Wind speed and wind power time series exhibit pronounced intermittency, long-range correlations, and scale-dependent variability arising from atmospheric turbulence and nonlinear energy conversion processes. While numerous studies have reported multifractal signatures in wind-related data, existing reviews remain fragmented, method-driven, or application-focused, lacking a unifying perspective grounded in multifractal theory and scaling universality. This paper presents a comprehensive and theory-oriented review of multifractal analysis applied to wind speed and wind power variability, explicitly framed within the mathematical and physical foundations of fractal and multifractal processes. We systematically synthesize how different multifractal formalisms-detrended fluctuation analysis, wavelet-based approaches, and structure-function methods-characterize scaling heterogeneity across temporal scales, terrains, and operational regimes. Crucially, the review advances beyond cataloging results by identifying recurring universality patterns, dominant sources of multifractality, and systematic distortions introduced by nonlinear power conversion. By consolidating empirical findings through the lens of multifractal spectra, singularity widths, and generalized Hurst exponents, this work establishes multifractality as a fundamental descriptor of wind energy dynamics rather than a secondary statistical feature. The review concludes by outlining open theoretical challenges and proposing a research agenda that positions multifractal analysis as a core framework for understanding variability, intermittency, and uncertainty in wind energy systems.
Namazi et al. (Fri,) studied this question.