Accurate modelling of wind speed is very important for the assessment of the wind energy potential of a region. The two-parameter Weibull distribution is widely used to model wind speed. Many different numerical methods can be used to estimate the shape and scale parameters of the Weibull distribution function. This paper proposes the estimation of parameters based on a novel approach, the Information Geometry Method (IGM). Non-Euclidean geometry and the Riemannian metric called the Fisher metric or the information metric are used in this approach. Differential equations derived from the Fisher information matrix are solved for the Weibull statistical manifold by the shooting method. The IGM is compared with the graphical method, maximum likelihood method, method of Lysen, method of Justus, and power density method. In particular, it is shown that this approach has a better performance than the other estimation methods according to the power density results for the periods of three years from 2012 to 2014 for Bilecik, a city of Turkey. Therefore, the IGM as a new approach used for the estimation of parameters of the Weibull distribution function can be a good alternative for the assessment of the wind energy potential.
1. GWEC. Global Wind Energy Council. Global wind statistics 2016. <http://www.gwec.net> (accessed 2017-02-23).
2. Pishgar-Komleh, S. H., Keyhani, A., and Sefeedpari, P. Wind speed and power density analysis based on Weibull and Rayleigh distributions (a case study: Firouzkooh county of Iran). Renew. Sust. Energ. Rev., 2015, 42, 313–322.
https://doi.org/10.1016/j.rser.2014.10.028
3. Garcia, A., Torres, J. L., Prieto, E., and Francisco, A. D. Fitting wind speed distributions: a case study. Sol. Energy, 1998, 62(2), 139–144.
https://doi.org/10.1016/S0038-092X(97)00116-3
4. Luna, R. E. and Church, H. W. Estimation of long-term concentrations using a universal wind speed distribution. J. Appl. Meteorol., 1974, 13, 910–916.
https://doi.org/10.1175/1520-0450(1974)013<0910:EOLTCU>2.0.CO;2
5. Justus, C. G., Hargraves, W. R., and Yalcin, A. Nationwide assessment of potential output from wind-powered generators. J. Appl. Meteorol., 1976, 15, 673–678.
https://doi.org/10.1175/1520-0450(1976)015<0673:NAOPOF>2.0.CO;2
6. Kiss, P. and Janosi, I. M. Comprehensive empirical analysis of ERA-40 surface wind speed distribution over Europe. Energ. Convers. Manage., 2008, 49, 2142–2151.
https://doi.org/10.1016/j.enconman.2008.02.003
7. Bardsley, W. E. Note on the use of the inverse Gaussian distribution for wind energy applications. J. Appl. Meteorol., 1980, 19, 1126–1130.
https://doi.org/10.1175/1520-0450(1980)019<1126:NOTUOT>2.0.CO;2
8. Vogel, R. M., McMahon, T. A., and Chiew, F. H. S. Floodflow frequency model selection in Australia. J. Hydrol., 1993, 146, 421–449.
https://doi.org/10.1016/0022-1694(93)90288-K
9. Guttman, N. B., Hosking, J. R. M., and Wallis, J. R. Regional precipitation quantile values for the continental United States computed from L-moments. J. Clim., 1993, 6, 2326–2340.
https://doi.org/10.1175/1520-0442(1993)006<2326:RPQVFT>2.0.CO;2
10. Stedinger, J. R. Fitting log normal distributions to hydrologic data. Water Resour. Res., 1980, 16, 481–490.
https://doi.org/10.1029/WR016i003p00481
11. Mert, I. and Karakuş, C. A statistical analysis of wind speed data using Burr, generalized gamma, and Weibull distributions in Antakya, Turkey. Turk. J. Elec. Eng. & Comp. Sci., 2015, 23, 1571–1586.
https://doi.org/10.3906/elk-1402-66
12. Sohoni, V., Gupta, S., and Nema, R. A comparative analysis of wind speed probability distributions for wind power assessment of four sites. Turk. J. Elec. Eng. & Comp. Sci., 2016, 24, 4724–4735.
