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Estimation of the wind speed in Sivas province by using the artificial neural networks

  • Gurlek, Cahit (Department of Mechanical Engineering, Sivas Cumhuriyet University) ;
  • Sahin, Mustafa (Turkish Railway Machines Industry Inc.) ;
  • Akkoyun, Serkan (Department of Physics, Sivas Cumhuriyet University)
  • 투고 : 2019.10.24
  • 심사 : 2021.02.17
  • 발행 : 2021.02.25

초록

In this study, the artificial neural network (ANN) method was used for estimating the monthly mean wind speed of Sivas, in the central part of Turkey. Eighteen years of wind speed data obtained from nine measurement stations during the period of 2000-2017 at 10 m height was used for ANN analysis. It was found that mean absolute percentage error (MAPE) ranged from 3.928 to 6.662, mean bias error (MBE) ranged from -0.089 to -0.003, while root mean square error (RMSE) ranged from 0.050 to 0.157 and R2 ranged from 0.86 to 0.966. ANN models provide a good approximation of the wind speed for all measurement stations, however, a tendency to underestimate is also obvious.

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참고문헌

  1. Akinci, T.C. and Nogay, H.S. (2012), "Wind speed correlation between neighboring measuring stations", Arab. J. Sci. Eng., 37, 1007-1019. https://doi.org/10.1007/s13369-012-0223-4.
  2. Bilgili, M. and Sahin, B. (2010), "Comparative analysis of regression and artificial neural network models for wind speed prediction", Meteor. Atmos. Phy., 109, 61-72. https://doi.org/10.1007/s00703-010-0093-9.
  3. Bilgili, M. and Sahin, B. (2013), "Wind speed prediction of target station from reference stations data", Energy Sources, Part A, 35(5), 455-466. https://doi.org/10.1080/15567036.2010.512906.
  4. BP (2018), Statistical Review of World Energy.
  5. Cam, E., Arcaklioglu, E., Cavusoglu, A. and Akbiyik, B. (2005), "A classification mechanism for determining average wind speed and power in several regions of Turkey using artificial neural networks", Renew. Energy, 30, 227-239. https://doi.org/10.1016/j.renene.2004.05.008.
  6. Ekins, P. and McGlade, C. (2015), "The geographical distribution of fossil fuels unused when limiting global warming to 2℃", Nature, 517, 187-190. https://doi.org/10.1038/nature14016.
  7. Franchini, M. and Mannucci, P.M. (2015), "Impact on human health of climate changes", Europ. J. Intern. Medicine, 26, 1-5. https://dx.doi.org/10.1016/j.ejim.2014.12.008.
  8. IEA (2014), World energy statistics 2014.
  9. IEA (2016), Energy policies of IEA countries, Turkey, 2016 review.
  10. Isik, A.H., Orgen, F.K.D., Sirin, C., Tuncer, A.D., Gungor, A. (2019), "Prediction of wind blowing durations of Eastern Turkey with machine learning for integration of renewable energy and organic farming-stock raising", Sci. J. Mehmet Akif Univ., 2(3), 47-53.
  11. Jackson, R.B., Le Quere, C., Andrew, R.M., Canadell, J.G., Korsbakken, J.I., Liu, Z., Peters, G.P. and Zheng, B. (2018), "Global energy growth is outpacing decarbonization", Environ. Res. Lett., 13, 120401. https://doi.org/10.1088/1748-9326/aaf303.
  12. Kilic, B. (2019), "Determination of wind dissipation maps and wind energy potential in Burdur province of Turkey using geographic information system (GIS)", Sustain. Energy Technol. Assessments, 36, 100555. https://doi.org/10.1016/j.seta.2019.100555.
  13. Kundapura, S. and Hegde, A.V. (2017), "Current approaches of artificial intelligence in breakwaters - A review", Ocean Syst. Eng., 7(2), 75-87. https://doi.org/10.12989/ose.2017.7.2.075.
  14. Kwatra, N. (2002), "Application of artificial neural network for determination of wind induced pressures on gable roof", Wind Struct., 5(1), 1-14. https://dx.doi.org/10.12989/was.2002.5.1.001.
  15. Lam, J.C., Wan, K.K.W. and Yang L. (2008), "Solar radiation modelling using ANNs for different climates in China", Energy Convers. Managem., 49, 1080-1090. https://doi.org/10.1016/j.enconman.2007.09.021.
  16. Lewis, C.D. (1982), Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting, Butterworth Scientific, London, Boston.
  17. Oztopal, A. (2006), "Artificial neural network approach to spatial estimation of wind velocity data", Energy Convers. Managem., 47, 395-406. https://doi.org/10.1016/j.enconman.2005.05.009.
  18. Smith, K.R., Frumkin, H., Balakrishnan, K., Butler, C.D., Chafe, Z.A., Fairlie, I., Kinney, P., Kjellstrom, T., Mauzerall, D.L., McKone, T.E., McMichael, A.J. and Schneider, M. (2013), "Energy and human health", Annu. Rev. Public Health, 34, 159-188. https://doi.org/10.1146/annurev-publhealth-031912-114404.
  19. Ulkat, D. and Gunay, M.E. (2018), "Prediction of mean monthly wind speed and optimization of wind power by artificial neural networks using geographical and atmospheric variables: case of Aegean Region of Turkey", Neural Comput. Appli., 30, 3037-3048. https://doi.org/10.1007/s00521-017-2895-x.
  20. Vargas, S.A., Esteves, G.R.T., Macaira, P.M., Bastos, B.Q., Oliveira, F.L.C. and Souza, R.C. (2019), "Wind power generation: A review and a research agenda", J. Cleaner Production, 218, 850-870. https://doi.org/10.1016/j.jclepro.2019.02.015.