A novel wind speed prediction model based on neural networks, wavelet transformation, mutual information, and coot optimization algorithm

Wind is a renewable, sustainable, and clean source of energy. This has led to wind gaining a lot of attention in recent decades as a reliable alternative to fossil fuels. However, wind speed fluctuations complicate its integration with power grids. To tackle this issue, this paper proposes a new win...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Scientific reports Jg. 15; H. 1; S. 10860 - 22
Hauptverfasser: Amirteimoury, Faezeh, Keynia, Farshid, Amirteimoury, Elaheh, Memarzadeh, Gholamreza, Shabanian, Hanieh
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 29.03.2025
Nature Publishing Group
Nature Portfolio
Schlagworte:
639/166/4073
639/166/987
Algorithms
Fossil fuels
Humanities and Social Sciences
Long short-term memory
multidisciplinary
Neural networks
Optimization algorithms
Prediction models
Science
Science (multidisciplinary)
Wavelet transforms
Wind speed
ISSN:2045-2322, 2045-2322
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Wind is a renewable, sustainable, and clean source of energy. This has led to wind gaining a lot of attention in recent decades as a reliable alternative to fossil fuels. However, wind speed fluctuations complicate its integration with power grids. To tackle this issue, this paper proposes a new wind speed prediction model that combines four techniques: Discrete Wavelet Transform, which smooths the wind speed signal; Mutual Information, which selects the most informative part of the wind speed time series; Coot Optimization Algorithm for optimal feature selection; and Bidirectional Long Short-Term Memory for capturing complex patterns. To evaluate the efficiency of the proposed model, its performance was measured using error metrics such as mean squared error, mean absolute error, mean absolute percentage error, coefficient of determination ( ), and median absolute error. The proposed model was examined using two different wind speed datasets and achieved high prediction accuracy. Additionally, 14 different benchmark models were created, and their prediction results were compared with those of the proposed model. A comparison between the results of the proposed model and benchmark models demonstrated the superiority of the proposed model.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-94082-2