Ultra-short-term wind power prediction method based on optimized signal decomposition and deep learning Article Swipe

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Yong Sheng Wang , Jiajing Gao , Hongmei Xing , Guangchen Liu , Yongsheng Qi , Xuehui Wang , Fan Yang ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.1016/j.ijepes.2025.111417

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Deep Learning

Artificial Intelligence

Computer Science

Signal Processing

Electronic Engineering

Concepts

Deep learning Artificial intelligence Computer science Wind power Decomposition SIGNAL (programming language) Power (physics) Artificial neural network Hilbert–Huang transform Deep belief network Signal processing Electronic engineering Pattern recognition (psychology) Control theory (sociology) Algorithm Engineering Wind power forecasting Electric power system Control engineering Wind speed

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Type: article
Language: en
Landing Page: https://doi.org/10.1016/j.ijepes.2025.111417
OA Status: gold
References: 33
OpenAlex ID: https://openalex.org/W7106841108

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OpenAlex ID: https://openalex.org/W7106841108

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DOI: https://doi.org/10.1016/j.ijepes.2025.111417

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Title: Ultra-short-term wind power prediction method based on optimized signal decomposition and deep learning

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Type: article

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Language: en

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Publication year: 2025

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Publication date: 2025-11-27

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Authors: Yong Sheng Wang, Jiajing Gao, Hongmei Xing, Guangchen Liu, Yongsheng Qi, Xuehui Wang, Fan Yang

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Landing page: https://doi.org/10.1016/j.ijepes.2025.111417

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OA status: gold

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OA URL: https://doi.org/10.1016/j.ijepes.2025.111417

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Concepts: Deep learning, Artificial intelligence, Computer science, Wind power, Decomposition, SIGNAL (programming language), Power (physics), Artificial neural network, Hilbert–Huang transform, Deep belief network, Signal processing, Electronic engineering, Pattern recognition (psychology), Control theory (sociology), Algorithm, Engineering, Wind power forecasting, Electric power system, Control engineering, Wind speed

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Cited by: 0

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References (count): 33

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