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Influence of the Random Data Sampling in Estimation of Wind Speed Resource: Case Study

1Centre d’Excellence Régionale pour la Maîtrise de l'Electricité (CERME), Laboratoire de Recherche en Sciences de l’Ingénieur (LARSI), École Nationale Supérieure d’Ingénieurs (ENSI), Université de Lomé, 01 BP 1515 Lomé 01, Togo

2Laboratoire de Recherche en Sciences de l’Ingénieur (LARSI), Département de Génie Électrique, Institut Universitaire de Technologie, Université Nazi BONI, 01 BP 1091 Bobo-Dioulasso 01, Burkina Faso

Received: 21 May 2021; Revised: 29 Sep 2021; Accepted: 10 Oct 2021; Available online: 2 Nov 2021; Published: 1 Feb 2022.
Editor(s): Editor Office
Open Access Copyright (c) 2022 The Authors. Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
In this study, statistical analysis is performed in order to characterize wind speeds distribution according to different samples randomly drawn from wind speed data collected. The purpose of this study is to assess how random sampling influences the estimation quality of the shape (k) and scale (c) parameters of a Weibull distribution function. Five stations were chosen in West Africa for the study, namely: Accra Kotoka, Cotonou Cadjehoun, Kano Mallam Aminu, Lomé Tokoin and Ouagadougou airport. We used the energy factor method (EPF) to compute shape and scale parameters. Statistical indicators used to assess estimation accuracy are the root mean square error (RMSE) and relative percentage error (RPE). Study results show that good accuracy in Weibull parameters and power density estimation is obtained with sampled wind speed data of 30% for Accra, 20% for Cotonou, 80% for Kano, 20% for Lomé, and 20% for Ouagadougou site. This study showed that for wind potential assessing at a site, wind speed data random sampling is sufficient to calculate wind power density. This is very useful in wind energy exploitation development.
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Keywords: Weibull parameter; wind speed; wind power density; random sample; statistical analysis

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  1. Adaramola, M. S., Agelin,-Chaab, M., Paul S.S. (2014) Assessment of wind power generation along the coast of Ghana. Energy Conversion and Management, 77, 61–9. DOI: 10.1016/j.enconman.2013.09.005
  2. Ahmed, S., A. (2013) Comparative study of four methods for estimating Weibull parameters for Halabja, Iraq. Int J Phys Sci, 8(5), 186–192. DOI: 10.5897/IJPS12.697
  3. Akdag, S.,A,, Dinler, A., A. (2009) New method to estimate Weibull parameters for wind energy applications. Energy Conversion and Management, 50(7), 1761–1766. DOI: 10.1016/j.enconman.2009.03.020
  4. Al-Zohbi, G., P., Hendrick, et Bouillard, P. (2014) Evaluation du potentiel d’énergie éolienne au Liban. Revue des énergies renouvelables, 17(1)
  5. Arslan, T., Y., M., Bulut, A., Yavuz A. (2014) Comparative study of numerical methods for determining Weibull parameters for wind energy potential. Renewable and Sustainable Energy Reviews, Elsevier 40(C), 820-825. DOI: 10.1016/j.rser.2014.08.009
  6. Boudia, S., M,, Guerri, O. (2015) Investigation of wind power potential at Oran, northwest of Algeria. Energy Conversion and Management, 105, 81–92. DOI: 10.1016/J.ENCONMAN.2015.07.055
