Evaluation of the performance of five distribution functions for estimating Weibull parameters for wind energy potential in Nigeria
Wind energy is one of the most sought after worldwide among renewable energy sources. In order to determine the wind potential of a particular site, the wind speed has to be characterized and the Weibull parameters accurately determined. Characterization of wind speed in terms of Weibull parameters...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Scientific African |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S246822762300491X |
| _version_ | 1827329798234439680 |
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| author | E.F. Nymphas R.O. Teliat |
| author_facet | E.F. Nymphas R.O. Teliat |
| author_sort | E.F. Nymphas |
| collection | DOAJ |
| description | Wind energy is one of the most sought after worldwide among renewable energy sources. In order to determine the wind potential of a particular site, the wind speed has to be characterized and the Weibull parameters accurately determined. Characterization of wind speed in terms of Weibull parameters for wind energy potential in Nigeria is scarce. Several methods of determining Weibull parameters exist in literature. In this study, five numerical methods, Weibull-two (W), Mixture Weibull (MW), Gamma (G), lognormal (LN) and normal (N), were used to analyse wind speed data from twelve stations measured at 10 m height in order to determine the best method for estimating Weibull parameters for each station and region. The methods were compared and their accuracies were determined by three goodness-of-fit (GOF) tests namely Maximum error of Kolmogorov–Smirnov (K-S), root mean square error (RMSE) and Chi square (χ2). The performances of the methods were ranked on a scale of 1–5 and the results revealed that the MW was ranked the best in all the stations by two of the GOFs tests. It was closely followed by LN. The Weibull and Gamma methods performed poorly. Thus the MW is selected as the best method for determining Weibull parameters accurately and hence wind energy potential for Nigeria. |
| first_indexed | 2024-03-07T15:46:21Z |
| format | Article |
| id | doaj.art-4aae365787b945ad8766081996d948d4 |
| institution | Directory Open Access Journal |
| issn | 2468-2276 |
| language | English |
| last_indexed | 2024-03-07T15:46:21Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Scientific African |
| spelling | doaj.art-4aae365787b945ad8766081996d948d42024-03-05T04:30:11ZengElsevierScientific African2468-22762024-03-0123e02037Evaluation of the performance of five distribution functions for estimating Weibull parameters for wind energy potential in NigeriaE.F. Nymphas0R.O. Teliat1Corresponding author.; Department of Physics, University of Ibadan, NigeriaDepartment of Physics, University of Ibadan, NigeriaWind energy is one of the most sought after worldwide among renewable energy sources. In order to determine the wind potential of a particular site, the wind speed has to be characterized and the Weibull parameters accurately determined. Characterization of wind speed in terms of Weibull parameters for wind energy potential in Nigeria is scarce. Several methods of determining Weibull parameters exist in literature. In this study, five numerical methods, Weibull-two (W), Mixture Weibull (MW), Gamma (G), lognormal (LN) and normal (N), were used to analyse wind speed data from twelve stations measured at 10 m height in order to determine the best method for estimating Weibull parameters for each station and region. The methods were compared and their accuracies were determined by three goodness-of-fit (GOF) tests namely Maximum error of Kolmogorov–Smirnov (K-S), root mean square error (RMSE) and Chi square (χ2). The performances of the methods were ranked on a scale of 1–5 and the results revealed that the MW was ranked the best in all the stations by two of the GOFs tests. It was closely followed by LN. The Weibull and Gamma methods performed poorly. Thus the MW is selected as the best method for determining Weibull parameters accurately and hence wind energy potential for Nigeria.http://www.sciencedirect.com/science/article/pii/S246822762300491XGoodness-of-fitWind energy potentialRenewable energy, Weibull parameters |
| spellingShingle | E.F. Nymphas R.O. Teliat Evaluation of the performance of five distribution functions for estimating Weibull parameters for wind energy potential in Nigeria Scientific African Goodness-of-fit Wind energy potential Renewable energy, Weibull parameters |
| title | Evaluation of the performance of five distribution functions for estimating Weibull parameters for wind energy potential in Nigeria |
| title_full | Evaluation of the performance of five distribution functions for estimating Weibull parameters for wind energy potential in Nigeria |
| title_fullStr | Evaluation of the performance of five distribution functions for estimating Weibull parameters for wind energy potential in Nigeria |
| title_full_unstemmed | Evaluation of the performance of five distribution functions for estimating Weibull parameters for wind energy potential in Nigeria |
| title_short | Evaluation of the performance of five distribution functions for estimating Weibull parameters for wind energy potential in Nigeria |
| title_sort | evaluation of the performance of five distribution functions for estimating weibull parameters for wind energy potential in nigeria |
| topic | Goodness-of-fit Wind energy potential Renewable energy, Weibull parameters |
| url | http://www.sciencedirect.com/science/article/pii/S246822762300491X |
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