A Wind Speed Prediction Method Based on Signal Decomposition Technology Deep Learning Model

Jie Du, Shuaizhi Chen (), Linlin Pan and Yubao Liu
Additional contact information
Jie Du: School of Software, Nanjing University of Information Science & Technology, Nanjing 210044, China
Shuaizhi Chen: School of Software, Nanjing University of Information Science & Technology, Nanjing 210044, China
Linlin Pan: China Electric Power Research Institute, Beijing 100192, China
Yubao Liu: Precision Regional Earth Modeling and Information Center, Nanjing University of Information Science & Technology, Nanjing 210044, China

Energies, 2025, vol. 18, issue 5, 1-26

Abstract: Accurate and reliable wind speed prediction plays a significant role in ensuring the reasonable scheduling of wind power resources. However, wind speed sequences often exhibit complex characteristics such as instability and volatility, which create substantial challenges for prediction. In order to cope with these challenges, a multi-step wind speed prediction method based on secondary decomposition (SD) techniques and deep learning prediction models is proposed in this paper. First, the original signal was decomposed into multiple sequences by using two signal decomposition techniques, multi-scale wavelet power spectrum analysis (MWPSA) and variational mode decomposition (VMD). Second, a model was constructed by combining convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) networks, and attention mechanism to perform multi-step wind speed predicting for each sequence, and the model parameters were optimized by the particle swarm optimization (PSO) algorithm. Ultimately, the results from all sequences were combined to generate the final wind speed prediction. The predictive performance of the proposed method was evaluated using real wind speed data collected from a wind farm in China. Experimental results show that the proposed method significantly outperforms other comparison models in multi-step wind speed prediction, which highlights its accuracy and reliability.

Keywords: wind speed predicting; signal decomposition; hybrid deep learning model; particle swarm optimization algorithm (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: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/5/1136/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/5/1136/ (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:18:y:2025:i:5:p:1136-:d:1599642

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 ().