  7. Chang, T., J,, Chen, C., L,, Tu, Y., L,, Yeh, H., T,, Wu, Y., T. (2015) Evaluation of the climate change impact on wind resources in Taiwan Strait. Energy Conversion and Management, 95, 435–45. https://doi.org/10.1016/j.enconman.2015.02.033
  8. Climate-data (undated) Géolocalisation et données climatiques des sites choisis. http://fr.climate.data.org/location. Accessed on 2 Juillet 2019
  9. Fagbenle, R., O., J., Katende, O., O., Ajayi, Okeniyi, J., O. (2011) Assessment of Wind Energy Potential of two Sites in North-East, Nigeria. Renewable Energy, 36(4), 1277- 1283. https://doi.org/10.1016/j.renene.2010.10.003
  10. Gabbasa, M,, Sopian, K,, Yaakob, Z,, Faraji, Zonooz, M., R,, Fudholi, A,, Asim, N. (2013) Review of the energy supply status for sustainable development in the Organization of Islamic Conference. Renewable and Sustainable Energy Reviews, 28, 18–28. DOI: https://doi.org/10.1016/j.rser.2013.07.045
  11. George, F. (2014) A comparison of shape and scale estimators of the two-parameter Weibull distribution. J. Modern Appl Statist Methods, 13(1), 23–35. DOI: 10.22237/jmasm/1398916920
  12. Guarienti, J., A., Almeida, A., K., Neto, A., M., Oliveira, A., R., Paulo, F., J., De Almeida, O., I., K. (2020) Performance analysis of numerical methods for determining Weibull distribution parameters applied to wind speed in Mato Grosso do Sul, Brazil. Sustainable Energy Technologies and Assessments, 42, 100854. https://doi.org/10.1016/j.seta.2020.100854
  13. GWEC: Global Wind Energy Council. Global wind statistics (2014). http://www.gwec.net. Consulté le 15 Juillet 2019
  14. Hennessey Jr JP. (1977). Some aspects of wind power statistics. J Appl Meteorol, 16(2), 119–128. DOI: https://doi.org/10.1175/1520-0450(1977)016<0119:SAOWPS>2.0.CO;2
  15. Houekpoheha, M., A., B., Kounouhewa, B., N., Tokpohozin, N., A. (2014) Estimation de la puissance énergétique éolienne à partir de la distribution de Weibull sur la côte béninoise à Cotonou dans le Golfe de Guinée. Revue des Energies Renouvelables 17(3), 489 – 495
  16. IEMSPR: International Energy Management Support Program / Rural Electrification Agencies in Sub-Saharan Africa (undated) Rural electrification statistics in Sub-Saharan Africa. https://www.ifdd.francophonie.org/docs/prisme/Agences_er_afr_subsah.pdf. Accessed on 10 Janvier 2019
  17. Jowder, F.A.L. (2009) Wind power analysis and site matching of wind turbine generators in Kingdom of Bahrain. Appl Energy, 86(4), 538–545. DOI: https://doi.org/10.1016/j.apenergy.2008.08.006
  18. Kapen, P., T., Gouajio, M., J., Yemélé D. (2020) Analysis and efficient comparison of ten numerical methods in estimating Weibull parameters for wind energy potential: Application to the city of Bafoussam, Cameroon. Renewable Enegy, 159,1188-1198. https://doi.org/10.1016/j.renene.2020.05.185
  19. Kang, D., Ko, K., and Huh J. (2018) Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea. Energies, 11(2), 356. https://doi.org/10.3390/en11020356
  20. Kasra, M.,, Alavi, O., Mostafaeipour, A., Goudarzi, N., Jalilvand, M. (2016) Assessing different parameters estimation methods of Weibull distribution to compute wind power density. Energy Conversion and Management, 108(2016), 322–335. DOI: https://doi.org/10.1016/j.enconman.2015.11.015
  21. Khahro, S., F,, Tabbassum, K,, Soomro, A., M,, Dong, L,, Liao, X. (2014) Evaluation of wind power production prospective and Weibull parameter estimation methods for Babaurband, Sindh Pakistan. Energy Convers Manage, 78, 956–967. DOI: https://doi.org/10.1016/j.enconman.2013.06.062
