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Akhter, M. N., Mekhilef, S., Mokhlis, H., & Shah, N. M. Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques. IET Renewable Power Generation., 2019. 13(7): 1009–1023AkhterM. N.MekhilefS.MokhlisH.ShahN. M.Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques201913710091023Search in Google Scholar
Zhen, Z., Liu, J., Zhang, Z., Wang, F., Chai, H., Yu, Y.,... & Lin, Y. Deep learning based surface irradiance mapping model for solar PV power forecasting using sky image. IEEE Transactions on Industry Applications., 2020. 56(4): 3385–3396ZhenZ.LiuJ.ZhangZ.WangF.ChaiH.YuY.LinY.Deep learning based surface irradiance mapping model for solar PV power forecasting using sky image202056433853396Search in Google Scholar
Jewell, J., Vinichenko, V., Nacke, L., & Cherp, A. Prospects for powering past coal. Nature Climate Change., 2019. 9(8): 592–597JewellJ.VinichenkoV.NackeL.CherpA.Prospects for powering past coal201998592597Search in Google Scholar
Touchent, K., Hammouch, Z. & Mekkaoui, T. A modified invariant subspace method for solving partial differential equations with non-singular kernel fractional derivatives. Applied Mathematics and Nonlinear Sciences., 2020. 5(2): 35–48TouchentK.HammouchZ.MekkaouiT.A modified invariant subspace method for solving partial differential equations with non-singular kernel fractional derivatives2020523548Search in Google Scholar
Aghili, A. Complete Solution For The Time Fractional Diffusion Problem With Mixed Boundary Conditions by Operational Method. Applied Mathematics and Nonlinear Sciences., 2020. 6(1): 9–20AghiliA.Complete Solution For The Time Fractional Diffusion Problem With Mixed Boundary Conditions by Operational Method202061920Search in Google Scholar
Giorgi, F., Raffaele, F., & Coppola, E. The response of precipitation characteristics to global warming from climate projections. Earth System Dynamics., 2019. 10(1): 73–89GiorgiF.RaffaeleF.CoppolaE.The response of precipitation characteristics to global warming from climate projections20191017389Search in Google Scholar
Abdel-Nasser, M., & Mahmoud, K. Accurate photovoltaic power forecasting models using deep LSTM-RNN. Neural Computing and Applications., 2019. 31(7): 2727–2740Abdel-NasserM.MahmoudK.Accurate photovoltaic power forecasting models using deep LSTM-RNN201931727272740Search in Google Scholar
Mehr, A. D., Nourani, V., Khosrowshahi, V. K., & Ghorbani, M. A. A hybrid support vector regression–firefly model for monthly rainfall forecasting. International Journal of Environmental Science and Technology., 2019. 16(1): 335–346MehrA. D.NouraniV.KhosrowshahiV. K.GhorbaniM. A.A hybrid support vector regression–firefly model for monthly rainfall forecasting2019161335346Search in Google Scholar
Mehr, A. D., Nourani, V., Khosrowshahi, V. K., & Ghorbani, M. A. A hybrid support vector regression–firefly model for monthly rainfall forecasting. International Journal of Environmental Science and Technology., 2019. 16(1): 335–346MehrA. D.NouraniV.KhosrowshahiV. K.GhorbaniM. A.A hybrid support vector regression–firefly model for monthly rainfall forecasting2019161335346Search in Google Scholar
Zhang, Z., & Hong, W. C. Electric load forecasting by complete ensemble empirical mode decomposition adaptive noise and support vector regression with quantum-based dragonfly algorithm. Nonlinear Dynamics., 2019. 98(2): 1107–1136ZhangZ.HongW. C.Electric load forecasting by complete ensemble empirical mode decomposition adaptive noise and support vector regression with quantum-based dragonfly algorithm201998211071136Search in Google Scholar
Sanikhani, H., Kisi, O., Maroufpoor, E., & Yaseen, Z. M. Temperature-based modeling of reference evapotranspiration using several artificial intelligence models: application of different modeling scenarios. Theoretical and Applied Climatology., 2019. 135(1): 449–462SanikhaniH.KisiO.MaroufpoorE.YaseenZ. M.Temperature-based modeling of reference evapotranspiration using several artificial intelligence models: application of different modeling scenarios20191351449462Search in Google Scholar
Ramon, J., Lledo, L., Torralba, V., Soret, A., & Doblas-Reyes, F. J. What global reanalysis best represents near-surface winds?. Quarterly Journal of the Royal Meteorological Society., 2019. 145(724): 3236–3251RamonJ.LledoL.TorralbaV.SoretA.Doblas-ReyesF. J.What global reanalysis best represents near-surface winds?201914572432363251Search in Google Scholar