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

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)...

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Main Authors: Salami, Adekunlé Akim (Author), Ouedraogo, Seydou (Author), Kodjoa, Koffi Mawugno (Author), Ajavona, Ayité Sénah Akoda (Author)
Format: EJournal Article
Published: Center of Biomass & Renewable Energy, Diponegoro University, 2022-02-01.
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LEADER 02459 am a22002773u 4500
001 IJRED_UNDIP_38511_pdf
042 |a dc 
100 1 0 |a Salami, Adekunlé Akim  |e author 
700 1 0 |a Ouedraogo, Seydou  |e author 
700 1 0 |a Kodjoa, Koffi Mawugno  |e author 
700 1 0 |a Ajavona, Ayité Sénah Akoda  |e author 
245 0 0 |a Influence of the Random Data Sampling in Estimation of Wind Speed Resource: Case Study 
260 |b Center of Biomass & Renewable Energy, Diponegoro University,   |c 2022-02-01. 
500 |a https://ejournal.undip.ac.id/index.php/ijred/article/view/38511 
520 |a 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. 
540 |a Copyright (c) 2022 The Authors. Published by Centre of Biomass and Renewable Energy (CBIORE) 
540 |a https://creativecommons.org/licenses/by-sa/4.0 
546 |a eng 
690 |a Weibull parameter; wind speed; wind power density; random sample; statistical analysis 
655 7 |a info:eu-repo/semantics/article  |2 local 
655 7 |a info:eu-repo/semantics/publishedVersion  |2 local 
655 7 |2 local 
786 0 |n International Journal of Renewable Energy Development; Vol 11, No 1 (2022): February 2022; 133-143 
786 0 |n 2252-4940 
787 0 |n https://ejournal.undip.ac.id/index.php/ijred/article/view/38511/pdf 
856 4 1 |u https://ejournal.undip.ac.id/index.php/ijred/article/view/38511/pdf  |z Get Fulltext