Note on the Use of the Inverse Gaussian Distribution for Wind Energy Applications

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Abstract
The inverse Gaussian distribution is suggested as an alternative to the three-parameter Weibull distribution for the description of wind speed data with low frequencies of low speeds. A comparison of the two distributions indicates a region of strong similarity, corresponding reasonably well to three-parameter Weibull distributions which have been fitted to wind data. Maximum likelihood estimation of the inverse Gaussian parameters is much simpler than the iterative technique required for the three-parameter Weibull distribution. In addition, the inverse Gaussian distribution features the mean wind speed as a parameter, a desirable property for wind energy investigations. A summation-reproductive property of the distribution permits estimation of the mean wind energy flux from a sequence of speed averages. Abstract The inverse Gaussian distribution is suggested as an alternative to the three-parameter Weibull distribution for the description of wind speed data with low frequencies of low speeds. A comparison of the two distributions indicates a region of strong similarity, corresponding reasonably well to three-parameter Weibull distributions which have been fitted to wind data. Maximum likelihood estimation of the inverse Gaussian parameters is much simpler than the iterative technique required for the three-parameter Weibull distribution. In addition, the inverse Gaussian distribution features the mean wind speed as a parameter, a desirable property for wind energy investigations. A summation-reproductive property of the distribution permits estimation of the mean wind energy flux from a sequence of speed averages.

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