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Spatio-temporal Model Combining VMD and AM for Wind Speed Prediction

Yingnan Zhao1,*, Peiyuan Ji1, Fei Chen1, Guanlan Ji1, Sunil Kumar Jha2

1 Nanjing University of Information Science and Technology, School of Computer and Software, Nanjing, 210044, China
2 IT Fundamentals and Education Technologies Applications, University of Information Technology and Management in Rzeszow, Rzeszow, Voivodeship, 100031, Poland

* Corresponding Author: Yingnan Zhao. Email: email

Intelligent Automation & Soft Computing 2022, 34(2), 1001-1016. https://doi.org/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 short-term prediction on the actual wind power dataset prove that VCGA is superior to prior algorithms.

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APA Style
Zhao, Y., Ji, P., Chen, F., Ji, G., Jha, S.K. (2022). Spatio-temporal model combining VMD and AM for wind speed prediction. Intelligent Automation & Soft Computing, 34(2), 1001-1016. https://doi.org/10.32604/iasc.2022.027710
Vancouver Style
Zhao Y, Ji P, Chen F, Ji G, Jha SK. Spatio-temporal model combining VMD and AM for wind speed prediction. Intell Automat Soft Comput . 2022;34(2):1001-1016 https://doi.org/10.32604/iasc.2022.027710
IEEE Style
Y. Zhao, P. Ji, F. Chen, G. Ji, and S.K. Jha "Spatio-temporal Model Combining VMD and AM for Wind Speed Prediction," Intell. Automat. Soft Comput. , vol. 34, no. 2, pp. 1001-1016. 2022. https://doi.org/10.32604/iasc.2022.027710



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