Modeling climate change impact on wind power resources using adaptive neuro-fuzzy inference system

References

• Alizadeh, M. J., Rajaee, T., & Motahari, M. (2016). Flow forecasting models using hydrologic and hydrometric data. Proceedings of the Institution of Civil Engineers-Water Management.
   Google Scholar
• Alizdeh, M. J., Joneyd, P. M., Motahhari, M., Ejlali, F., & Kiani, H. (2015). A wavelet-ANFIS model to estimate sedimentation in dam reservoir. International Journal of Computer Applications, 114(9), 19–25. doi: 10.5120/20007-1958
   Google Scholar
• Allahdadi, M. N., Chegini, V., Fotouhi, N., Golshani, A., Jandaghi, A. M., Moradi, M., … Taebi, S. (2004). Wave modeling and Hindcast of the Caspian Sea. Proceedings of the 6th International Conference on Coasts, Ports, and Marine Structures, Tehran, Iran.
   Google Scholar
• Amirinia, G., Kamranzad, B., & Mafi, S. (2017). Wind and wave energy potential in southern Caspian Sea using uncertainty analysis. Energy, 120, 332–345. doi: 10.1016/j.energy.2016.11.088
   Web of Science ®Google Scholar
• Aqil, M., Kita, I., Yano, A., & Nishiyama, S. (2007). Analysis and prediction of flow from local source in a river basin using a Neuro-fuzzy modeling tool. Journal of Environmental Management, 85(1), 215–223. doi: 10.1016/j.jenvman.2006.09.009
   PubMed Web of Science ®Google Scholar
• Beyaztas, U., Salih, S. Q., Chau, K.-W., Al-Ansari, N., & Yaseen, Z. M. (2019). Construction of functional data analysis modeling strategy for global solar radiation prediction: Application of cross-station paradigm. Engineering Applications of Computational Fluid Mechanics, 13(1), 1165–1181. doi: 10.1080/19942060.2019.1676314
   Web of Science ®Google Scholar
• Chang, T.-J., Chen, C.-L., Tu, Y.-L., Yeh, H.-T., & Wu, Y.-T. (2015). Evaluation of the climate change impact on wind resources in Taiwan Strait. Energy Conversion and Management, 95, 435–445. doi: 10.1016/j.enconman.2015.02.033
   Web of Science ®Google Scholar
• Cheng, C.-T., Lin, J.-Y., Sun, Y.-G., & Chau, K. (2005). Long-term prediction of discharges in Manwan Hydropower using adaptive-network-based fuzzy inference systems models. International Conference on Natural Computation.
   Google Scholar
• Davy, R., Gnatiuk, N., Pettersson, L., & Bobylev, L. (2018). Climate change impacts on wind energy potential in the European domain with a focus on the Black Sea. Renewable and Sustainable Energy Reviews, 81, 1652–1659. doi: 10.1016/j.rser.2017.05.253
   Web of Science ®Google Scholar
• de Jong, P., Barreto, T. B., Tanajura, C. A., Kouloukoui, D., Oliveira-Esquerre, K. P., Kiperstok, A., & Torres, E. A. (2019). Estimating the impact of climate change on wind and solar energy in Brazil using a South American regional climate model. Renewable Energy, 141, 390–401. doi: 10.1016/j.renene.2019.03.086
   Web of Science ®Google Scholar
• Dosio, A. (2016). Projections of climate change indices of temperature and precipitation from an ensemble of bias-adjusted high-resolution EURO-CORDEX regional climate models. Journal of Geophysical Research: Atmospheres, 121(10), 5488–5511.
