Assessing the Best Fit Probability Distribution Model for Wind Speed Data for Different Sites of Burkina Faso

Boro, Drissa; Thierry, Ky; Kieno, Florent P.; Bathiebo, J.

doi:10.9734/cjast/2020/v39i2230845

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…Wind speed distribution is stochastic in nature and can be best expressed as probability distribution function with an acceptable accuracy level (Matthew, 2006 andBoro et al, 2020). To determine the probability distribution of the wind regime, the measured wind speed data was fitted into various probability functions using easyfit software which is an add-on in Microsoft Excel spread sheet, to identify suitable statistical distributions representing the wind speed regime of Heipang village.…”

Section: Wind Speed Data Analysis and Determination Of Weilbull Param...mentioning

confidence: 99%

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Performance Evaluation of Some Selected Wind Turbines in Heipang Wind Speed Regime

Maren,

Bello,

Dandakouta

et al. 2022

Nigerian Journal of Tropical Engineering

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Heipang in Plateau State is classified under moderate wind speed regime in Nigeria, thus, has high potential for wind electricity generation. Due to high cost, it is difficult to design a wind turbine for a particular site; therefore, the designer of the wind energy project has to choose from the available options in markets, which come in different sizes and speed characteristics. This paper is aimed at evaluating the performance of some selected wind turbines in Heipang wind speed regime. The method used is based on wind speed analysis and computation of the capacity factors of wind turbines expressed as a product of wind turbines’ power output models and probability distribution of wind speed regime of Heipang; and the total annual energy generation of the wind turbines using Wind Energy Resources Analysis (WERA) software. Results showed that Heipang has an annual mean wind speed of 6.3 m/s and its wind speed regime best fitted into Weibull probability distribution function with average Weibull shape and scale parameters of 3.05 and 7.03 m/s respectively at 10 m height. For small (<10kW), medium (10kW-250kW) and large (>250kW) wind turbines classifications; WT4, WT14 and WT25 have the highest capacity factors of 0.61, 0.7 and 0.53 respectively and WT2, WT22, WT27 have the highest total annual energy generation of 0.0054, 1.12 and 4.03 GWh/year respectively. In conclusion, Heipang has high wind speed potential for wind power technology and wind turbines with higher annual energy generation are better options for selections for wind power generation applications

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Section: Wind Speed Data Analysis and Determination Of Weilbull Param...mentioning

confidence: 99%

“…respectively. The power law exponent  is given as (Boro et al, 2020;Tonsie et al, 2021 andManwell et al, 2005):…”

Section: Wind Speed Data Analysis and Determination Of Weilbull Param...mentioning

confidence: 99%

Performance Evaluation of Some Selected Wind Turbines in Heipang Wind Speed Regime

Maren,

Bello,

Dandakouta

et al. 2022

Nigerian Journal of Tropical Engineering

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“…One of these drawbacks is the need of determining the values of the parameters appropriately by various types of estimation methods. According to [16,17,[28][29][30], the Kernal density method is the well-known used method.…”

Section: Introductionmentioning

confidence: 99%

Optimal Prediction of Wind Energy Resources Based on WOA—A Case Study in Jordan

et al. 2023

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The average wind speed in a given area has a significant impact on the amount of energy that can be harvested by wind turbines. The regions with the most attractive possibilities are typically those that are close to the seaside and have open terrain inland. There is also good potential in several mountainous locations. Despite these geographical restrictions on where wind energy projects can be located, there is enough topography in most of the world’s regions to use wind energy projects to meet a significant amount of the local electricity needs. This paper presents a new method of energy prediction of wind resources in several wind sites in Jordan, which can be used to decide whether a specific wind site is suitable for wind farm installation purposes. Three distribution models, Weibull, Gamma and Rayleigh, were employed to characterize the provided wind data. Different estimation methods were used to assign the parameters associated with each distribution model and the optimal parameters were estimated using whale optimization algorithms which reduce the error between the estimated and the measured wind speed probability. The distribution models’ performance was investigated using three statistical indicators. These indicators were: root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE). Finally, using the superlative distribution models, the wind energy for the chosen wind sites was estimated. This estimation was based on the calculation of the wind power density (ED) and the total wind energy (ET) of the wind regime. The results show that the total wind energy ranged from slightly under 100 kWh/m2 to nearly 1250 kWh/m2. In addition, the sites recording the highest estimated wind energy had the optimum average wind speed and the most symmetrical distribution pattern.

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“…A number of research studies were observed to determine optimal and/or best distribution, which represents the wind regime of any location (Carta et al , 2009; Morgan et al , 2011; Lo Brano et al , 2011; Boro et al , 2020). However, several authors have found that there is a major difference between the potential of actual wind energy and the estimated wind energy of a location (Deep et al , 2020).…”

Section: Introductionmentioning

confidence: 99%

A probabilistic-based analysis for wind distribution determination of a runway

Suzer

Kaba

2021

AEAT

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Purpose The purpose of this study is to describe precisely the wind speed regime and characteristics of a runway of an International Airport, the north-western part of Turkey. Design methodology approach Three different probability distributions, namely, Inverse Gaussian (IG), widely used two-parameter Weibull and Rayleigh distributions in the literature, are used to represent wind regime and characteristics of the runway. The parameters of each distribution are estimated by the pattern search (PS)-based heuristic algorithm. The results are compared with the other three methods-based numerical computation, including maximum-likelihood method, moment method (MoM) and power density method, respectively. To evaluate the fitting performance of the proposed method, several statistical goodness tests including the mostly used root mean square error (RMSE) and chi-squared (X2) are conducted. Findings In the light of the statistical goodness tests, the results of the IG-based PS attain better performance than the classical Weibull and Rayleigh functions. Both the RMSE and X2 values achieved by the IG-based PS method lower than that of Weibull and Rayleigh distributions. It exhibits a better fitting performance with 0.0074 for RMSE and 0.58 × 10−4 for X2 for probability density function (PDF) in 2012 and with RMSE of 0.0084 and X2 of 0.74 × 10−4 for PDF in 2013. As regard the cumulative density function of the measured wind data, the best results are found to be Weibull-based PS with RMSE of 0.0175 and X2 of 3.25 × 10−4 in 2012. However, Weibull-based MoM shows more excellent ability in 2013, with RMSE of 0.0166 and X2 of 2.94 × 10−4. Consequently, it is considered that the results of this study confirm that IG-based PS with the lowest error value can a good choice to model more accurately and characterize the wind speed profile of the airport. Practical implications This paper presents a realistic point of view regarding the wind regime and characteristics of an airport. This study may cast the light on researchers, policymakers, policy analysts and airport designers intending to investigate the wind profile of a runway at the airport in the world and also provide a significant pathway on how to determine the wind distribution of the runway. Originality value Instead of the well-known Weibull distribution for the representing of wind distribution in the literature, in this paper, IG distribution is used. Furthermore, the suitability of IG to represent the wind distribution is validated when compared with two-parameter Weibull and Rayleigh distributions. Besides, the performance and efficiency of PS have been evaluated by comparing it with other methods.

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Assessing the Best Fit Probability Distribution Model for Wind Speed Data for Different Sites of Burkina Faso

Cited by 5 publications

References 23 publications

Performance Evaluation of Some Selected Wind Turbines in Heipang Wind Speed Regime

Performance Evaluation of Some Selected Wind Turbines in Heipang Wind Speed Regime

Optimal Prediction of Wind Energy Resources Based on WOA—A Case Study in Jordan

A probabilistic-based analysis for wind distribution determination of a runway

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