Abstract
Weibull distribution is often invoked to interpret and predict wind characteristics needed for effective design of wind power systems for different locations. In this paper, daily average wind data for Enugu (6.4°N; 7.5°E), Onitsha (6.8°N; 6.1°E) and Owerri (5.5°N; 7.0°E) over a 25-year period is modeled in terms of the Weibull distribution in order to accurately predict wind potentials for the locations. The monthly and annual wind speed probability density distributions at 10 m meteorological height were analyzed and the Weibull shape and scale factors were empirically determined for the locations. The predicted and measured wind speed probability density distributions of the locations are compared and the accuracy of the model determined for each location using Pearson product moment correlation coefficient (r) and root-mean-square error (ξ). We find r and ξ to be 0.64, 1.40, 0.67, 1.17 and 0.93, 1.55, respectively, for Enugu, Onitsha and Owerri. The results suggest that the model can be used, with acceptable accuracy, for predicting wind energy output needed for preliminary design assessment of wind machines for the locations. Key words: Renewable energy-general, wind, Weibull distribution.
Highlights
Energy plays a central role in economic development and industrialization of any nation
Weibull distribution is often invoked to interpret and predict wind characteristics needed for effective design of wind power systems for different locations
Daily average wind data for Enugu (6.4°N; 7.5°E), Onitsha (6.8°N; 6.1°E) and Owerri (5.5°N; 7.0°E) over a 25-year period is modeled in terms of the Weibull distribution in order to accurately predict wind potentials for the locations
Summary
Twenty-five years of wind data in south-eastern locations of Nigeria: Modeling and prediction of wind potential. Weibull distribution is often invoked to interpret and predict wind characteristics needed for effective design of wind power systems for different locations. Daily average wind data for Enugu (6.4°N; 7.5°E), Onitsha (6.8°N; 6.1°E) and Owerri (5.5°N; 7.0°E) over a 25-year period is modeled in terms of the Weibull distribution in order to accurately predict wind potentials for the locations. The predicted and measured wind speed probability density distributions of the locations are compared and the accuracy of the model determined for each location using Pearson product moment correlation coefficient (r) and root-mean-square error (ξ). The results suggest that the model can be used, with acceptable accuracy, for predicting wind energy output needed for preliminary design assessment of wind machines for the locations
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