   Web of Science ®Google Scholar
• Fotovatikhah, F., Herrera, M., Shamshirband, S., Chau, K.-W., Faizollahzadeh Ardabili, S., & Piran, M. J. (2018). Survey of computational intelligence as basis to big flood management: Challenges, research directions and future work. Engineering Applications of Computational Fluid Mechanics, 12(1), 411–437. doi: 10.1080/19942060.2018.1448896
   Web of Science ®Google Scholar
• Khanali, M., Ahmadzadegan, S., Omid, M., Nasab, F. K., & Chau, K. W. (2018). Optimizing layout of wind farm turbines using genetic algorithms in Tehran province, Iran. International Journal of Energy and Environmental Engineering, 9(4), 399–411. doi: 10.1007/s40095-018-0280-x
   Web of Science ®Google Scholar
• Koenigk, T., Berg, P., & Döscher, R. (2015). Arctic climate change in an ensemble of regional CORDEX simulations. Polar Research, 34(1), 24603. doi: 10.3402/polar.v34.24603
   Web of Science ®Google Scholar
• Likens, G., Benbow, M., Burton, T., Van Donk, E., Downing, J., & Gulati, R. (2009). Encyclopedia of inland waters.
   Google Scholar
• Manwell, J. F., McGowan, J. G., & Rogers, A. L. (2010). Wind energy explained: Theory, design and application. Seattle, USA: John Wiley & Sons.
   Google Scholar
• Mariotti, L., Diallo, I., Coppola, E., & Giorgi, F. (2014). Seasonal and intraseasonal changes of African monsoon climates in 21st century CORDEX projections. Climatic Change, 125(1), 53–65. doi: 10.1007/s10584-014-1097-0
   Web of Science ®Google Scholar
• Meinshausen, M., Smith, S. J., Calvin, K., Daniel, J. S., Kainuma, M., Lamarque, J.-F., … Riahi, K. (2011). The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Climatic Change, 109(1–2), 213–241. doi: 10.1007/s10584-011-0156-z
   Web of Science ®Google Scholar
• Moazenzadeh, R., Mohammadi, B., Shamshirband, S., & Chau, K.-w. (2018). Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran. Engineering Applications of Computational Fluid Mechanics, 12(1), 584–597. doi: 10.1080/19942060.2018.1482476
   Web of Science ®Google Scholar
• Panapakidis, I. P., Michailides, C., & Angelides, D. C. (2019). A data-driven short-term forecasting model for offshore wind speed prediction based on computational intelligence. Electronics, 8(4), 420. doi: 10.3390/electronics8040420
   Web of Science ®Google Scholar
• Petković, D., Ćojbašić, Ž, Nikolić, V., Shamshirband, S., Kiah, M. L. M., Anuar, N. B., & Wahab, A. W. A. (2014). Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission. Energy, 64, 868–874. doi: 10.1016/j.energy.2013.10.094
   Web of Science ®Google Scholar
• Porté-Agel, F., Wu, Y.-T., & Chen, C.-H. (2013). A numerical study of the effects of wind direction on turbine wakes and power losses in a large wind farm. Energies, 6(10), 5297–5313. doi: 10.3390/en6105297
   Web of Science ®Google Scholar
• Powell, M. D., Vickery, P. J., & Reinhold, T. A. (2003). Reduced drag coefficient for high wind speeds in tropical cyclones. Nature, 422(6929), 279–283. doi: 10.1038/nature01481
   PubMed Web of Science ®Google Scholar
• Pryor, S. C., & Barthelmie, R. (2010). Climate change impacts on wind energy: A review. Renewable and Sustainable Energy Reviews, 14(1), 430–437. doi: 10.1016/j.rser.2009.07.028
   Web of Science ®Google Scholar
• Qasem, S. N., Samadianfard, S., Kheshtgar, S., Jarhan, S., Kisi, O., Shamshirband, S., & Chau, K.-W. (2019). Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates. Engineering Applications of Computational Fluid Mechanics, 13(1), 177–187. doi: 10.1080/19942060.2018.1564702
   Web of Science ®Google Scholar
