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All issues Volume 357 (2022) MATEC Web Conf., 357 (2022) 08005 References
Open Access
Issue
MATEC Web Conf.
Volume 357, 2022
25th Polish-Slovak Scientific Conference on Machine Modelling and Simulations (MMS 2020)
Article Number 08005
Number of page(s) 10
Section Theoretical and Applied Mathematics in Engineering
DOI https://doi.org/10.1051/matecconf/202235708005
Published online 22 June 2022
  1. J. Galliková, V. Stuchly, R. Poprocky, P. Volna, P. Model Calculations of posterior reliability indicators for the proposal of the maintenance system. MATEC Web of Conferences 157 (2018). [Google Scholar]
  2. K.C. Datsiou, M. Overend The strength of aged glass. Glass Structures & Engineering 2, 105–120 (2017). [CrossRef] [Google Scholar]
  3. A. Gagliano, F. Nocera, F. Patania, A. Capizzi Assessment of micro-wind turbines performance in the urban environments: an aided methodology through geographical information systems. International Journal of Energy and Environmental Engineering 4, 43, (2013). [CrossRef] [Google Scholar]
  4. R. Ponechal, P. Jurás Full scale measurements and simulations of the wind speed in the close proximity of the building skin. MATEC Web Conferences 107 (2017). [Google Scholar]
  5. P. Jurás, P. Durica Influence of wind direction on wind-driven rain load as boundary condition for HAM simulation on high rise buildings with usage of traditional cement-lime coatings. Building simulation 2015: 14th international conference of IBPSA 842849 (2015). [Google Scholar]
  6. A. De Souza, D. Ozonur Statistical Behaviour of O3, Ox, NO, NO2 and NOx in Urban Environment. Ozone: Science & Engineering 42, 1, 66–78 (2020). [CrossRef] [Google Scholar]
  7. C.G. Justus, W.R. Hargraves, A. Mikhail, D. Grabber Methods for estimating wind speed frequency distributions. Journal of Applied Meteorology 17, 350–353 (1978). [CrossRef] [Google Scholar]
  8. M.J.M. Stevens, P.T. Smulders The estimation of the parameters of the Weibull wind speed distribution for wind energy utilization purposes. Wind Engineering 3, 132–145 (1979). [Google Scholar]
  9. K. Conradsen, L.B. Nielsen, L.P. Prahm Review of Weibull statistics for estimation of wind speed distributions. Journal of Climate and Applied Meteorology 23, 1173–1183 (1994). [Google Scholar]
  10. I.Y.F. Lun, J.C. Lam A study of Weibull parameters using long-term wind observation. Renewable Energy 20, 145–153 (2000). [CrossRef] [Google Scholar]
  11. J.V. Seguro, T.W. Lambert Modern estimation of the parameters of the Weibull speed distribution for wind energy analysis. Journal of Wind Engineering and Industrial Aerodynamics 85, 75–84 (2000). [CrossRef] [Google Scholar]
  12. A.S.S. Dorvlo Estimating wind speed distribution. Energy Conversion and Management 43, 2311–2318 (2002). [CrossRef] [Google Scholar]
  13. A.N. Celik A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey. Renewable Energy 29, 593–604 (2003). [Google Scholar]
  14. E.K. Akpinar, S. Akpinar Determination of the wind energy potential for Maden, Turkey. Energy Conversion and Management 45, 2901–2914 (2004). [CrossRef] [Google Scholar]
  15. P. Ramirez, J.A. Carta Influence of the data sampling interval in the estimation of parameters of the Weibull wind speed probability density distribution a case study. Energy Conversion and Management 45, 2419–2438 (2005). [CrossRef] [Google Scholar]
  16. J.A. Carta, P. Ramirez, S. Velasquez A review wind speed probability distributions used in wind energy analysis Case studies in the Canary Islands. Renewable and Sustainable Energy Rewiews 13, 933–955 (2009). [CrossRef] [Google Scholar]
  17. I. Pobociková, Z. Sedliacková, J. Simon Statistical analysis of wind speed data based on Weibull and Rayleigh distribution. Communications- Scientific Letters of the University of Zilina 16, 136–141 (2014). [CrossRef] [Google Scholar]
  18. Y. Kassem, H. Çamur, M.A. Abugharara Assessment of wind energy potential for selecting small-scale wind turbines in low wind locations in Libya: A comparative study. International Journal of Engineering Research and Technology 12(6), 820–836 (2019). [Google Scholar]
  19. C. Carrillo, J. Cidrás, E. Diaz-Dorado, A.F. Obando-Montaño An Approach to Determine the Weibull Parameters for Wind Energy Analysis: The Case of Galicia (Spain). Energies 7, 4, 2676–2700 (2014). [CrossRef] [Google Scholar]
