A Novel Ensemble Model Based on an Advanced Optimization Algorithm for Wind Speed Forecasting

Yukun Wang, Aiying Zhao (), Xiaoxue Wei and Ranran Li
Additional contact information
Yukun Wang: School of Economics and Management, Yanshan University, Qinhuangdao 066004, China
Aiying Zhao: School of Economics and Management, Yanshan University, Qinhuangdao 066004, China
Xiaoxue Wei: School of Economics and Management, Anhui Normal University, Wuhu 241000, China
Ranran Li: School of Economics and Management, Yanshan University, Qinhuangdao 066004, China

Energies, 2023, vol. 16, issue 14, 1-19

Abstract: Concerning the vision of achieving carbon neutral and peak carbon goals, wind energy is extremely important as a renewable and clean energy source. However, existing research ignores the implicit features of the data preprocessing technique and the role of the internal mechanism of the optimization algorithm, making it difficult to achieve high-accuracy prediction. To fill this gap, this study proposes a wind speed forecasting model that combines data denoising techniques, optimization algorithms, and machine learning algorithms. The model discusses the important parameters in the data decomposition technique, determines the best parameter values by comparing the model’s performance, and then decomposes and reconstructs the wind speed time series. In addition, a novel optimization algorithm is used to optimize the parameters of the machine learning algorithm using a waiting strategy and an aggressive strategy to improve the effectiveness of the model. Several control experiments were designed and implemented using 10-min wind speed data from three sites in Penglai, Shandong Province. Based on the numerical comparison results and the discussion of the proposed model, it is concluded that the developed model can obtain high accuracy and reliability of wind speed prediction in the short term relative to other comparative models and can have further applications in wind power plants.

Keywords: wind speed forecasting; selection of parameters; egret population optimization algorithm; ensemble 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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/14/5281/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/14/5281/ (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:16:y:2023:i:14:p:5281-:d:1190784

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 ().