Yingnan Zhao1,*, Peiyuan Ji1, Fei Chen1, Guanlan Ji1, Sunil Kumar Jha2
Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1001-1016, 2022, DOI:10.32604/iasc.2022.027710
Abstract This paper proposes a spatio-temporal model (VCGA) based on variational mode decomposition (VMD) and attention mechanism. The proposed prediction model combines a squeeze-and-excitation network to extract spatial features and a gated recurrent unit to capture temporal dependencies. Primarily, the VMD can reduce the instability of the original wind speed data and the attention mechanism functions to strengthen the impact of important information. In addition, the VMD and attention mechanism act to avoid a decline in prediction accuracy. Finally, the VCGA trains the decomposition result and derives the final results after merging the prediction result of each component. Contrasting experiments for… More >
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