A Comparative Study on Wind Energy Assessment Distribution Models: A Case Study on Weibull Distribution

Abstract

Wind power generation highly depends on the determination of wind power potential, which drives the design and feasibility of the wind energy production investment. This gives an important role to wind power estimation, which creates the need for an accurate wind data analysis and wind energy potential assessments for a given location. Such assessments require the implementation of an accurate and suitable wind distribution model. Therefore, in the quest for a well-fitted model, eight methods for estimating the Weibull parameters are investigated in this paper. The methods were then investigated by employing statistical tools, and their performances have been discussed in terms of various error indicators such as root mean squared error (RMSE), regression error (R2), chi-square (X2), and mean absolute error (MAE). Meteorological data for diverse terrain from 14 provinces with 30 sites scattered across Iran were employed to examine the performance of the investigated methods. The results demonstrated that the empirical method has superiority over the investigated technique in terms of errors.

Technical analysis of wind energy potentials using a modified Weibull and Raleigh distribution model parameters approach in the Gambia

“…The variations of the Weibull and Raleigh distributions results from the measured results can be attributed to the different numerical coefficients and parameters involved in the calculations of the EPFM and STDM, c and L, among others. This is in agreement with the results of Teimourian et al [ 33 ] on assessing wind energy distribution models.…”

Comparative Analysis of Estimated Small Wind Energy Using Different Probability Distributions in a Desert City in Northwestern Mexico

“…Therefore, determining the function that best represents the wind regime at a location will contribute to a better estimation. In this regard, several studies [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] have used different probability density functions, such as Weibull, Gamma, Raleigh, Beta, log-normal, and some combinations with them. On the other hand, Wais [12,13] mentions that the two-parameter Weibull distribution is recognized as an appropriate model and the most widely used in the wind industry, but also concludes that the two-parameter Weibull distribution is not always sufficient to specify the wind speed distribution and evaluate the available wind energy.…”

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