  20. T. Aized, S.M.S. Rehman, S. Kamran, A. Kazim, S. Ubaidur Rehman Design and analysis of wind pump for wind conditions in Pakistan. Advances in Mechanical Engineering (2019). [Google Scholar]
  21. Y. El Khchine, M. Sriti, N.E. El Kadri Elyamani Evaluation of wind energy potential and trends in Morocco. Heliyon 5 (6), art. no. e01830 (2019). [CrossRef] [Google Scholar]
  22. F.H. Mahmood, A.K. Resen, A.B. Khamees Wind characteristic analysis based on Weibull distribution of Al-Salman site, Iraq. Energy Reports 6, 79–87 (2020). [CrossRef] [Google Scholar]
  23. V. Katinas, M. Marciukaitis, G. Gecevicius, A. Markevicius Statistical Analysis of Wind Characteristics Based on Weibull Methods for Estimation of Power Generation in Lithuania. Renewable Energy 113, 190–201 (2017). [CrossRef] [Google Scholar]
  24. V.S.S. Balaguru, N.J. Swaroopan, K. Raju, M.H. Alsharif, M.-K. Kim Techno-economic investigation of wind energy potential in selected sites with uncertainty factors. Sustainability 13, 4, 1–32 (2021). [Google Scholar]
  25. Y. Kassem, H. Çamur, R.A.F. Aateg Exploring solar and wind energy as a power generation source for solving the electricity crisis in Libya. Energies 13, 14, (2020). [Google Scholar]
  26. Liptovsky Mikulás town homepage, https://www.mikulas.sk - access 26.03.2020. [Google Scholar]
  27. Climate Data.Org, https://en.climate-data.org/europe/slovakia/region-of-zilina/liptovsky-mikulas-7300/- access 26.03.2020. [Google Scholar]
  28. I. Pobociková, Z. Sedliacková, J. Simon, D. Jurásová Statistical analysis of the wind speed at mountain site Chopok, Slovakia, using Weibull distribution. IOP Conf. Series: Materials Science and Engineering 776 (2020). [Google Scholar]
  29. S.A. Akdag, A. Dinler A new method to estimate Weibull parameters for wind energy applications. Energy Conversion and Management 50, 1761–1766 (2009). [CrossRef] [Google Scholar]
  30. P. Battacharya Weibull distribution for estimating the parameters. Wind Energy Management ed P Battacharya (InTech) chapter 1, 1–12 (2011). [Google Scholar]
  31. I. Tizgui, F. El Guezar, H. Bouzahir, B. Benaid Comparison of methods in estimating Weibull parameters for wind energy applications. International Journal of Energy Sector Management 11, 650–663 (2017). [CrossRef] [Google Scholar]
  32. T.P. Chang Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application. Appl. Energy 88, 272–282 (2011). [CrossRef] [Google Scholar]
  33. W. Werapun, Y. Tirawanichakul, J. Waewsak Comparative Study of Five Methods to Estimate Weibull Parameters for Wind Speed on Phangan Island, Thailand. Energy Procedia 79, 976–981 (2015). [CrossRef] [Google Scholar]
  34. M. Teimouri, S.M. Hoseini, S. Nadarajah Comparison of estimation methods for the Weibull distribution. Statistics 47(1), 93–109 (2013). [CrossRef] [Google Scholar]
  35. M. Sumair, T. Aized, S.A.R. Gardezi, S. Ubaidur Rehman, S.M.S. Rehman A novel method developed to estimate Weibull parameters. Energy Reports 6, 1715–1733 (2020). [CrossRef] [Google Scholar]
  36. D. Kang, K. Ko, J. Huh Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea. Energies 11, 2, (2018). [Google Scholar]
  37. A.K. Azad, M.G. Rasul, T. Yusaf Statistical Diagnosis of the Best Weibull Methods for Wind Power Assessment for Agricultural Applications. Energies MDPI Open Access Journal 7, 5, 1–30 (2014). [Google Scholar]
  38. I. Pobociková, Z. Sedliacková Comparison of four methods for estimating the Weibull distribution parameters. Applied mathematical sciences 8, 4137–4149 (2014). [CrossRef] [Google Scholar]
  39. T.B.M.J. Ouarda, C. Charron, F. Chebana Review of criteria for the selection of probability distributions for wind speed data and introduction of the moment and L-moment ratio diagram methods, with a case study. Energy Conversion and Management 124, 247–265 (2016). [CrossRef] [Google Scholar]
  40. W. Hare Assessment of Knowledge on Impacts of Climate Change- Contribution to the Specification of Art. 2 of the UNFCCC: Impacts on ecosystems, food production, water and socio-economic systems. External Expertise Report German Advisory Council on Global Change Berlin Germany (2003). [Google Scholar]

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