Evaluation of eleven numerical methods for determining weibull parameters for wind energy generation in the caribbean region of Colombia
The two-parameter Weibull probability density function (PDF) is widely utilized by different researchers and engineers to fit wind speed data for statistical analysis and modeling. The characterization of wind resources in the frequency and probability domain is necessary to estimate the power outpu...
- Autores:
-
Vega Zuñiga, Samuel
Rueda-Bayona, Juan Gabriel
Ospino C., Adalberto
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2022
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/9259
- Acceso en línea:
- https://hdl.handle.net/11323/9259
https://doi.org/10.18280/mmep.090124
https://repositorio.cuc.edu.co/
- Palabra clave:
- Weibull parameters
PDF Weibull
Wind speed
Shape
Scale parameters
- Rights
- openAccess
- License
- Atribución 4.0 Internacional (CC BY 4.0)
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Evaluation of eleven numerical methods for determining weibull parameters for wind energy generation in the caribbean region of Colombia |
| title |
Evaluation of eleven numerical methods for determining weibull parameters for wind energy generation in the caribbean region of Colombia |
| spellingShingle |
Evaluation of eleven numerical methods for determining weibull parameters for wind energy generation in the caribbean region of Colombia Weibull parameters PDF Weibull Wind speed Shape Scale parameters |
| title_short |
Evaluation of eleven numerical methods for determining weibull parameters for wind energy generation in the caribbean region of Colombia |
| title_full |
Evaluation of eleven numerical methods for determining weibull parameters for wind energy generation in the caribbean region of Colombia |
| title_fullStr |
Evaluation of eleven numerical methods for determining weibull parameters for wind energy generation in the caribbean region of Colombia |
| title_full_unstemmed |
Evaluation of eleven numerical methods for determining weibull parameters for wind energy generation in the caribbean region of Colombia |
| title_sort |
Evaluation of eleven numerical methods for determining weibull parameters for wind energy generation in the caribbean region of Colombia |
| dc.creator.fl_str_mv |
Vega Zuñiga, Samuel Rueda-Bayona, Juan Gabriel Ospino C., Adalberto |
| dc.contributor.author.spa.fl_str_mv |
Vega Zuñiga, Samuel Rueda-Bayona, Juan Gabriel Ospino C., Adalberto |
| dc.subject.proposal.eng.fl_str_mv |
Weibull parameters PDF Weibull Wind speed Shape Scale parameters |
| topic |
Weibull parameters PDF Weibull Wind speed Shape Scale parameters |
| description |
The two-parameter Weibull probability density function (PDF) is widely utilized by different researchers and engineers to fit wind speed data for statistical analysis and modeling. The characterization of wind resources in the frequency and probability domain is necessary to estimate the power output potential of new wind energy projects. Considering that exist a variety of Weibull equations evidenced in the literature review, this article evaluates 11 different methods to calculate the shape and scale parameters of the Weibull PDF. In this sense, it was written an algorithm within a Matlab function that solves the 11 methods for calculating the Weibull PDF parameters. Wind speed data extracted from the ERA5 database was used as input data for applying the proposed algorithm, and statistical parameters such as the Root Mean Square Error (RMSE), the Relative Root Mean Square Error (RRMSE), and chi-square test (X2) we utilized for assessing the performance of each one of the 11 methods for modeling the wind distribution. The statistical results pointed that the numerical iteration methods (e.g. maximum likelihood method) showed better results than parameterized equations such as the Graphical Method, hence, this research recommends the implicit methods for determining Weibull PDF parameters of wind speed data. |
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2022 |
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2022-06-16T14:13:18Z |
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2022-06-16T14:13:18Z |
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2022-02-28 |
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Vega-Zuñiga, S., Rueda-Bayona, J.G., Ospino-Castro, A. (2022). Evaluation of eleven numerical methods for determining Weibull parameters for wind energy generation in the Caribbean region of Colombia. Mathematical Modelling of Engineering Problems, Vol. 9, No. 1, pp. 194-199. https://doi.org/10.18280/mmep.090124 |
