Multi-Step Short-Term Wind Speed Prediction Models Based on Adaptive Robust Decomposition Coupled with Deep Gated Recurrent Unit

Kui Yang, Bofu Wang, Xiang Qiu, Jiahua Li, Yuze Wang and Yulu Liu
Additional contact information
Kui Yang: School of Science, Shanghai Institute of Technology, Shanghai 201418, China
Bofu Wang: Shanghai Key Laboratory of Mechanics in Energy Engineering, Shanghai Institute of Applied Mathematics and Mechanics, School of Mechanics and Engineering Science, Shanghai Frontiers Science Base for Mechanoinfomatics, Shanghai University, Shanghai 200072, China
Xiang Qiu: School of Science, Shanghai Institute of Technology, Shanghai 201418, China
Jiahua Li: School of Urban Construction and Safety Engineering, Shanghai Institute of Technology, Shanghai 201418, China
Yuze Wang: School of Mechanical Engineering, Shanghai Institute of Technology, Shanghai 201418, China
Yulu Liu: School of Science, Shanghai Institute of Technology, Shanghai 201418, China

Energies, 2022, vol. 15, issue 12, 1-24

Abstract: Accurate wind speed prediction is a premise that guarantees the reliable operation of the power grid. This study presents a combined prediction model that integrates data preprocessing, cascade optimization, and deep learning prediction to improve prediction performance. In data preprocessing, the wavelet soft threshold denoising (WSTD) is employed to filter the blurring noise of the original data. Then, the robust empirical mode decomposition (REMD) and adaptive variational mode decomposition (AVMD) are adopted to carry out a two-stage adaptive decomposition. Spearman correlation is used to quantify the mode that need to be decomposed for the second time. In the cascade optimization, the hybrid grey wolf algorithm (HGWO) is employed to optimize the parameters of the VMD and the gated recurrent unit (GRU), which overcomes the problem of empirical parameter adjustment. The HGWO is also adopted in the prediction strategy to optimize the GRU model to predict the grouped intrinsic mode functions (IMFs). Lastly, the final wind speed prediction result is obtained by superimposing the values of all the predicted models. The proposed model was validated with the measured wind speed data of the four quarters in the Bay area of China and was compared with 20 models of the classic method to further evaluate the effectiveness of the model. The results show that the whole process of the proposed model is adaptive, the final multi-step prediction performance is good, and high prediction accuracy can be attained.

Keywords: wind speed prediction; wavelet soft threshold denoising; robust empirical mode decomposition; cascade optimization strategy; deep gated recurrent unit; adaptive model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/12/4221/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/12/4221/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:12:p:4221-:d:834146

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().