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Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application

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  1. D’Amico, Guglielmo & Petroni, Filippo & Prattico, Flavio, 2015. "Economic performance indicators of wind energy based on wind speed stochastic modeling," Applied Energy, Elsevier, vol. 154(C), pages 290-297.
  2. Guedes, Kevin S. & de Andrade, Carla F. & Rocha, Paulo A.C. & Mangueira, Rivanilso dos S. & de Moura, Elineudo P., 2020. "Performance analysis of metaheuristic optimization algorithms in estimating the parameters of several wind speed distributions," Applied Energy, Elsevier, vol. 268(C).
  3. Altunkaynak, Abdüsselam & Erdik, Tarkan & Dabanlı, İsmail & Şen, Zekai, 2012. "Theoretical derivation of wind power probability distribution function and applications," Applied Energy, Elsevier, vol. 92(C), pages 809-814.
  4. Zhang, Hua & Yu, Yong-Jing & Liu, Zhi-Yuan, 2014. "Study on the Maximum Entropy Principle applied to the annual wind speed probability distribution: A case study for observations of intertidal zone anemometer towers of Rudong in East China Sea," Applied Energy, Elsevier, vol. 114(C), pages 931-938.
  5. Chang, Tian-Pau & Ko, Hong-Hsi & Liu, Feng-Jiao & Chen, Pai-Hsun & Chang, Ying-Pin & Liang, Ying-Hsin & Jang, Horng-Yuan & Lin, Tsung-Chi & Chen, Yi-Hwa, 2012. "Fractal dimension of wind speed time series," Applied Energy, Elsevier, vol. 93(C), pages 742-749.
  6. Estefania Artigao & Antonio Vigueras-Rodríguez & Andrés Honrubia-Escribano & Sergio Martín-Martínez & Emilio Gómez-Lázaro, 2021. "Wind Resource and Wind Power Generation Assessment for Education in Engineering," Sustainability, MDPI, vol. 13(5), pages 1-27, February.
  7. Mehr Gul & Nengling Tai & Wentao Huang & Muhammad Haroon Nadeem & Moduo Yu, 2019. "Assessment of Wind Power Potential and Economic Analysis at Hyderabad in Pakistan: Powering to Local Communities Using Wind Power," Sustainability, MDPI, vol. 11(5), pages 1-23, March.
  8. Suzer, Ahmet Esat & Atasoy, Vehbi Emrah & Ekici, Selcuk, 2021. "Developing a holistic simulation approach for parametric techno-economic analysis of wind energy," Energy Policy, Elsevier, vol. 149(C).
  9. Bilal, Boudy & Adjallah, Kondo Hloindo & Yetilmezsoy, Kaan & Bahramian, Majid & Kıyan, Emel, 2021. "Determination of wind potential characteristics and techno-economic feasibility analysis of wind turbines for Northwest Africa," Energy, Elsevier, vol. 218(C).
  10. Chaurasiya, Prem Kumar & Ahmed, Siraj & Warudkar, Vilas, 2018. "Comparative analysis of Weibull parameters for wind data measured from met-mast and remote sensing techniques," Renewable Energy, Elsevier, vol. 115(C), pages 1153-1165.
  11. Guangyu Fan & Yanru Wang & Bo Yang & Chuanxiong Zhang & Bin Fu & Qianqian Qi, 2022. "Characteristics of Wind Resources and Post-Project Evaluation of Wind Farms in Coastal Areas of Zhejiang," Energies, MDPI, vol. 15(9), pages 1-18, May.
  12. Carneiro, Tatiane C. & Melo, Sofia P. & Carvalho, Paulo C.M. & Braga, Arthur Plínio de S., 2016. "Particle Swarm Optimization method for estimation of Weibull parameters: A case study for the Brazilian northeast region," Renewable Energy, Elsevier, vol. 86(C), pages 751-759.
  13. Alkhalidi, Mohamad A. & Al-Dabbous, Shoug Kh. & Neelamani, S. & Aldashti, Hassan A., 2019. "Wind energy potential at coastal and offshore locations in the state of Kuwait," Renewable Energy, Elsevier, vol. 135(C), pages 529-539.
  14. Liu, Feng-Jiao & Chen, Pai-Hsun & Kuo, Shyi-Shiun & Su, De-Chuan & Chang, Tian-Pau & Yu, Yu-Hua & Lin, Tsung-Chi, 2011. "Wind characterization analysis incorporating genetic algorithm: A case study in Taiwan Strait," Energy, Elsevier, vol. 36(5), pages 2611-2619.
  15. Sunoh Kim & Jin Hur, 2020. "A Probabilistic Modeling Based on Monte Carlo Simulation of Wind Powered EV Charging Stations for Steady-States Security Analysis," Energies, MDPI, vol. 13(20), pages 1-13, October.
