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

International Journal of Renewable Energy Development

Koffi Mawugno Kodjoa¹  Ayité Sénah Akoda Ajavona¹  Adekunlé Akim Salami¹  Seydou Ouedraogo² 

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

关键词: weibull parameter;    wind speed;    wind power density;    random sample;    statistical analysis;   

DOI  :  10.14710/ijred.2022.38511

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown