Abstract
This study compares three probability density functions (PDFs) for understanding and estimating wind power based on wind patterns: the commonly-used Weibull distribution, the relatively-new Birnbaum-Saunders distribution, and the Nakagami distribution. The wind profile of Jumla, Nepal was analyzed using data from 2004 to 2014. The Nakagami distribution performed similarly to the Weibull distribution in terms of understanding wind patterns. However, for estimating wind power, the Nakagami distribution was found to be more effective than the Weibull distribution in most cases. The Birnbaum-Saunders distribution was found to be the least effective of the three PDFs compared.
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