Investigation of Wind Characteristics and Estimation of Wind Power Potential of Narok County Using Weibull Distribution

Steven Okoth *

Department of Mathematics and Physical Sciences, Faculty of Pure, Applied and Health Sciences, Maasai Mara University, Kenya.

Otieno Fredrick

Department of Mathematics and Physical Sciences, Faculty of Pure, Applied and Health Sciences, Maasai Mara University, Kenya.

Isaac Motochi

Department of Mathematics and Physical Sciences, Faculty of Pure, Applied and Health Sciences, Maasai Mara University, Kenya.

*Author to whom correspondence should be addressed.


Aim: To investigate wind characteristics and estimate wind power density of Narok weather station in Narok county using Weibull distribution.

Research Design: Historical hourly wind direction and speed data recorded by the Kenya Meteorological Department in Narok weather station was analyzed.

Place and duration: The study utilized data samples collected at Narok weather station over a period spanning from 2011 to 2021.

Methods: To assess the temporal characteristics, a statistical average technique was employed. The spatial aspect, specifically wind speed variation with height, was evaluated through wind speed extrapolation using the power law. The dominant wind direction was determined by plotting a polar chart based on a frequency distribution table prepared using both wind direction and wind speed data. The turbulence intensity of the wind was calculated using the turbulence intensity equation. The Weibull parameters were estimated using the maximum likelihood estimation method. The Weibull probability distribution was used to analyze wind speed distribution and power density. The extrapolated Weibull parameters were utilized to calculate wind power density at various heights. The accuracy of the wind regime distribution in Narok was assessed by employing the R2 technique.

Results: The wind regime in Narok exhibited an average annual wind speed of 4.3 m/s and a mean wind power density of 126 W/m2. Analysis of diurnal wind speed variation revealed peak wind speeds around noon, with wind speeds exceeding the cut-in wind threshold (3 m/s) between 0430hrs and 2100hrs. March and October were identified as the windiest months, exhibiting the highest wind power densities, while June and December demonstrated the lowest values. Wind speed and, consequently, wind power density increased exponentially with height. The prevailing wind directions in Narok were primarily from the East, followed by the North and North West. The wind regime in Narok exhibited turbulence, as indicated by average turbulence intensities exceeding 0.25. The wind regime in Narok was accurately described by the Weibull distribution, with an approximation accuracy of 0.94 based on the R2 error.

Conclusion: The wind regime in Narok is generally suitable for extracting wind power at heights above 15 m, regardless of the scale of the wind power extraction.

Keywords: Narok, wind speed, wind power density, wind turbine, Weibull probability distribution function

How to Cite

Okoth, S., Fredrick, O., & Motochi, I. (2023). Investigation of Wind Characteristics and Estimation of Wind Power Potential of Narok County Using Weibull Distribution. Journal of Energy Research and Reviews, 15(2), 35–46.


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Amos Omamo O, Anthony Rodrigues J, Wafula Muliaro J. A system dynamics model of technology and society: In the context of a developing nation. International Journal of System Dynamics Applications. 2020;9(2): 22.

Ondiek Renish Awuor, Olorunnisola Abel Olajide, Basweti Evans. Analysis of household use of traditional fuels and possible contribution to deforestation in Kisii County, Kenya. Open Journal of Ecology. 2022;12(11).

Stacey Waudo, James Koske, Fuchaka Waswa. More trees and more biomass energy options for increased energy security within households in Navakholo Sub-County, Kenya. East African Journal of Environment and Natural Resources. 2023;6(1).

Mohammed Takase, Rogers Kipkoech, Paul Kwame Essandoh. A comprehensive review of energy scenario and sustainable energy in Kenya. Fuel Communications. 2021;7(100015):1-13.

Perera F. Pollution from fossil-fuel combustion is the leading environmental threat to global pediatric health and equity: Solutions exist. Int J Environ Res Public Health; 2021.

Ahmad Asaad Mohammad, Furkan; Alam Mohammad Saad, Das, Ganesh. Automation of the Grid: Indian Initiatives, in 2017 IEEE International Conference on Technological Advancements in Power and Energy (TAP Energy), Delhi,India; 2017.

Devasani SKR, Vodnala S, Singarapu D et al. A comprehensive review on performance, combustion and emissions of ternary and quaternary biodiesel/diesel blends. Environ Sci Pollut Res; 2021.

Moses Jeremiah Barasa Kabeyi, Oludolapo Akanni Olanrewaju. Sustainable energy transition for renewable and low carbon grid electricity generation and supply. Frontiers in Energy Research; 2022.