  16. Dongbum Kang & Kyungnam Ko & Jongchul Huh, 2018. "Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea," Energies, MDPI, vol. 11(2), pages 1-19, February.
  17. Birgir Hrafnkelsson & Gudmundur V. Oddsson & Runar Unnthorsson, 2016. "A Method for Estimating Annual Energy Production Using Monte Carlo Wind Speed Simulation," Energies, MDPI, vol. 9(4), pages 1-14, April.
  18. Deep, Sneh & Sarkar, Arnab & Ghawat, Mayur & Rajak, Manoj Kumar, 2020. "Estimation of the wind energy potential for coastal locations in India using the Weibull model," Renewable Energy, Elsevier, vol. 161(C), pages 319-339.
  19. Yunus Yetis & Kambiz Tehrani & Mo Jamshidi, 2022. "Wind Speed Forecasting using Machine Learning Approach based on Meteorological Data-A case study," Energy and Environment Research, Canadian Center of Science and Education, vol. 12(2), pages 1-11, December.
  20. Jung, Sungmoon & Arda Vanli, O. & Kwon, Soon-Duck, 2013. "Wind energy potential assessment considering the uncertainties due to limited data," Applied Energy, Elsevier, vol. 102(C), pages 1492-1503.
  21. Liang, Zhengtang & Liang, Jun & Zhang, Li & Wang, Chengfu & Yun, Zhihao & Zhang, Xu, 2015. "Analysis of multi-scale chaotic characteristics of wind power based on Hilbert–Huang transform and Hurst analysis," Applied Energy, Elsevier, vol. 159(C), pages 51-61.
  22. Navid Goudarzi & Kasra Mohammadi & Alexandra St. Pé & Ruben Delgado & Weidong Zhu, 2020. "Wind Resource Assessment and Economic Viability of Conventional and Unconventional Small Wind Turbines: A Case Study of Maryland," Energies, MDPI, vol. 13(22), pages 1-15, November.
  23. Eryilmaz, Serkan, 2018. "Reliability analysis of multi-state system with three-state components and its application to wind energy," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 58-63.
  24. Xu, Lei & Wang, Shengwei & Tang, Rui, 2019. "Probabilistic load forecasting for buildings considering weather forecasting uncertainty and uncertain peak load," Applied Energy, Elsevier, vol. 237(C), pages 180-195.
  25. Arslan, Talha & Bulut, Y. Murat & Altın Yavuz, Arzu, 2014. "Comparative study of numerical methods for determining Weibull parameters for wind energy potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 820-825.
  26. César Henrique Mattos Pires & Felipe M. Pimenta & Carla A. D'Aquino & Osvaldo R. Saavedra & Xuerui Mao & Arcilan T. Assireu, 2020. "Coastal Wind Power in Southern Santa Catarina, Brazil," Energies, MDPI, vol. 13(19), pages 1-23, October.
  27. Shamshirband, Shahaboddin & Keivani, Afram & Mohammadi, Kasra & Lee, Malrey & Hamid, Siti Hafizah Abd & Petkovic, Dalibor, 2016. "Assessing the proficiency of adaptive neuro-fuzzy system to estimate wind power density: Case study of Aligoodarz, Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 429-435.
  28. Mekalathur B Hemanth Kumar & Saravanan Balasubramaniyan & Sanjeevikumar Padmanaban & Jens Bo Holm-Nielsen, 2019. "Wind Energy Potential Assessment by Weibull Parameter Estimation Using Multiverse Optimization Method: A Case Study of Tirumala Region in India," Energies, MDPI, vol. 12(11), pages 1-21, June.
  29. Qin, Zhilong & Li, Wenyuan & Xiong, Xiaofu, 2013. "Incorporating multiple correlations among wind speeds, photovoltaic powers and bus loads in composite system reliability evaluation," Applied Energy, Elsevier, vol. 110(C), pages 285-294.
  30. Emeksiz, Cem & Tan, Mustafa, 2022. "Wind speed estimation using novelty hybrid adaptive estimation model based on decomposition and deep learning methods (ICEEMDAN-CNN)," Energy, Elsevier, vol. 249(C).
  31. Sumair, Muhammad & Aized, Tauseef & Aslam Bhutta, Muhammad Mahmood & Siddiqui, Farrukh Arsalan & Tehreem, Layba & Chaudhry, Abduallah, 2022. "Method of Four Moments Mixture-A new approach for parametric estimation of Weibull Probability Distribution for wind potential estimation applications," Renewable Energy, Elsevier, vol. 191(C), pages 291-304.