https://doi.org/10.3906/elk-1412-207
13. Morgan, E. C., Lackner, M., Vogel, R. M., and Baise, L. G. Probability distributions for offshore wind speeds. Energ. Convers. Manage., 2011, 52, 15–26.
https://doi.org/10.1016/j.enconman.2010.06.015
14. Takle, E. S. and Brown, J. M. Note on the use of Weibull statistics to characterise wind-speed data. J. Appl. Meteorol., 1978, 17, 556–559.
https://doi.org/10.1175/1520-0450(1978)017<0556:NOTUOW>2.0.CO;2
15. Jaramillo, O. A. and Borja, M. A. Wind speed analysis in La Ventosa, Mexico: a bimodal probability distribution case. Renew. Energ., 2004, 29, 1613–1630.
https://doi.org/10.1016/j.renene.2004.02.001
16. Rosen, K., Van Buskirk, R., and Garbesi, K. Wind energy potential of coastal Eritrea: an analysis of sparse wind data. Sol. Energy, 1999, 66, 201–213.
https://doi.org/10.1016/S0038-092X(99)00026-2
17. Carta, J. A., Ramirez, P., and Velazquez, S. A review of wind speed probability distributions used in wind energy analysis case studies in the Canary Islands. Renew. Sust. Energ. Rev., 2009, 13, 933–955.
https://doi.org/10.1016/j.rser.2008.05.005
18. Auwera, V., Meyer, L. F., and Malet, L. M. The use of the Weibull three parameter model for estimating mean wind power densities. J. Appl. Meteorol., 1980, 19, 819–825.
https://doi.org/10.1175/1520-0450(1980)019<0819:TUOTWT>2.0.CO;2
19. Stevens, M. J. and 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.
20. Justus, C. G., Hargraves, W. R., Mikhail, A., and Graber, D. Methods for estimating wind speed frequency distributions.
J. Appl. Meteorol., 1978, 17, 350–353.
https://doi.org/10.1175/1520-0450(1978)017<0350:MFEWSF>2.0.CO;2
21. Akdag, S. A. and Dinler, A. A new method to estimate Weibull parameters for wind energy applications. Energ. Convers. Manage., 2009, 50, 1761–1766.
https://doi.org/10.1016/j.enconman.2009.03.020
22. Lysen, E. H. Introduction to Wind Energy. SWD Publication SWD 82-1, The Netherlands, 1983.
23. Carneiro, T. C., Melo, S. P., Carvalho, P. C., and Braga, A. P. D. S. Particle Swarm Optimization method for estimation of Weibull parameters: a case study for the Brazilian northeast region. Renew. Energ., 2016, 86, 751–759.
https://doi.org/10.1016/j.renene.2015.08.060
24. Shamshirband, S., Keivani, A., Mohammadi, K., Lee, M., Hamid, S. H. A., and Petkovic, D. Assessing the proficiency of adaptive neuro-fuzzy system to estimate wind power density: case study of Aligoodarz, Iran. Renew. Sust. Energ. Rev., 2016, 59, 429–435.
https://doi.org/10.1016/j.rser.2015.12.269
25. Arslan, O. Technoeconomic analysis of electricity generation from wind energy in Kutahya, Turkey. Energy, 2010, 35, 120–131.
https://doi.org/10.1016/j.energy.2009.09.002
https://doi.org/10.1016/j.energy.2009.12.039
26. Malik, A. and Al-Badi, A. H. Economics of Wind turbine as an energy fuel saver – a case study for remote application in Oman. Energy, 2009, 34, 1573–1578.
https://doi.org/10.1016/j.energy.2009.07.002
27. Liu, F. J. and Chang, T. P. Validity analysis of maximum entropy distribution based on different moment constraints for wind energy assessment. Energy, 2011, 36, 1820–1826.
