<p>This study addresses the challenges of wind speed intermittency in wind power generation, aiming to enhance wind speed modeling for improved energy efficiency and reliability. We systematically refined each step of the modeling process, beginning with an exploration of 14 PDFs, including Weibull, Gamma, their length-biased variants, and bimodal mixtures. A grid search algorithm was integrated with five parameter estimation methods to optimize bimodal mixtures given their complexity. A total of 70 models were evaluated across twelve sites with diverse climatic and topographical characteristics. To assess the trade-off between wind speed modeling accuracy and wind power density estimation, various goodness-of-fit metrics were employed. Additionally, the k-means clustering, and Principal Component Analysis were used to identify the most suitable PDFs for specific site characteristics, addressing a key gap in wind power assessment literature. Key findings highlight the superior performance of length-biased models. Class A, which includes three bimodal length-biased mixtures achieved the best balance between wind speed modeling and wind power density estimation. Notably, bimodal length-biased Weibull exhibited consistent performance across all estimation methods. The results also suggest potential biases in data collection process. To our knowledge, length-biased mixture distributions have not been previously explored in wind power modeling.</p>