Application of fractal analysis on wind speed time series: A review

Wind speed time series are characterized by complex, non-linear, and irregular patterns, which challenge traditional statistical methods in capturing their dynamics. Fractal analysis, with its focus on self-similarity, scale-invariance, and multi-scale dependencies, offers a powerful framework for u...

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Bibliographic Details
Published inAdvances in Wind Engineering Vol. 2; no. 1; p. 100028
Main Authors Shu, Zhenru, Chan, Pak Wai
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.03.2025
Subjects
Chaotic time series
Complexity characterization
Fractal dimension
Hurst analysis
Time series prediction
Wind speed
Hurst analysis
Complexity characterization
Chaotic time series
Time series prediction
Wind speed
Fractal dimension
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Summary:Wind speed time series are characterized by complex, non-linear, and irregular patterns, which challenge traditional statistical methods in capturing their dynamics. Fractal analysis, with its focus on self-similarity, scale-invariance, and multi-scale dependencies, offers a powerful framework for understanding the intricate behaviors of wind speed data. However, the application of fractal techniques to wind speed analysis remains underexplored. This review outlines key concepts of fractal analysis, such as fractal, self-similarity, and scale-invariance. It also highlights common fractal techniques and their potential applications to wind speed data. Overall, this paper provides insights into how fractal analysis can advance the understanding of wind dynamics and contribute to more accurate wind speed forecasting. It identifies key opportunities for future research, particularly in developing hybrid models that combine fractal analysis with machine learning for improved predictive capabilities in wind engineering and related fields.
ISSN:2950-6018
2950-6018
DOI:10.1016/j.awe.2024.100028