A comprehensive analysis of Weibull distribution parameter estimation methods to improve wind potential assessment

Authors

Keywords:

Wind potential, Weibull distribution, Parameters, Determination methods, Wind speeds, Estimation, Wind energy density, Brazil

Abstract

The integration of various technologies and the techno-economic analysis are crucial for the successful deployment of renewable energies. This approach makes it possible to maximize the efficient use of clean energy sources, reduce costs, and improve system resilience. The work employs theoretical techniques to calculate specific characteristics of the Weibull distribution using experimental data collected by the Climate Research Unit (CRU Time-Series (TS) v. 4.0). 10 methods were used to estimate the Weibull distribution parameters. 10 methods were used to estimate the Weibull distribution parameters. The “Wreg” method has shown to be the most suitable for determining the Weibull distribution parameters in 23 Brazilian locations. On the other hand, the “PM” method proved to be suitable for four locations in Brazil, while the other methods were not considered adequate.

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Author Biographies

Amaury de Souza, Universidade Federal de Mato Grosso do Sul

Has a degree in Physics from the Federal University of São Carlos (1976), a master's degree in Agronomy (Agricultural Meteorology) from the Federal University of Viçosa (1988), a PhD in Environmental Technologies from the Federal University of Mato Grosso do Sul (2013). Participation as an Associate Researcher at Brown University. Associate Editor of the Journal of Mathematical Techniques and Computational Mathematics (JMTCM). Editor-Chef of the Universidade Federal de Mato Grosso do Sul, Associate Editor of the Journal Water (ISSN 2073-4441), Journal of Energy Research and Reviews, International Journal of Environment and Climate Change. Experience in Environmental Meteorology, Urban Meteorology, Climatology, Fire Meteorology and Health Meteorology.

Elias Silva de Medeiros, Universidade Federal da Grande Dourados

He holds a Bachelor's degree in Statistics from the State University of Paraíba (2011), a Master's degree in Sciences (Statistics and Agricultural Experimentation) from the Escola Superior de Agricultura Luiz de Queiroz - USP (2014) and a PhD in Statistics and Agricultural Experimentation from the Federal University of Lavras ( 2018). He is currently an adjunct professor at the Federal University of Grande Dourados. Has experience in the area of ​​Probability and Statistics, with an emphasis on Statistics

Carolina Cristina Bicalho, Universidade Estadual de Mato Grosso do Sul

PhD in Statistics in Agricultural Experimentation from the Federal University of Lavras (MG). Master's degree in Systems Engineering from the Federal University of Lavras. Lato-Sensu Postgraduate Degree in Information Systems Administration from the Federal University of Lavras. Degree in Mathematics from the Federal University of Lavras (MG) and Degree in Technology in Systems Analysis and Development from the Federal Center for Technological Education of Bambuí - MG. Computer Science Technician from the Federal Center for Technological Education of Bambuí-MG. Area of ​​expertise: Mathematical Modeling, Statistics, Spatial Statistics, Time Series, Regression. She is currently a professor at the State University of Mato Grosso do Sul, working in the disciplines of calculus, numerical calculation, analytical geometry and linear algebra.

Ricardo Alves de Olinda, Universidade Estadual da Paraíba

He has a degree in Statistics from the State University of Paraíba, a master's degree in Statistics and Agricultural Experimentation from the Federal University of Lavras (2008) and a PhD in Statistics and Agricultural Experimentation from the University of São Paulo, Escola Superior de Agricultura "Luiz de Queiroz" (2012). He is currently an associate professor at the Department of Statistics, a permanent professor at the Master's Degree in Public Health at the State University of Paraíba, a permanent professor at the Master's Degree in Environmental Management and Technology at the Federal University of Mato Grosso-UFMT, and a Collaborator in the Postgraduate Program -Graduation in Biosciences and Health (PPG-BioS), Rondonópolis Campus. Coordinator of the Multi-User Big Data and Geoinformation Center at UEPB, he was a member of the Insurance and Risk Study Group ''GESER'' at the Luiz de Queiroz College of Agriculture, he worked in the quantitative modeling nucleus. Reviewer of periodicals: Revista Ciência Agronômica (UFC. On-line), Revista de Economia e Sociologia Rural (Printed), Revista Agroambiente (UFRR. On-line), Revista Brasileira de Parasitologia Veterinária (RBPV), Revista Ciência e Natura, Revista Principia and Brazilian Journal of Biometrics (RBB). Leader of the Applied and Computational Statistics research group at the State University of Paraíba. Has experience in the area of ​​Probability and Statistics, with an emphasis on Spatial Statistics, Data Science, theory of extreme values, planning and statistical analysis of experiments and multivariate statistics

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Published

2024-11-29

How to Cite

Souza, A. de, Medeiros, E. S. de, Bicalho, C. C., & Olinda, R. A. de. (2024). A comprehensive analysis of Weibull distribution parameter estimation methods to improve wind potential assessment. Ciência E Natura, 46, e87369. Retrieved from https://periodicos.ufsm.br/cienciaenatura/article/view/87369

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