https://doi.org/10.1016/j.energy.2010.11.033
28. Chang, T. P., Liu, F. J., Ko, H. H., Cheng, S. P., Sun, L. C., and Kuo, S. C. Comparative analysis on power curve models of wind turbine generator in estimating capacity factor. Energy, 2014, 73, 88–95.
https://doi.org/10.1016/j.energy.2014.05.091
29. Arslan, T., Bulut, Y. M., and Yavuz, A. A. Comparative study of numerical methods for determining Weibull parameters for wind energy potential. Renew. Sust. Energ. Rev., 2014, 40, 820–825.
https://doi.org/10.1016/j.rser.2014.08.009
30. Carrasco-Díaz, M., Rivas, D., Orozco-Contreras, M., and Anchez-Montante, O. S. An assessment of wind power potential along the coast of Tamaulipas, northeastern Mexico. Renew. Energ., 2015, 78, 295–305.
https://doi.org/10.1016/j.renene.2015.01.007
31. Manwell, J. F., McGowan, J. G., and Rogers, A. L. Wind Energy Explained: Theory, Design and Application. John Wiley & Sons, Amherst, USA, 2002.
https://doi.org/10.1002/0470846127
32. Mathew, S. Wind Energy: Fundamentals, Resource Analysis and Economics. Springer-Verlag, Berlin–Heidelberg, 2006.
https://doi.org/10.1007/3-540-30906-3
33. Boudia, S. M. and Guerri, O. Investigation of wind power potential at Oran, northwest of Algeria. Energ. Convers. Manage., 2015, 105, 81–92.
https://doi.org/10.1016/j.enconman.2015.07.055
34. Tizpar, A., Satkin, M., Roshan, M. B., and Armoudli, Y. Wind resource assessment and wind power potential of Mil-E Nader region in Sistan and Baluchestan Province, Iran – Part 1: Annual energy estimation. Energ. Convers. Manage., 2014, 79, 273–280.
https://doi.org/10.1016/j.enconman.2013.10.004
35. Patel, M. R. Wind and Solar Power Systems: Design, Analysis, and Operation. CRC Press, 2005.
https://doi.org/10.1201/9781420039924
36. Mohammadi, K., Alavi, O., Mostafaeipour, A., Goudarzi, N., and Jalilvand, M. Assessing different parameters estimation methods of Weibull distribution to compute wind power density. Energ. Convers. Manage., 2016, 108, 322–335.
https://doi.org/10.1016/j.enconman.2015.11.015
37. Galanis, G., Chu, P. C., Kallos, G., Kuo, Y.-H., and Dodson, C. T. J. Wave height characteristics in the north Atlantic ocean: a new approach based on statistical and geometrical techniques. Stoch. Environ. Res. Risk Assess., 2012, 26, 83–103.
https://doi.org/10.1007/s00477-011-0540-2
38. Amari, S.-i. Differential-Geometrical Methods in Statistics. Lecture Notes in Statistics, 28. Springer-Verlag, Berlin, 1985.
https://doi.org/10.1007/978-1-4612-5056-2
39. Amari, S.-i. and Nagaoka, H. Methods of Information Geometry. Translations of Mathematical Monographs, 191. American Mathematical Society, Oxford University Press, 2000.
40. Arwini, K. and Dodson, C. T. J. Alpha-geometry of the Weibull manifold. Presented at The Second Basic Science Conference, Al-Fatah University, Tripoli, Libya, 4–8 November, 2007. http://www.maths.manchester.ac.uk/~kd/PREPRINTS/ WeibullTripoli07.pdf (accessed 2017-12-05).
41. Famelis, I., Galanis, G., Ehrhardt, M., and Triantafyllou, D. Classical and quasi-Newton methods for a meteorological parameters prediction boundary value problem. Appl. Math., 2014, 8, 2683–2693.
https://doi.org/10.12785/amis/080604
42. Arwini, K. and Dodson, C. T. J. Information Geometry: Near Randomness and Near Independence. Lecture Notes in Mathematics, 1953. Springer-Verlag, Berlin, 2008.
https://doi.org/10.1007/978-3-540-69393-2