Wind Energy Potential Assessment of Great Cumbrae Island Using Weibull Distribution Function

Kombe, Emmanuel Yeri; Muguthu, Joseph

doi:10.9734/jenrr/2019/v2i229734

Cited by 11 publications

(2 citation statements)

References 13 publications

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“…One of the main outcomes of this study was to identify a potential future offshore wind farm site with an annual power production capacity of approximately 35.36 MWh. Emmanuel et al [8] determined the wind farm construction feasibility at the Great Cumbrae Island near the coast of Ayrshire in the United Kingdom (UK). They used wind data measured over ten years at 50 m in height.…”

Section: Introductionmentioning

confidence: 99%

Accuracy Testing of Different Methods for Estimating Weibull Parameters of Wind Energy at Various Heights above Sea Level

Ali,

Park,

Noon

et al. 2024

Energies

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The Weibull algorithm is one of the most accurate tools for forecasting and estimating wind energy potential. Two main parameters of the Weibull algorithm are the ‘Weibull shape’ and ‘Weibull scale’ factors. There are six different numerical methods to estimate the two Weibull parameters. These six methods are the empirical method of Justus (method 1), the empirical method of Lysen (method 2), the maximum likelihood method (method 3), the modified maximum likelihood method (method 4), the energy pattern factor method (method 5) and the graphical method (method 6). Many commercial wind energy software programs use the Weibull algorithm, and these six methods are used to calculate the potential wind energy at a given site. However, their accuracy is rarely discussed, particularly regarding wind data height. For this purpose, wind data measured for a long period (six years) at real sites are introduced. The wind data sites are categorized into three levels, i.e., low, medium, and high, based on wind data measurement height. The analysis shows that methods 1 and 2 are the most accurate methods among all six methods at low and medium heights. The number of errors increases with the height of these two methods. Methods 3 and 4 are the most suitable options for larger heights, as these scenarios have minimal error. The present study’s findings can be used in various fields, e.g., wind energy forecasting and wind farm planning.

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Section: Introductionmentioning

confidence: 99%

Accuracy Testing of Different Methods for Estimating Weibull Parameters of Wind Energy at Various Heights above Sea Level

Ali,

Park,

Noon

et al. 2024

Energies

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“…According to Mohammadi et al 2016 [22], it was found that different sites used different methods of Weibull parameter estimation since the wind characteristics of the site determine that best approximation method. Kombe et al [16] used the Weibull distribution function to fit 10 years of monthly mean wind speed data of Great Cumbrae Island. The calculated mean wind speed was 7.598 m/s and the wind power density calculated for the region was 483.50 W/m 2 .…”

Section: Introductionmentioning

confidence: 99%

Assessment of Wind Power Potential at Eastern-Jerusalem, Palestine

Alsamamra¹,

Shoqeir²

2020

OJEE

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Due to the energy demand and lack of supplied energy of Palestinian cities, wind resource assessment is important and necessary. The objective of the work is to analyze the wind speed data characteristics and wind power potential at eastern Jerusalem that are collected at 10 m above ground level from 2008 to 2018. The variations of monthly, seasonal, and annual wind speed are analyzed, and the measured maximum, minimum, and mean values are presented in this study. Wind speed characteristics have been analyzed by the well-known Weibull distribution function, and used to evaluate the wind power of the proposed site. Moreover, the relationship between wind power and mean wind speed is fitted by a second-order polynomial. The shape parameter moderate values showed that wind speed was relatively steady at the site. The highest average maximum value was found to be 5.7 m/s in June-2008, whereas the mean maximum values ranged from 5.4 m/s in June to 3.8 m/s in November. The highest mean power value was found to be 31.66 w/m 2 in July with a maximum value of 23.18 w/m 2 in 2013. R 2 of the polynomial fit provides values of 95% for monthly mean and 96% for annual mean.

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Technical and performance assessments of wind turbines in low wind speed areas using numerical, metaheuristic and remote sensing procedures

Akpan,

Ben,

Ekwok

et al. 2024

Applied Energy

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Wind Energy Potential Assessment of Great Cumbrae Island Using Weibull Distribution Function

Cited by 11 publications

References 13 publications

Accuracy Testing of Different Methods for Estimating Weibull Parameters of Wind Energy at Various Heights above Sea Level

Accuracy Testing of Different Methods for Estimating Weibull Parameters of Wind Energy at Various Heights above Sea Level

Assessment of Wind Power Potential at Eastern-Jerusalem, Palestine

Technical and performance assessments of wind turbines in low wind speed areas using numerical, metaheuristic and remote sensing procedures

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