Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil

Applied Energy

Volumn 89, Issue 1, 2012, Pages 395-400

  Costa Rocha, Paulo Alexandre ^a   de Sousa, Ricardo Coelho ^a   de Andrade, Carla Freitas ^a   da Silva, Maria Eugênia Vieira ^a  

^a FEDERAL UNIVERSITY OF CEARÁ   (Brazil)

Author keywords

Energy efficiency; Numerical methods; Weibull distribution; Wind energy

Indexed keywords

ELECTRIC GENERATORS; ENERGY EFFICIENCY; MEAN SQUARE ERROR; STATISTICAL TESTS; WEIBULL DISTRIBUTION; WIND POWER;

CHI-SQUARE TESTS; ELECTRICITY GENERATION; EQUIVALENT ENERGY; NORTHEAST REGIONS; RENEWABLE SOURCES; ROOT MEAN SQUARE ERRORS; WEIBULL; WEIBULL PARAMETERS; WIND ENERGY GENERATION; WIND SPEED DATA;

NUMERICAL METHODS;

ELECTRICITY GENERATION; ENERGY EFFICIENCY; NUMERICAL MODEL; RENEWABLE RESOURCE; VARIANCE ANALYSIS; WEIBULL THEORY; WIND POWER; WIND VELOCITY;

BRAZIL; CEARA; PARACURU;

EID: 80053336497     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2011.08.003     Document Type: Article

References (17)

1. SEINFRACE. Secretary of infrastructure-government of state of Ceara: wind energy resource atlas of state of Ceara, Brazil; 2001.
2. Celik A.N. A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey. Renew Energy 2003, 29:593-604.
3. Akpinar E.K., Akpinar S. Determination of the wind energy potential for Maden-Elazig. Energy Convers Manage 2004, 45:2901-2914.
4. Deaves D.M., Lines I.G. On the fitting of low mean wind speed data to the Weibull distribution. J Wind Eng Indus Aerodynam 1997, 66:169-178.
5. Celik A.N., Makkawi A., Muneer T. Critical evaluation of wind speed frequency distribution functions. J Renew Sustain Energy 2010, 2:013102.
6. Ohunakin O.S., Adaramola M.S., Oyewola O.M. Wind energy evaluation for electricity generation using WECS in seven selected locations in Nigeria. Appl Energy 2011, 88:3197-3206.
7. Ucar A., Balo F. Evaluation of wind energy potential and electricity generation at six locations in Turkey. Appl Energy 2009, 86:1864-1872.
8. Chang T.P. Estimation of wind energy potential using different probability density functions. Appl Energy 2011, 88:1848-1856.
9. Kwon S.D. Uncertainty analysis of wind energy potential assessment. Appl Energy 2010, 87:856-865.
10. Chang T.P. Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application. Appl Energy 2011, 88:272-282.
11. Fisher R.A. Frequency distribution of the values of the correlation coefficient in samples from an indefinitely large population. Biometrika, London 1915, 10:507-521.
12. Stevens M.J., Smulders P.T. The estimation of the parameters of the Weibull wind speed distribution for wind energy utilization purposes. Wind Eng 1979, 3:132-145.
13. Akdag S.A., Dinler A. A new method to estimate Weibull parameters for wind energy applications. Energy Convers Manage 2009, 50:1761-1766.
14. Justus C.G., Mikhail A. Height variation of wind speed and wind distribution statistics. Geophys Res Lett 1976, 3:261-264.
15. Silva GR. Características de vento da Região Nordeste, análise, modelagem e aplicações para projetos de centrais eólicas. Master's thesis, Recife Pe; 2003.
16. Surucu B. Goodness-of-fit tests for multivariate distributions. Commun Stat-Theory Methods 2006, 35:1319-1331.
17. Surucu B. A power comparison and simulation study of goodness-of-fit tests. Comput Math Appl 2008, 56:1617-1625.