  32. Camilo Carrillo & José Cidrás & Eloy Díaz-Dorado & Andrés Felipe Obando-Montaño, 2014. "An Approach to Determine the Weibull Parameters for Wind Energy Analysis: The Case of Galicia (Spain)," Energies, MDPI, vol. 7(4), pages 1-25, April.
  33. Hdidouan, Daniel & Staffell, Iain, 2017. "The impact of climate change on the levelised cost of wind energy," Renewable Energy, Elsevier, vol. 101(C), pages 575-592.
  34. Chang, Tian-Pau & Liu, Feng-Jiao & Ko, Hong-Hsi & Cheng, Shih-Ping & Sun, Li-Chung & Kuo, Shye-Chorng, 2014. "Comparative analysis on power curve models of wind turbine generator in estimating capacity factor," Energy, Elsevier, vol. 73(C), pages 88-95.
  35. Tiam Kapen, Pascalin & Jeutho Gouajio, Marinette & Yemélé, David, 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, Elsevier, vol. 159(C), pages 1188-1198.
  36. Liu, Feng Jiao & Chang, Tian Pau, 2011. "Validity analysis of maximum entropy distribution based on different moment constraints for wind energy assessment," Energy, Elsevier, vol. 36(3), pages 1820-1826.
  37. Olgun Aydin & Bartłomiej Igliński & Krzysztof Krukowski & Marek Siemiński, 2022. "Analyzing Wind Energy Potential Using Efficient Global Optimization: A Case Study for the City Gdańsk in Poland," Energies, MDPI, vol. 15(9), pages 1-22, April.
  38. Usta, Ilhan, 2016. "An innovative estimation method regarding Weibull parameters for wind energy applications," Energy, Elsevier, vol. 106(C), pages 301-314.
  39. Murthy, K.S.R. & Rahi, O.P., 2017. "A comprehensive review of wind resource assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1320-1342.
  40. Chen, Xinping & Foley, Aoife & Zhang, Zenghai & Wang, Kaimin & O'Driscoll, Kieran, 2020. "An assessment of wind energy potential in the Beibu Gulf considering the energy demands of the Beibu Gulf Economic Rim," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
  41. Chandel, S.S. & Ramasamy, P. & Murthy, K.S.R, 2014. "Wind power potential assessment of 12 locations in western Himalayan region of India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 530-545.
  42. Kang, Dongbum & Ko, Kyungnam & Huh, Jongchul, 2015. "Determination of extreme wind values using the Gumbel distribution," Energy, Elsevier, vol. 86(C), pages 51-58.
  43. Shu, Z.R. & Li, Q.S. & He, Y.C. & Chan, P.W., 2016. "Observations of offshore wind characteristics by Doppler-LiDAR for wind energy applications," Applied Energy, Elsevier, vol. 169(C), pages 150-163.
  44. Bahrami, Arian & Teimourian, Amir & Okoye, Chiemeka Onyeka & Khosravi, Nima, 2019. "Assessing the feasibility of wind energy as a power source in Turkmenistan; a major opportunity for Central Asia's energy market," Energy, Elsevier, vol. 183(C), pages 415-427.
  45. Sergei Kolesnik & Yossi Rabinovitz & Michael Byalsky & Asher Yahalom & Alon Kuperman, 2023. "Assessment of Wind Speed Statistics in Samaria Region and Potential Energy Production," Energies, MDPI, vol. 16(9), pages 1-35, May.
  46. Carapellucci, Roberto & Giordano, Lorena, 2013. "A methodology for the synthetic generation of hourly wind speed time series based on some known aggregate input data," Applied Energy, Elsevier, vol. 101(C), pages 541-550.
  47. Wang, Jianzhou & Huang, Xiaojia & Li, Qiwei & Ma, Xuejiao, 2018. "Comparison of seven methods for determining the optimal statistical distribution parameters: A case study of wind energy assessment in the large-scale wind farms of China," Energy, Elsevier, vol. 164(C), pages 432-448.
  48. Ju-Young Shin & Changsam Jeong & Jun-Haeng Heo, 2018. "A Novel Statistical Method to Temporally Downscale Wind Speed Weibull Distribution Using Scaling Property," Energies, MDPI, vol. 11(3), pages 1-27, March.
  49. Riccardo De Blasis & Giovanni Batista Masala & Filippo Petroni, 2021. "A Multivariate High-Order Markov Model for the Income Estimation of a Wind Farm," Energies, MDPI, vol. 14(2), pages 1-16, January.
  50. Emilio Gómez-Lázaro & María C. Bueso & Mathieu Kessler & Sergio Martín-Martínez & Jie Zhang & Bri-Mathias Hodge & Angel Molina-García, 2016. "Probability Density Function Characterization for Aggregated Large-Scale Wind Power Based on Weibull Mixtures," Energies, MDPI, vol. 9(2), pages 1-15, February.
