참고문헌
- 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.
- 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.
- 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.
- BP (2018), Statistical Review of World Energy.
- 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.
- 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.
- 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.
- IEA (2014), World energy statistics 2014.
- IEA (2016), Energy policies of IEA countries, Turkey, 2016 review.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Lewis, C.D. (1982), Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting, Butterworth Scientific, London, Boston.
- 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.
- 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.
- 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.
- 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.