Wind Speed Prediction Based on a Hybrid Deep Learning Model of SSA-VMD and SSA-CNN-BiLSTM Article Swipe
Yang Liu
,
Haitao Han
,
Haibo Liu
,
Binbin Zhong
,
Chao Gao
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3233/atde250927
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3233/atde250927
To enhance wind velocity forecasting precision, this study introduces a composite deep learning framework integrating Sparrow Search Algorithm (SSA), Variational Mode Decomposition (VMD), Convolutional Neural Networks (CNNs), and Bidirectional Long Short-Term Memory (BiLSTM) architectures. The methodology begins with seasonal segmentation of annual wind data into spring, summer, autumn, and winter periods, with spring serving as the primary test case. Initial wind speed measurements undergo decomposition into multiple constituent signals through VMD processing, where SSA optimizes critical VMD parameters. Subsequently, a combined CNN-BiLSTM architecture processes these decomposed components, with SSA simultaneously fine-tuning the model’s hyperparameters. Following individual subsequence analysis, signal reconstruction yields comprehensive wind speed predictions. Empirical validation employs actual meteorological records from a terrestrial monitoring station in Gansu Province. Comparative analysis demonstrates superior predictive performance and enhanced fitting accuracy relative to conventional models, confirming the framework’s effectiveness in capturing dynamic wind patterns.
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- book-chapter
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- en
- Landing Page
- https://doi.org/10.3233/atde250927
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- https://openalex.org/W4414794673
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https://openalex.org/W4414794673Canonical identifier for this work in OpenAlex
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https://doi.org/10.3233/atde250927Digital Object Identifier
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Wind Speed Prediction Based on a Hybrid Deep Learning Model of SSA-VMD and SSA-CNN-BiLSTMWork title
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book-chapterOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-10-01Full publication date if available
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Yang Liu, Haitao Han, Haibo Liu, Binbin Zhong, Chao GaoList of authors in order
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https://doi.org/10.3233/atde250927Publisher landing page
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://doi.org/10.3233/atde250927Direct OA link when available
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