Research on enhancing short-term wind power forecasting through feature fusion in a hybrid deep learning framework
X
Xianlong Su
J
Jinming Gao
(Department of Biomedical Engineering)
K
Kai Han
(Key Laboratory of Medical Molecule Science and Pharmaceutics Engineering, Ministry of Industry and Information Technology, School of Chemistry and Chemical Engineering, Center for Quantum Technology Research and School of Physics)
H
Hankil Kim
H
Hoekyung Jung
Article Details
Journal
Scientific Reports
Volume / Issue
Vol. 16, Issue 1
Published
February 20, 2026
ISSN
2045-2322
Publisher
Nature Portfolio
Authors (5)
X
Xianlong Su
J
Jinming Gao
Department of Biomedical Engineering
K
Kai Han
Key Laboratory of Medical Molecule Science and Pharmaceutics Engineering, Ministry of Industry and Information Technology, School of Chemistry and Chemical Engineering, Center for Quantum Technology Research and School of Physics
H
Hankil Kim
H
Hoekyung Jung
Related Articles from this Journal
AI models of unstable flow exhibit hallucination
Ramdhan Wibawa, Birendra Jha
Aug 2026
10.1038/s41598-026-64023-8
The MCAA-YOLO + XPBI integrated model provides a hybrid intelligent measurement method for predicting the body weight of Hu sheep
Hang Zhang, Yuang Cheng et al.
Aug 2026
10.1038/s41598-026-64075-w
Application of iron nano-biofertilizers as a sustainable approach to enhance growth and micronutrient uptake in soybean
Ava Mohrazi, Reza Ghasemi-Fasaei
Aug 2026
10.1038/s41598-026-66011-4
Adolescents’ status-dependent public and private conformity to prosocial and antisocial decisions in a public goods game
Niloofar Saeedi, Khatereh Borhani et al.
Aug 2026
10.1038/s41598-026-64831-y
An explainable hierarchical capsule network framework for supporting expert sensory evaluation of herbal medicines with natural language generation
Hyein Lee, Dae-Hyun Jung
Aug 2026
10.1038/s41598-026-64156-w