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: | , , , |
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| Format: | EJournal Article |
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Center of Biomass & Renewable Energy, Diponegoro University,
2022-02-01.
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| Online Access: | Get Fulltext |
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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 |