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2369-0739 |
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https://hdl.handle.net/11323/9259 |
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https://doi.org/10.18280/mmep.090124 |
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10.18280/mmep.090124 |
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2369-0747 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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Vega-Zuñiga, S., Rueda-Bayona, J.G., Ospino-Castro, A. (2022). Evaluation of eleven numerical methods for determining Weibull parameters for wind energy generation in the Caribbean region of Colombia. Mathematical Modelling of Engineering Problems, Vol. 9, No. 1, pp. 194-199. https://doi.org/10.18280/mmep.090124 2369-0739 10.18280/mmep.090124 2369-0747 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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https://hdl.handle.net/11323/9259 https://doi.org/10.18280/mmep.090124 https://repositorio.cuc.edu.co/ |
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eng |
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eng |
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Mathematical Modelling of Engineering Problems |
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[1] Carneiro, T.C., Melo, S.P., Carvalho, P.C., Braga, A.P.D.S. (2016). Particle swarm optimization method for estimation of Weibull parameters: A case study for the Brazilian northeast region. Renewable Energy, 86: 751- 759. https://doi.org/10.1016/j.renene.2015.08.060 [2] Pryor, S.C., Barthelmie, R.J., Bukovsky, M.S., Leung, L.R., Sakaguchi, K. (2020). Climate change impacts on wind power generation. Nature Reviews Earth & Environment, 1(12): 627-643. https://doi.org/10.1038/s43017-020-0101-7 [3] Haque, A.U., Mandal, P., Meng, J., Kaye, M.E., Chang, L. (2012). A new strategy for wind speed forecasting using hybrid intelligent models. In 2012 25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), Montreal, QC, Canada, pp. 1-4. https://doi.org/10.1109/CCECE.2012.6334847 [4] Mohammadi, K., Alavi, O., Mostafaeipour, A., Goudarzi, N., Jalilvand, M. (2016). Assessing different parameters estimation methods of Weibull distribution to compute wind power density. Energy Conversion and Management, 108: 322-335. https://doi.org/10.1016/j.enconman.2015.11.015 [5] Wais, P. (2017). A review of Weibull functions in wind sector. Renewable and Sustainable Energy Reviews, 70: 1099-1107. https://doi.org/10.1016/j.rser.2016.12.014 [6] Murthy, K.S.R., Rahi, O.P. (2017). A comprehensive review of wind resource assessment. Renewable and Sustainable Energy Reviews, 72: 1320-1342. https://doi.org/10.1016/j.rser.2016.10.038 [7] Guenoukpati, A., Salami, A.A., Kodjo, M.K., Napo, K. (2020). Estimating Weibull parameters for wind energy applications using seven numerical methods: Case studies of three coastal sites in West Africa. International Journal of Renewable Energy Development, 9(2): 217- 226, https://doi.org/10.14710/ijred.9.2.217-226 [8] Masseran, N. (2015). Evaluating wind power density models and their statistical properties. Energy, 84: 533- 541. https://doi.org/10.1016/j.energy.2015.03.018 [9] Chang, T.P. (2011). Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application. Applied Energy, 88(1): 272- 282. https://doi.org/10.1016/j.apenergy.2010.06.018 [10] Rocha, C. (2012). Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil. Appl. Energy, 89: 395-400. https://doi.org/10.1016/j.apenergy.2011.08.003 [11] Arslan, T., Bulut, Y.M., Yavuz, A.A. (2014). Comparative study of numerical methods for determining Weibull parameters for wind energy potential. Renewable and Sustainable Energy Reviews, 40: 820-825. https://doi.org/10.1016/j.rser.2014.08.009 [12] Kapen, P.T., Gouajio, M.J., Yemélé, D. (2020). Analysis and efficient comparison of ten numerical methods in estimating Weibull parameters for wind energy potential: Application to the city of Bafoussam, Cameroon. Renewable Energy, 159: 1188-1198. https://doi.org/10.1016/j.renene.2020.05.185 [13] Bilir, L., Imir, M., Devrim, Y., Albostan, A. (2015). Seasonal and yearly wind speed distribution and wind power density analysis based on Weibull distribution function. International Journal of Hydrogen Energy, 40(44): 15301-15310. https://doi.org/10.1016/j.ijhydene.2015.04.140 [14] Usta, I. (2016). An innovative estimation method regarding Weibull parameters for wind energy applications. Energy, 106: 301-314. https://doi.org/10.1016/j.energy.2016.03.068 [15] Chaurasiya, P.K., Ahmed, S., Warudkar, V. (2018). Comparative analysis of Weibull parameters for wind data measured from met-mast and remote sensing techniques. Renewable Energy, 115: 1153-1165. 