  51. Sunoh Kim & Jin Hur, 2020. "Probabilistic Approaches to the Security Analysis of Smart Grid with High Wind Penetration: The Case of Jeju Island’s Power Grids," Energies, MDPI, vol. 13(21), pages 1-13, November.
  52. Kim, SunOh & Hur, Jin, 2021. "Probabilistic power output model of wind generating resources for network congestion management," Renewable Energy, Elsevier, vol. 179(C), pages 1719-1726.
  53. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Optimal operation value of combined wind power and energy storage in multi-stage electricity markets," Applied Energy, Elsevier, vol. 235(C), pages 1153-1168.
  54. He, J.Y. & Li, Q.S. & Chan, P.W. & Zhao, X.D., 2023. "Assessment of future wind resources under climate change using a multi-model and multi-method ensemble approach," Applied Energy, Elsevier, vol. 329(C).
  55. Pagnini, Luisa C. & Burlando, Massimiliano & Repetto, Maria Pia, 2015. "Experimental power curve of small-size wind turbines in turbulent urban environment," Applied Energy, Elsevier, vol. 154(C), pages 112-121.
  56. Guglielmo D’Amico & Giovanni Masala & Filippo Petroni & Robert Adam Sobolewski, 2020. "Managing Wind Power Generation via Indexed Semi-Markov Model and Copula," Energies, MDPI, vol. 13(16), pages 1-21, August.
  57. Katinas, Vladislovas & Marčiukaitis, Mantas & Gecevičius, Giedrius & Markevičius, Antanas, 2017. "Statistical analysis of wind characteristics based on Weibull methods for estimation of power generation in Lithuania," Renewable Energy, Elsevier, vol. 113(C), pages 190-201.
  58. Akdağ, Seyit Ahmet & Güler, Önder, 2018. "Alternative Moment Method for wind energy potential and turbine energy output estimation," Renewable Energy, Elsevier, vol. 120(C), pages 69-77.
  59. Celik, Ali N. & Kolhe, Mohan, 2013. "Generalized feed-forward based method for wind energy prediction," Applied Energy, Elsevier, vol. 101(C), pages 582-588.
  60. Oluseyi O. Ajayi & Richard O. Fagbenle & James Katende & Julius M. Ndambuki & David O. Omole & Adekunle A. Badejo, 2014. "Wind Energy Study and Energy Cost of Wind Electricity Generation in Nigeria: Past and Recent Results and a Case Study for South West Nigeria," Energies, MDPI, vol. 7(12), pages 1-27, December.
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  62. Eryilmaz, Serkan & Devrim, Yilser, 2019. "Theoretical derivation of wind plant power distribution with the consideration of wind turbine reliability," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 192-197.
  63. Abul Kalam Azad & Mohammad Golam Rasul & Talal Yusaf, 2014. "Statistical Diagnosis of the Best Weibull Methods for Wind Power Assessment for Agricultural Applications," Energies, MDPI, vol. 7(5), pages 1-30, May.
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  65. Allouhi, A. & Zamzoum, O. & Islam, M.R. & Saidur, R. & Kousksou, T. & Jamil, A. & Derouich, A., 2017. "Evaluation of wind energy potential in Morocco's coastal regions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 311-324.
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  67. Toja-Silva, Francisco & Lopez-Garcia, Oscar & Peralta, Carlos & Navarro, Jorge & Cruz, Ignacio, 2016. "An empirical–heuristic optimization of the building-roof geometry for urban wind energy exploitation on high-rise buildings," Applied Energy, Elsevier, vol. 164(C), pages 769-794.
  68. Dinler, Ali, 2013. "A new low-correlation MCP (measure-correlate-predict) method for wind energy forecasting," Energy, Elsevier, vol. 63(C), pages 152-160.
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  72. Shoaib, Muhammad & Siddiqui, Imran & Amir, Yousaf Muhammad & Rehman, Saif Ur, 2017. "Evaluation of wind power potential in Baburband (Pakistan) using Weibull distribution function," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1343-1351.
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  78. Örkcü, H. Hasan & Aksoy, Ertugˇrul & Dogˇan, Mustafa İsa, 2015. "Estimating the parameters of 3-p Weibull distribution through differential evolution," Applied Mathematics and Computation, Elsevier, vol. 251(C), pages 211-224.
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  84. Shu, Z.R. & Li, Q.S. & Chan, P.W., 2015. "Investigation of offshore wind energy potential in Hong Kong based on Weibull distribution function," Applied Energy, Elsevier, vol. 156(C), pages 362-373.
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