Utilizing the Weibull distribution for wind speed analysis enables the calculation of percentile wind speeds, which helps in understanding the frequencies at which certain wind speeds are exceeded. It is vital to focus on upper percentile values, as they reflect extreme wind speeds that can result in considerable damage when they coincide with storms or heavy rainfall. The monthly wind speed data can be approximated by establishing confidence intervals for the percentile of Weibull distribution. We developed these intervals using generalized confidence intervals, the bootstrap method, and Bayesian techniques employing both gamma and uniform priors. Findings from our simulation studies showed that the Bayesian HPD interval with a uniform prior provided coverage probabilities close to the nominal confidence level and resulted in shorter intervals compared to other methods. To validate the effectiveness of our proposed approaches, we applied them to monthly wind speed data from southern Thailand.