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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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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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