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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.
Shu et al. (Sun,) studied this question.