  22. Kidmo, D., K,, Danwe, R,, Doka, S.,Y,, Djongyang, N. (2015) Statistical analysis of wind speed distribution based on six Weibull Methods for wind power evaluation in Garoua, Cameroon. Revue des Energies Renouvelables, 18(1), 105–125
  23. Masseran, N., (2015) Evaluating wind power density models and their statistical properties. Energy, 84, 533–541. DOI: https://doi.org/10.1016/j.energy.2015.03.018
  24. Mathew, S. (2006) Wind energy: fundamentals, resource analysis and economics. Berlin, Heidelberg: Springer-Verlag. DOI: 10.1007/3-540-30906-3
  25. Meteorogram (undated) Weather data. http://weather.uwyo.edu./surface/ meteorogram/. Accessed on 10 Juin 2019
  26. Mohammadi, K., Mostafaeipour, A. (2013) Using different methods for comprehensive study of wind turbine utilization in Zarrineh, Iran. Energy Convers Manage, 65, 463–470. DOI: https://doi.org/10.1016/j.enconman.2012.09.004
  27. Mostafaeipour, A., Jadidi, M., Mohammadi, K., and Sedaghat, A. (2014) An Analysis of Wind Energy Potential and Economic Evaluation in Zahedan, Iran. Renewable and Sustainable Energy Reviews, 30, 641-650. DOI: https://doi.org/10.1016/j.rser.2013.11.016
  28. Mostafaeipour, A. (2010) Historical background, productivity and technical issues of qanats. Water History, 2, 61–80. DOI: https://doi.org/10.1007/s12685-010-0018-z
  29. Mouangue M., R., Kazet, M., Y., Kuitche, A., and Ndjaka, J., M. (2014) Influence of the Determination Methods of k and c Parameters on the Ability of Weibull Distribution to Suitably Estimate wind Potential and Electric Energy. International Journal of Renewable Energy Development, 3(2), 145-154. DOI: http://dx.doi.org/10.14710/ijred.3.2.145-154
  30. Ouarda, T., B., M., Charron, J., C., Shin, J., Y., Marpu,P., R., Al-Mandoos, A., H., Al Tamimi, M., H., Ghedira, H., Al Hosary T., N. (2015) Probability distributions of wind speed in the UAE. Energy Conversion and Management, 93, 414-434. https://doi.org/10.1016/j.enconman.2015.01.036
  31. Ouedraogo, S., Ajavon, A. S. A. Salami, A., A., Kodjo, M., K., and Bédja, K-S. (2017) Evaluation of wind potential in the sahelian area: case of three sites in Burkina Faso. Research Journal of Engineering Sciences, 6(11), 43-53
  32. Prem, K., Chaurasiya, S., A., Vilas, W. (2018) Study of different parameters estimation methods of Weibull distribution to determine wind power density using ground based Doppler Sodar instrument. Alexandria Engineering Journal, 57(4), 2299-2311. DOI: https://doi.org/10.1016/j.aej.2017.08.008
  33. Ramirez, J., A., C., Pénelope (2005) Influence of the data sampling interval in the estimation of the parameters of the Weibull wind speed probability density distribution: a case study. Energy Conversion and Management 46(15-16), 2419-2438. DOI: https://doi.org/10.1016/j.enconman.2004.11.004
  34. Razavieh, A., Sedaghat, A., Ayodele, R., Mostafaeipour, A. (2014) Worldwide wind energy status and the characteristics of wind energy in Iran, case study: the province of Sistan and Baluchestan. Int J Sustain Energy, 36(2), 103–123. DOI: https://doi.org/10.1080/14786451.2014.977288
  35. Sabzpooshani, M., Mohammadi, K. (2014) Establishing new empirical models for predicting monthly mean horizontal diffuse solar radiation in city of Isfahan, Iran. Energy, 69, 571–577. https://doi.org/10.1016/j.energy.2014.03.051
