A hybrid forecasting model for very short-term wind speed prediction based on secondary decomposition and deep learning algorithms

Ma, Ke; Zhang, Wenyu; Guo, Zhengfu; Zhao, Jing; Qiu, Wei

doi:10.1007/s12145-023-01044-1

Cited by 4 publications

(1 citation statement)

References 39 publications

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“…Year Type Methods [58] 2022 WSP EEMD + WPT [59] 2023 WSP VMD + SSA [60] 2023 WPP CEEMDAN + VMD [61] 2022 WSP CEEMDAN + LMD [62] 2023 WSP WT + VMD [63] 2023 WSP OVMD + DWT In summary, data preprocessing methods can be categorized into outlier detection methods and decomposition-based methods. Outlier detection methods focus on handling abnormal data in the original dataset to reduce the adverse impact of outliers on model training.…”

Section: Articlementioning

confidence: 99%

Review of AI-Based Wind Prediction within Recent Three Years: 2021–2023

Song,

Tan,

Huang

et al. 2024

Energies

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Wind prediction has consistently been in the spotlight as a crucial element in achieving efficient wind power generation and reducing operational costs. In recent years, with the rapid advancement of artificial intelligence (AI) technology, its application in the field of wind prediction has made significant strides. Focusing on the process of AI-based wind prediction modeling, this paper provides a comprehensive summary and discussion of key techniques and models in data preprocessing, feature extraction, relationship learning, and parameter optimization. Building upon this, three major challenges are identified in AI-based wind prediction: the uncertainty of wind data, the incompleteness of feature extraction, and the complexity of relationship learning. In response to these challenges, targeted suggestions are proposed for future research directions, aiming to promote the effective application of AI technology in the field of wind prediction and address the crucial issues therein.

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