10.1016/j.renene.2017.08.014 [16] Tizgui, I., El Guezar, F., Bouzahir, H., Benaid, B. (2017). Comparison of methods in estimating Weibull parameters for wind energy applications. International Journal of Energy Sector, 11(4): 650-663. https://doi.org/10.1108/IJESM-06-2017-0002 [17] Rueda-Bayona, J.G., Elles Pérez, C.J., Sánchez Cotte, E.H., González Ariza, Á.L., Rivillas Ospina, G.D. (2016). Identificación de patrones de variabilidad climática a partir de análisis de componentes principales, Fourier y clúster k-medias. Tecnura, 20(50): 55-68. https://doi.org/10.14483/udistrital.jour.tecnura.2016.4.a 04 [18] Rueda-Bayona, J.G. (2017). Identificación de la influencia de las variaciones convectivas en la generación de cargas transitorias y su efecto hidromecánico en las estructuras Offshore. PhD Thesis, Universidad del Norte, Barranquilla, Colombia. [19] Rueda-Bayona, J.G., Cabello Eras, J.J., Sagastume, A. (2021). Modeling wind speed with a long-term horizon and high-time interval with a hybrid Fourier-neural network model. Mathematical Modelling of Engineering Problems, 8(3): 431-440. https://doi.org/10.18280/mmep.080313 [20] Orimoloye, S., Horrillo-Caraballo, J., Karunarathna, H., Reeve, D.E. (2021). Wave overtopping of smooth impermeable seawalls under unidirectional bimodal sea conditions. Coastal Engineering, 165: 103792. https://doi.org/10.1016/j.coastaleng.2020.103792 |
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Vega Zuñiga, SamuelRueda-Bayona, Juan GabrielOspino C., Adalberto2022-06-16T14:13:18Z2022-06-16T14:13:18Z2022-02-28Vega-Zuñiga, S., Rueda-Bayona, J.G., Ospino-Castro, A. (2022). Evaluation of eleven numerical methods for determining Weibull parameters for wind energy generation in the Caribbean region of Colombia. Mathematical Modelling of Engineering Problems, Vol. 9, No. 1, pp. 194-199. https://doi.org/10.18280/mmep.0901242369-0739https://hdl.handle.net/11323/9259https://doi.org/10.18280/mmep.09012410.18280/mmep.0901242369-0747Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The two-parameter Weibull probability density function (PDF) is widely utilized by different researchers and engineers to fit wind speed data for statistical analysis and modeling. The characterization of wind resources in the frequency and probability domain is necessary to estimate the power output potential of new wind energy projects. Considering that exist a variety of Weibull equations evidenced in the literature review, this article evaluates 11 different methods to calculate the shape and scale parameters of the Weibull PDF. In this sense, it was written an algorithm within a Matlab function that solves the 11 methods for calculating the Weibull PDF parameters. Wind speed data extracted from the ERA5 database was used as input data for applying the proposed algorithm, and statistical parameters such as the Root Mean Square Error (RMSE), the Relative Root Mean Square Error (RRMSE), and chi-square test (X2) we utilized for assessing the performance of each one of the 11 methods for modeling the wind distribution. The statistical results pointed that the numerical iteration methods (e.g. maximum likelihood method) showed better results than parameterized equations such as the Graphical Method, hence, this research recommends the implicit methods for determining Weibull PDF parameters of wind speed data.International Information and Engineering Technology Association6 páginasapplication/pdfengAtribución 4.0 Internacional (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Evaluation of eleven numerical methods for determining weibull parameters for wind energy generation in the caribbean region of ColombiaArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85https://www.iieta.org/journals/mmep/paper/10.18280/mmep.090124ColombiaCaribbean regionCanadaMathematical Modelling of Engineering Problems[1] Carneiro, T.C., Melo, S.P., Carvalho, P.C., Braga, A.P.D.S. (2016). Particle swarm optimization method for estimation of Weibull parameters: A case study for the Brazilian northeast region. Renewable Energy, 86: 751- 