On the probabilistic distribution of wind speeds: theoretical development and comparison with data

Abstract

The probabilistic distributions of wind speed are a critical piece of information needed in the assessment of wind-energy potential, and have been conventionally described by various empirical correlations. Among the empirical correlations, the Weibull distribution has been most popular due to its ability to fit most accurately the variety of wind-speed data measured at different geographical locations in the world. This study develops a theoretical approach to the analytical determination of the wind-speed distributions through the application of the Maximum Entropy Principle (MEP). Although it has been used in a variety of fields, this is the first time MEP has been applied to the wind energy field. Under the MEP, the maximisation of Shannon's entropy is carried out subject to the conservation of mass, momentum and energy associated with the wind flow. It is shown that the present theoretical predictions agree very well with a variety of the measured data from different sources and have better accuracy than the Weibull distributions.