Informer learning framework based on secondary decomposition for multi-step forecast of ultra-short term wind speed

Abstract

Accurate and dependable wind speed prediction holds paramount importance in facilitating the dispatch and safe operation of power systems. Nonetheless, the inherent instability of wind speed makes wind speed prediction challenging. Consequently, a short-term wind speed prediction framework, amalgamating secondary decomposition (SD)-Informer, has been proposed in this paper. Initially, the variational mode decomposition (VMD) is applied to decompose the primary wind speed sequence. Through the VMD feature decomposition module, it effectively filters and eliminates superfluous noise from wind speed data. Subsequently, the complete ensemble empirical mode decomposition with adaptive noise technique is introduced for a secondary decomposition targeting the high-frequency components derived from the initial decomposition. To address the limitation of neural network models in capturing essential information from lengthy sequential data concurrently, a predictive model based on Informer is proposed as wind speed prediction module, thereby enhancing prediction accuracy. The validation of this hybrid model encompasses four distinct time ranges. Multiple models are scrutinized through comparative analysis to ascertain the superior performance of the proposed hybrid model. The root mean square error of the proposed method is reduced by 33.02%、25.46%、24.26%, and 23.12% compared to gate recurrent unit (GRU), vision Transformer (ViT), attention (AT)-ViT, and CNN-atteneion (CA)-Bi-directional long short-term memory (BiLSTM) respectively. The mean absolute error of the proposed method in the first quarter is 0.432, with model comparison values reduction of 36.19%、22.99%、20.44%, and17.71% respectively. The experimental results indicate that the proposed model exhibits a strong capability in capturing the long-term dependencies between the input and output sequences of wind speed. It can perform multi-step predictions while ensuring high prediction accuracy.