Weibull Probability Distribution of Wind Speed for Gaza Strip for 10 Years

Badawi, Ahmed Samir; Hasbullah, Nurul Fadzlin; Yusoff, Siti Hajar; Khan, Sheroz; Hashim, Aisha Hassan Abdalla; Zyoud, Alhareth; Elamassie, Mohammed

doi:10.4028/www.scientific.net/amm.892.284

Cited by 16 publications

(16 citation statements)

References 28 publications

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“…Where , and are the mean wind speed (m/s), c (m/s), k (dimensionless), respectively. Parameter indicates the width of the wind speed probability distribution, which represents the wind probability distribution peak of any specific region [71], [73], [74]. Parameter indicates the abscissa scale of the wind probability distribution, which shows the wind in particular location [71], [75].…”

Section: Weibull Parameters Calculationmentioning

confidence: 99%

Data bank: nine numerical methods for determining the parameters of weibull for wind energy generation tested by five statistical tools

Badawi

Yusoff

Zyoud

et al. 2021

IJPEDS

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This study aims to determine the potential of wind energy in the mediterranean coastal plain of Palestine. The parameters of the Weibull distribution were calculated on basis of wind speed data. Accordingly, two approaches were employed: analysis of a set of actual time series data and theoretical Weibull probability function. In this analysis, the parameters Weibull shape factor ‘k’ and the Weibull scale factor ‘c’ were adopted. These suitability values were calculated using the following popular methods: method of moments (MM), standard deviation method (STDM), empirical method (EM), maximum likelihood method (MLM), modified maximum likelihood method (MMLM), second modified maximum likelihood method (SMMLM), graphical method (GM), least mean square method (LSM) and energy pattern factor method (EPF). The performance of these numerical methods was tested by root mean square error (RMSE), index of agreement (IA), Chi-square test (X2), mean absolute percentage error (MAPE) and relative root mean square error (RRMSE) to estimate the percentage of error. Among the prediction techniques. The EPF exhibited the greatest accuracy performance followed by MM and MLM, whereas the SMMLM exhibited the worst performance. The RMSE achieved the best prediction accuracy, whereas the RRMSE attained the worst prediction accuracy.

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Section: Weibull Parameters Calculationmentioning

confidence: 99%

Data bank: nine numerical methods for determining the parameters of weibull for wind energy generation tested by five statistical tools

Badawi

Yusoff

Zyoud

et al. 2021

IJPEDS

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“…The analytical tool mostly employed is the Weibull distribution function (WDF) because of its flexibility and simplicity. Besides, it is adequate for a large range of wind data (Badawi et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Techno-Economic Assessment of Wind Turbines in Nigeria

Okakwu¹,

Olabode²,

Alayande

et al. 2021

IJEEP

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Wind energy potentials of some selected high altitude and coastal areas in Nigeria are assessed for possible utilization for the generation of electricity. The main aim is to provide pragmatic insight that will enhance the investment in wind energy conversion systems in an optimal manner. The data used in this present study were obtained from the Nigeria Meteorological Agency, which includes average wind speeds per day of four locations across the country, measured at the anemometer height of 10 m over a period of 11 years. With the sites classified based on their wind power densities, the capacity factor estimation value was used to select the most suitable turbine for the selected sites, and the present value cost method was employed to estimate the unit cost of energy of the turbine at various hub-heights. The results obtained from this study reveal that Jos, Kano and Ikeja are economically viable as well as having excellent wind resources integration into the grid, while P/H is viable for a standalone application. The outcome of the study provides useful information that will aid renewable energy policymakers in Nigeria for wind energy development.

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“…Several studies have been conducted indifferent territories across Palestine, but we believe that the present study is the first research study conducted in Gaza. Calculating wind power density (WPD) based on the measured data of a target meteorological location is the best strategy to investigate the feasibility of using wind energy at any location [10,7,[11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Wind Power for Electrical Energy Generation in the Mediterranean Coast of Palestine for 14 Years

Badawi¹,

Hasbullaha²,

Yusoff³

et al. 2019

IJECE

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The generation, distributionand transmission of electricity in Palestine have recently emerged as major issues. This study comprehensively assesses the production of wind energy and the estimation of wind energy potential in Palestine’s south coastal plain. The goal is to evaluate Palestine’s wind energy production by studying wind data and calculating energy and power. This study analyses two actual time series datasets. Findings are elaborated to determine the wind energy conversion per 1 m2. The wind speed data from January 1996 to December 2006 in Gaza and from January 2012 to December 2015 in Ashqelon are selected as the data sample. This study is crucial given the need for clean and renewable energy, the power shortage in the Gaza Strip and the limited number of wind energy studies conducted in the south coastal plain of Palestine, especially Gaza Strip. This study estimates the wind energy potential of the Gaza Strip to determine the wind potential. The annual mean wind speed and power are 4.11 ms-1 and 903.4 Wm-2, respectively. Moreover, the study clarifies the electrical energy generation in the Gaza Strip using small-scale turbines and offers a feasible alternative to existing schemes.

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Weibull Probability Distribution of Wind Speed for Gaza Strip for 10 Years

Cited by 16 publications

References 28 publications

Data bank: nine numerical methods for determining the parameters of weibull for wind energy generation tested by five statistical tools

Data bank: nine numerical methods for determining the parameters of weibull for wind energy generation tested by five statistical tools

Techno-Economic Assessment of Wind Turbines in Nigeria

Evaluation of Wind Power for Electrical Energy Generation in the Mediterranean Coast of Palestine for 14 Years

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