Oasis wind farm for practical assessment and benchmarked against other models. Overall, the proposed model outperforms the other models in these trials.