Application of Weibull Distribution in Renewable Energy

Abstract

The growing global emphasis on renewable energy has intensified the need for precise statistical modeling techniques to enhance energy forecasting, system reliability, and performance optimization. Among the various probabilistic approaches, the Weibull distribution has emerged as a pre-eminent model for characterizing stochastic variability in renewable resources such as wind speed and solar irradiance. This study offers a comprehensive reassessment of the Weibull distribution’s theoretical foundations and its empirical applications in renewable energy systems. The paper reorganizes classical Weibull analysis within a modern research framework—evaluating parameter estimation methods, goodness-of-fit diagnostics, and cross-domain applications in wind, solar, and hybrid systems. Through a synthesis of recent studies, the article highlights the Weibull model’s comparative advantages over Rayleigh, lognormal, and gamma alternatives. It further examines integration with computational platforms and artificial intelligence for predictive analytics. The results reaffirm that Weibull-based modeling not only provides robust probabilistic representation of resource variability but also serves as an analytical cornerstone for reliability engineering and sustainable energy planning.