• Reyers, M., Pinto, J. G., & Moemken, J. (2015). Statistical–dynamical downscaling for wind energy potentials: Evaluation and applications to decadal hindcasts and climate change projections. International Journal of Climatology, 35(2), 229–244. doi: 10.1002/joc.3975
   Web of Science ®Google Scholar
• Rusu, E., & Onea, F. (2013). Evaluation of the wind and wave energy along the Caspian Sea. Energy, 50, 1–14. doi: 10.1016/j.energy.2012.11.044
   Web of Science ®Google Scholar
• Sailor, D., Hu, T., Li, X., & Rosen, J. (2000). A neural network approach to local downscaling of GCM output for assessing wind power implications of climate change. Renewable Energy, 19(3), 359–378. doi: 10.1016/S0960-1481(99)00056-7
   Web of Science ®Google Scholar
• Salih, S. Q., Allawi, M. F., Yousif, A. A., Armanuos, A. M., Saggi, M. K., Ali, M., … Chau, K.-W. (2019). Viability of the advanced adaptive neuro-fuzzy inference system model on reservoir evaporation process simulation: Case study of Nasser Lake in Egypt. Engineering Applications of Computational Fluid Mechanics, 13(1), 878–891. doi: 10.1080/19942060.2019.1647879
   Web of Science ®Google Scholar
• Shamshirband, S., Keivani, A., Mohammadi, K., Lee, M., Hamid, S. H. A., & Petkovic, D. (2016). Assessing the proficiency of adaptive neuro-fuzzy system to estimate wind power density: Case study of Aligoodarz, Iran. Renewable and Sustainable Energy Reviews, 59, 429–435. doi: 10.1016/j.rser.2015.12.269
   Web of Science ®Google Scholar
• Shin, J.-Y., Jeong, C., & Heo, J.-H. (2018). A novel statistical method to temporally downscale wind speed Weibull distribution using scaling property. Energies, 11(3), 633. doi: 10.3390/en11030633
   Web of Science ®Google Scholar
• Solaun, K., & Cerdá, E. (2020). Impacts of climate change on wind energy power – Four wind farms in Spain. Renewable Energy, 145, 1306–1316. doi: 10.1016/j.renene.2019.06.129
   Web of Science ®Google Scholar
• Stull, R. B. (2012). An introduction to boundary layer meteorology. Oslo, Norway: Springer Science & Business Media.
   Google Scholar
• Tobin, I., Jerez, S., Vautard, R., Thais, F., Van Meijgaard, E., Prein, A., … Nikulin, G. (2016). Climate change impacts on the power generation potential of a European mid-century wind farms scenario. Environmental Research Letters, 11(3), 034013. doi: 10.1088/1748-9326/11/3/034013
   Web of Science ®Google Scholar
• Viviescas, C., Lima, L., Diuana, F. A., Vasquez, E., Ludovique, C., Silva, G. N., … Lucena, A. F. (2019). Contribution of variable renewable energy to increase energy security in Latin America: Complementarity and climate change impacts on wind and solar resources. Renewable and Sustainable Energy Reviews, 113, 109232. doi: 10.1016/j.rser.2019.06.039
   Web of Science ®Google Scholar
• Winstral, A., Jonas, T., & Helbig, N. (2017). Statistical downscaling of gridded wind speed data using local topography. Journal of Hydrometeorology, 18(2), 335–348. doi: 10.1175/JHM-D-16-0054.1
   Web of Science ®Google Scholar
• Yang, H.-F., & Chen, Y.-P. P. (2019). Representation learning with extreme learning machines and empirical mode decomposition for wind speed forecasting methods. Artificial Intelligence, 277, 103176. doi: 10.1016/j.artint.2019.103176
   Web of Science ®Google Scholar
• Yaseen, Z. M., Sulaiman, S. O., Deo, R. C., & Chau, K.-W. (2019). An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction. Journal of Hydrology, 569, 387–408. doi: 10.1016/j.jhydrol.2018.11.069
   Web of Science ®Google Scholar
• Yu, B., Chowdhury, A. G., & Masters, F. J. (2008). Hurricane wind power spectra, cospectra, and integral length scales. Boundary-Layer Meteorology, 129(3), 411–430. doi: 10.1007/s10546-008-9316-8
   Web of Science ®Google Scholar