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Wind Characteristics and Potentials of Two-Parameter Weibull Distribution and Maximum Entropy-Based Distribution Functions at an Equatorial Location
Thorough knowledge of the wind characteristics and variations are of great importance in the development of wind energy resource in any location. This study examines the wind characteristics and assess the potential of two distribution functions in a low wind equatorial region of West Africa. High r...
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2022
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| LEADER | 00000njm a2000000a 4500 | ||
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| 001 | oai:repository.ui.edu.ng:123456789/11858 | ||
| 042 | |a dc | ||
| 720 | |a Otunla, T.A. |e author | ||
| 720 | |a Umoren A. K. |e author | ||
| 260 | |c 2022 | ||
| 520 | |a Thorough knowledge of the wind characteristics and variations are of great importance in the development of wind energy resource in any location. This study examines the wind characteristics and assess the potential of two distribution functions in a low wind equatorial region of West Africa. High resolution wind speed and direction data were obtained from a site in Nsukka, a location chosen in the region of study. Diurnal, seasonal and annual variations of both the wind speed and directions were examined. The potentials of two-parameter Weibull distribution and another distribution function based on Maximum Entropy principle (MEP) were assessed using R2 and root mean squared error (RMSE). The results indicated that day-time is windier than night-time. The transitions months of February, March and April have the highest wind speed. The dry season has greater energy potential than rainy season. The predominant wind direction lay within the sectors: South-South-West and East. The predominant wind sector for February, March and April is South-East. The R2 for daily, sub-seasonal day-time and night-time, monthly, and annual ranged between 0.90 and 0.99 for both MEP-based and Weibull distributions. The daily, sub-seasonal day-time and night-time, monthly, and annual RMSE also ranged between 0.011 to 0.075 for MEP-based and Weibull distribution respectively. Thus, both MEP-based and Weibull two-parameter distribution functions can be used to model wind data at the location of study. | ||
| 024 | 8 | |a 2600-7924 | |
| 024 | 8 | |a ui_art_otunla_wind_2022 | |
| 024 | 8 | |a Journal of Science and Technology 14 (2), pp. 40-51 | |
| 024 | 8 | |a https://repository.ui.edu.ng/handle/123456789/11858 | |
| 653 | |a Wind energy | ||
| 653 | |a Weibull distribution | ||
| 653 | |a Maximum entropy principle | ||
| 653 | |a Equatorial region | ||
| 245 | 0 | 0 | |a Wind Characteristics and Potentials of Two-Parameter Weibull Distribution and Maximum Entropy-Based Distribution Functions at an Equatorial Location |