  36. Sadam, A., Bikai, J., Tetang, A., Kapseu, C. (2020) Potentiel énergétique éolien et profil de consommation d’énergie dans le village Wouro Kessoum Ngaoundéré Cameroun. Rеvuе des Energies Renouvelables. 23(1), 72-85
  37. Saeed, M., A., Zahoor, A., Yang, J., Zhang, W. (2020) An optimal approach of wind power assessment using Chebyshev metric for determining the Weibull distribution parameters. Sustainable Energy Technologies and Assessments, 37, 100612. https://doi.org/10.1016/j.seta.2019.100612
  38. Salami, A., A., Ajavon, A., S., A., Kodjo, M., K., & Bedja, K., S. (2013) Contribution to improving the modeling of wind and evaluation of the wind potential of the site of Lomé: Problems of taking into account the frequency of calm winds. Renewable energy, 50, 449-455. DOI: https://doi.org/10.1016/j.renene.2012.06.057
  39. Salami, A., A., Ajavon, A., S., A., Kodjo, M., K., & Bedja, K., S., (2016) Evaluation of Wind Potential for an Optimum Choice of Wind Turbine Generator on the Sites of Lomé, Accra, and Cotonou Located in the Gulf of Guinea. International Journal of Renewable Energy Development, 5(3), 211-223. DOI: http://dx.doi.org/10.14710/ijred.5.3.211-223
  40. Salami, A., A., Ajavon, A., S., A., Kodjo M., K., Ouedraogo S. and Bedja K.-S. (2018) The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters. Int. Journal of Renewable Energy Development 7(2), 139-150. DOI: https://doi.org/10.14710/ijred.7.2.139-150
  41. Sathyajith, M., and Geeta, S., P. (2011) Advances in Wind Energy Conversion Technology. Springer-Verlag Berlin Heidelberg. DOI: 10.1007/978-3-540-88-258-9
  42. Seguro, J., V., and Lambert T., W. (2000) Modern Estimation of the Parameters of the Weibull Wind Speed Distribution for Wind Energy Analysis. Journal of Wind Energy Engineering and Industrial Aerodynamics, 85(1), 75 – 84. DOI: https://doi.org/10.1016/S0167-6105(99)00122-1
  43. Shoaib, M., Siddiqui, I., Rehman, S., Khan, S., Alhems, L., M. (2019) Assessment of wind energy potential using wind energy conversion system. Journal of Cleaner Production, 216, 346-360
  44. Signe, E., B., K., Kanmogne, G., A., Meva’a D., E., L. (2019) Comparison of seven numerical methods for determining Weibull parameters of wind for sustainable energy in Douala, Cameroon. International Journal of Energy Sector Management, 13(4), 903-915. https://doi.org/10.1108/IJESM-07-2018-0014
  45. Shu, Z., R., Li, Q., S,, Chan, P., W. (2015) Statistical analysis of wind characteristics and wind energy potential in Hong Kong. Energy Convers Manage, 101, 644–657. DOI: https://doi.org/10.1016/j.enconman.2015.05.070
  46. Tizpar, A,, Satkin, M,, Roshan, M., B., Armoudli, Y. (2014) Wind resource assessment and wind power potential of Mil-E Nader region in Sistan and Baluchestan Province, Iran-Part 1: Annual energy estimation. Energy Convers Manage, 79, 273–80. https://doi.org/10.1016/j.enconman.2013.10.004
  47. Usta I. (2016) An innovative estimation method regarding Weibull parameters for wind energy applications. Energy, 106, 301-314. https://doi.org/10.1016/j.energy.2016.03.068
  48. Wais, P. (2017) Two and three-parameter Weibull distribution in available wind power analysis. Renewable Energy, 103, 15-29. https://doi.org/10.1016/j.renene.2016.10.041
  49. Warit W., Yutthana T., Jompob W. (2015) Comparative Study of Five Methods to Estimate Weibull Parameters for Wind Speed on Phangan Island, Thailand. Energy Procedia, 79, 976-981. https://doi.org/10.1016/j.egypro.2015.11.596

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