Hydrogen is one of the prospective clean energies that could potentially address two pressing areas of global concern, namely energy crises and environmental issues. Nowadays, fossil-based technologies are widely used to produce hydrogen and release higher greenhouse gas emissions during the process. Decarbonizing the planet has been one of the major goals in the recent decades. To achieve this goal, it is necessary to find clean, sustainable, and reliable hydrogen production technologies with low costs and zero emissions. Therefore, this study aims to analyse the hydrogen generation from solar and wind energy sources and observe broad prospects with hybrid renewable energy sources in producing green hydrogen. The study mainly focuses on the critical assessment of solar, wind, and hybrid-powered electrolysis technologies in producing hydrogen. Furthermore, the key challenges and opportunities associated with commercial-scale deployment are addressed. Finally, the potential applications and their scopes are discussed to analyse the important barriers to the overall commercial development of solar-wind-based hydrogen production systems. The study found that the production of hydrogen appears to be the best candidate to be employed for multiple purposes, blending the roles of fuel energy carrier and energy storage modality. Further studies are recommended to find technical and sustainable solutions to overcome the current issues that are identified in this study.

The best Weibull distribution methods for the assessment of wind energy potential at different altitudes in desired locations are statistically diagnosed in this study. Seven different methods, namely graphical method (GM), method of moments (MOM), standard deviation method (STDM), maximum likelihood method (MLM), power density method (PDM), modified maximum likelihood method (MMLM) and equivalent energy method (EEM) were used to estimate the Weibull parameters and six statistical tools, namely relative percentage of error, root mean square error (RMSE), mean percentage of error, mean absolute percentage of error, chi-square error and analysis of variance were used to precisely rank the methods. The statistical fittings of the measured and calculated wind speed data are assessed for justifying the performance of the methods. The capacity factor and total energy generated by a small model wind turbine is calculated by numerical integration using Trapezoidal sums and Simpson’s rules. The results show that MOM and MLM are the most efficient methods for determining the value of k and c to fit Weibull distribution curves.