759. https://doi.org/10.1016/j.renene.2015.08.060[2] Pryor, S.C., Barthelmie, R.J., Bukovsky, M.S., Leung, L.R., Sakaguchi, K. (2020). Climate change impacts on wind power generation. Nature Reviews Earth & Environment, 1(12): 627-643. https://doi.org/10.1038/s43017-020-0101-7[3] Haque, A.U., Mandal, P., Meng, J., Kaye, M.E., Chang, L. (2012). A new strategy for wind speed forecasting using hybrid intelligent models. In 2012 25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), Montreal, QC, Canada, pp. 1-4. https://doi.org/10.1109/CCECE.2012.6334847[4] Mohammadi, K., Alavi, O., Mostafaeipour, A., Goudarzi, N., Jalilvand, M. (2016). Assessing different parameters estimation methods of Weibull distribution to compute wind power density. Energy Conversion and Management, 108: 322-335. https://doi.org/10.1016/j.enconman.2015.11.015[5] Wais, P. (2017). A review of Weibull functions in wind sector. Renewable and Sustainable Energy Reviews, 70: 1099-1107. https://doi.org/10.1016/j.rser.2016.12.014[6] Murthy, K.S.R., Rahi, O.P. (2017). A comprehensive review of wind resource assessment. Renewable and Sustainable Energy Reviews, 72: 1320-1342. https://doi.org/10.1016/j.rser.2016.10.038[7] Guenoukpati, A., Salami, A.A., Kodjo, M.K., Napo, K. (2020). Estimating Weibull parameters for wind energy applications using seven numerical methods: Case studies of three coastal sites in West Africa. International Journal of Renewable Energy Development, 9(2): 217- 226, https://doi.org/10.14710/ijred.9.2.217-226[8] Masseran, N. (2015). Evaluating wind power density models and their statistical properties. Energy, 84: 533- 541. https://doi.org/10.1016/j.energy.2015.03.018[9] Chang, T.P. (2011). Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application. Applied Energy, 88(1): 272- 282. https://doi.org/10.1016/j.apenergy.2010.06.018[10] Rocha, C. (2012). Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil. Appl. Energy, 89: 395-400. https://doi.org/10.1016/j.apenergy.2011.08.003[11] Arslan, T., Bulut, Y.M., Yavuz, A.A. (2014). Comparative study of numerical methods for determining Weibull parameters for wind energy potential. Renewable and Sustainable Energy Reviews, 40: 820-825. https://doi.org/10.1016/j.rser.2014.08.009[12] Kapen, P.T., Gouajio, M.J., Yemélé, D. (2020). Analysis and efficient comparison of ten numerical methods in estimating Weibull parameters for wind energy potential: Application to the city of Bafoussam, Cameroon. Renewable Energy, 159: 1188-1198. https://doi.org/10.1016/j.renene.2020.05.185[13] Bilir, L., Imir, M., Devrim, Y., Albostan, A. (2015). Seasonal and yearly wind speed distribution and wind power density analysis based on Weibull distribution function. International Journal of Hydrogen Energy, 40(44): 15301-15310. https://doi.org/10.1016/j.ijhydene.2015.04.140[14] Usta, I. (2016). An innovative estimation method regarding Weibull parameters for wind energy applications. Energy, 106: 301-314. https://doi.org/10.1016/j.energy.2016.03.068[15] Chaurasiya, P.K., Ahmed, S., Warudkar, V. (2018). Comparative analysis of Weibull parameters for wind data measured from met-mast and remote sensing techniques. Renewable Energy, 115: 1153-1165. 10.1016/j.renene.2017.08.014[16] Tizgui, I., El Guezar, F., Bouzahir, H., Benaid, B. (2017). Comparison of methods in estimating Weibull parameters for wind energy applications. International Journal of Energy Sector, 11(4): 650-663. https://doi.org/10.1108/IJESM-06-2017-0002[17] Rueda-Bayona, J.G., Elles Pérez, C.J., Sánchez Cotte, E.H., González Ariza, Á.L., Rivillas Ospina, G.D. (2016). Identificación de patrones de variabilidad climática a partir de análisis de componentes principales, Fourier y clúster k-medias. Tecnura, 20(50): 55-68. https://doi.org/10.14483/udistrital.jour.tecnura.2016.4.a 04[18] Rueda-Bayona, J.G. (2017). Identificación de la influencia de las variaciones convectivas en la generación de cargas transitorias y su efecto hidromecánico en las estructuras Offshore. PhD Thesis, Universidad del Norte, Barranquilla, Colombia.[19] Rueda-Bayona, J.G., Cabello Eras, J.J., Sagastume, A. (2021). Modeling wind speed with a long-term horizon and high-time interval with a hybrid Fourier-neural network model. Mathematical Modelling of Engineering Problems, 8(3): 431-440. https://doi.org/10.18280/mmep.080313[20] Orimoloye, S., Horrillo-Caraballo, J., Karunarathna, H., Reeve, D.E. (2021). Wave overtopping of smooth impermeable seawalls under unidirectional bimodal sea conditions. 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