Kuhudzai RJ. Clean Technica. Clean Technica, 4th 11; 2021.

Available: [Accessed on: 2nd 4 2022].

EaPR. Authoriy, "EPRA-RENEWABLE ENERGY," EPRA,Kenya, 1st January 2020. [Online].

Available: [Accessed on 2nd 5 2022].

Kazimierczuk AH. Wind energy in Kenya: A status and policy framework review. Renewable and Sustainable Energy Reviews. 2019;107:434-445.

Cheruiyot WK, Tonui JK, Limo SC. Assesment of wind enrgy potential at Kesses region-Kenya based on Weibull parameters. International Journal of Advanced Research. 2016;4(7):641-648.

Nyasani Erick Isaboke, Mathew Munji, Mukuru Ssessazi Alfred, Muhorakeye Lenatha, Douglas Nyabuga. Wind energy assesment as a potential alternative in Kisumu city, Kenya. World Journal of Engineering Research and Technology. 2018;4(4):75-104.

Laban Ongaki N,Christopher Maghang M, Joash Kerongo. Evaluation of the technical wind energy potential of kisii region based on the weibull and rayleigh distribution models. Journal of Energy. 2021;17.

Kwamboka JO, Kamau JN, Saoke CO, Ndeda JO, Maina AW. Analysis of the wind energy characteristics and potential on the Hilly Terrain of Manga, Nyamira County, Kenya. International Journal of Innovative Science and Research Technology. 2018; 3(3).

Kamau JN, Kinyua R, Gathua JK. An investigation of the utility scale wind energy for North-Estern Kenya Region. JAGST. 2011;2:13.

Justus Nzuka Mwanzia, David Wafula Wekesa. Joseph Kamau. Analysis of wind resource potential for small-scale wind turbine performance in Kiseveni, Kenya. International Journal of High Energy Physics. 2019;6(1):17-29.

Choge D. Analysis of Wind and solar energy potential in eldoret, Kenya. Journal of Energy Technologies and Policy. 2015; 5(2).

Okumu Otieno Kevin, Troon John Benedict, Samuel Muthiga Ngaga. Fitting wind speed to a 3-parameter distribution using maximum likelihood technique. International Journal of Statistical Distributions and Applications. 2021;7(1):1-6.

Ali K Resen, Ahmed B Khamees, Saif F. Yaseen. Determination of wind shear coefficients and conditions of atmospheric stability for three iraqi sites, in 3rd International Conference on Sustainable Engineering Techniques (ICSET 2020); 2020.

Xing Zheng,Yu Yao,Zhenhong Hu ,Ziying Yu andSiyuan Hu. Influence of Turbulence Intensity on the Aerodynamic Performance of Wind Turbines Based on the Fluid-Structure Coupling Method. Applied Sciences. 2023;13(1).

Otieno Fredrick Onyango, Sibomana Gaston, Elie Kabende, Felix Nkunda, Jared Hera Ndeda. Wind power potential in kigali and western provinces in rwanda. Asia Pacific Journal of Energy and Environment. 2014;1(3).

Daoudi Mohammeda, Ait Sidi Mou Abdelaziz, Elkhomri Mohammed, Elkhouzai Elmostapha. Wind speed data and wind energy potential using weibull distribution in Zagora, Morocco. Int. Journal of Renewable Energy Development (IJRED). 2019;8(3):267-273.

Boming Liu, Xian Ma, Jianping Guo, Hui Li, Shikuan Jin. Yingyng Ma, Wei Gong. Estimating hub-height wind speed based on a machine learning algorithm: Implications for wind energy assessment. Atmospheric Chemistry and Physics. 2023; 23:3181–3193.

Twidell John, Weir Tony. Wind resource, in Renewable Energy Resources, New York, Routledge. 2015;257.

Sheng-Lun Tai, Larry K. Berg, Raghavendra Krishnamurthy, Rob Newsom and Anthony Kirincich. Validation of turbulence intensity as simulated by the weather research and forecasting model off the U.S. Northeast Coast. Wind Energy Discussions. 2022;84.

Emeis S. Current issues in wind energy meteorology. Royal Meteorological Society. 2014;21:803–819.

Zuzana Sedliačková, Ivan Pobocikova, Maria Milchakova, Daniela Jurášová. Wind speed modeling using Weibull distribution: A case of Liptovský Mikuláš, Slovakia. MMS 2020. 2022;357.