[1] Savino JM. Wind power. vol. 22. 1975. doi:10.1049/ep.1976.0231.
[2] Kaldellis JK, Zafirakis D. The wind energy (r)evolution: A short review of a long history. Renew Energy 2011;36:1887–901. doi:10.1016/j.renene.2011.01.002.
[3] Petersen EL. In search of the wind energy potential. J Renew Sustain Energy 2017;9. doi:10.1063/1.4999514.
[4] Bradford T. Solar revolution: the economic transformation of the global energy industry. vol. 32. 2006. doi:10.1016/j.energy.2007.03.002.
[5] Keivanpour S, Ramudhin A, Ait Kadi D. The sustainable worldwide offshore wind energy potential: A systematic review. J Renew Sustain Energy 2017;9. doi:10.1063/1.5009948.
[6] Sahin AD. Progress and recent trends in wind energy. Prog Energy Combust Sci 2004;30:501–43. doi:10.1016/j.pecs.2004.04.001.
[7] REN21. Renewables 2017: Global status report. Paris: 2017. doi:10.1016/j.rser.2016.09.082.
[8] Fadare DA. The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria. Appl Energy 2010;87:934–42. doi:10.1016/j.apenergy.2009.09.005.
[9] Barati H, Haroonabadi H, Zadehali R. Wind speed forecasting in South Coasts of Iran : An Application of Artificial Neural Networks (ANNs) for Electricity Generation using Renewable Energy. Bull Environ Pharmacol Life Sci 2013;2:30–7.
[10] Brahimi T. Using artificial intelligence to predict wind speed for energy application in Saudi Arabia. Energies 2019;12. doi:10.3390/en12244669.
[11] Liu H, Tian HQ, Pan DF, Li YF. Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks. Appl Energy 2013;107:191–208. doi:10.1016/j.apenergy.2013.02.002.
[12] Arjun NN, Prema V, Kumar DK, Prashanth P, Preekshit VS, Rao KU. Multivariate regression models for prediction of wind speed. Proc. - 2014 Int. Conf. Data Sci. Eng. ICDSE 2014, 2014, p. 171–6. doi:10.1109/ICDSE.2014.6974632.
[13] Barhmi S, Elfatni O, Belhaj I. Forecasting of wind speed using multiple linear regression and artificial neural networks. Energy Syst 2019. doi:10.1007/s12667-019-00338-y.
[14] Li G, Shi J. On comparing three artificial neural networks for wind speed forecasting. Appl Energy 2010;87:2313–20. doi:10.1016/j.apenergy.2009.12.013.
[15] Chen X, Zhao J, Hu W, Yang Y. Short-term wind speed forecasting using decomposition-based neural networks combining abnormal detection method. Abstr Appl Anal 2014;2014. doi:10.1155/2014/984268.
[16] Santhosh M, Venkaiah C, Vinod Kumar DM. Ensemble empirical mode decomposition based adaptive wavelet neural network method for wind speed prediction. Energy Convers Manag 2018;168:482–93. doi:10.1016/j.enconman.2018.04.099.
[17] Liu H, Tian HQ, Liang XF, Li YF. Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks. Appl Energy 2015;157:183–94. doi:10.1016/j.apenergy.2015.08.014.
[18] Liu H, Tian HQ, Li YF. Four wind speed multi-step forecasting models using extreme learning machines and signal decomposing algorithms. Energy Convers Manag 2015;100:16–22. doi:10.1016/j.enconman.2015.04.057.
[19] Sun N, Zhou J, Liu G, He Z. A hybrid wind speed forecasting model based on a decomposition method and an improved regularized extreme learning machine. Energy Procedia 2019;158:217–22. doi:10.1016/j.egypro.2019.01.079.
[20] Liu Y, Zhang S, Chen X, Wang J. Artificial combined model based on hybrid nonlinear neural network models and statistics linear models-research and application for wind speed forecasting. Sustain 2018;10. doi:10.3390/su10124601.
[21] Cai H, Jia X, Feng J, Yang Q, Hsu YM, Chen Y, et al. A combined filtering strategy for short term and long term wind speed prediction with improved accuracy. Renew Energy 2019;136:1082–90. doi:10.1016/j.renene.2018.09.080.
[22] Gendeel M, Yuxian Z, Aoqi H. Performance comparison of ANNs model with VMD for short-term wind speed forecasting. IET Renew Power Gener 2018;12:1424–30. doi:10.1049/iet-rpg.2018.5203.
[23] Guo Z, Zhao W, Lu H, Wang J. Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model. Renew Energy 2012;37:241–9. doi:10.1016/j.renene.2011.06.023.
[24] Liu H, Chen C, Tian HQ, Li YF. A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks. Renew Energy 2012;48:545–56. doi:10.1016/j.renene.2012.06.012.
[25] Peng T, Zhou J, Zhang C, Zheng Y. Multi-step ahead wind speed forecasting using a hybrid model based on two-stage decomposition technique and AdaBoost-extreme learning machine. Energy Convers Manag 2017;153:589–602. doi:10.1016/j.enconman.2017.10.021.
[26] Wang L, Li X, Bai Y. Short-term wind speed prediction using an extreme learning machine model with error correction. Energy Convers Manag 2018;162:239–50. doi:10.1016/j.enconman.2018.02.015.
[27] Liu H, Mi X, Li Y. Comparison of two new intelligent wind speed forecasting approaches based on Wavelet Packet Decomposition, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Artificial Neural Networks. Energy Convers Manag 2018;155:188–200. doi:10.1016/j.enconman.2017.10.085.
[28] Qu Z, Mao W, Zhang K, Zhang W, Li Z. Multi-step wind speed forecasting based on a hybrid decomposition technique and an improved back-propagation neural network. Renew Energy 2019;133:919–29. doi:10.1016/j.renene.2018.10.043.
[29] Blanchard T, Samanta B. Wind speed forecasting using neural networks. Wind Eng 2020;44:33–48. doi:10.1177/0309524X19849846.
[30] Yong B, Qiao F, Wang C, Shen J, Wei Y, Zhou Q. Ensemble Neural Network Method for Wind Speed Forecasting. IEEE Work Signal Process Syst SiPS Des Implement 2019;2019-Octob:31–6. doi:10.1109/SiPS47522.2019.9020410.
[31] Chen L, Li Z, Zhang Y. Multiperiod-Ahead Wind Speed Forecasting Using Deep Neural Architecture and Ensemble Learning. Math Probl Eng 2019;2019. doi:10.1155/2019/9240317.
[32] Liu H, Mi X, Li Y. An experimental investigation of three new hybrid wind speed forecasting models using multi-decomposing strategy and ELM algorithm. Renew Energy 2018;123:694–705. doi:10.1016/j.renene.2018.02.092.
[33] Pradhan PP, Subudhi B. Wind speed forecasting based on wavelet transformation and recurrent neural network. Int J Numer Model Electron Networks, Devices Fields 2020;33:1–11. doi:10.1002/jnm.2670.
[34] Zhang Y, Yang S, Guo Z, Guo Y, Zhao J. Wind speed forecasting based on wavelet decomposition and wavelet neural networks optimized by the Cuckoo search algorithm. Atmos Ocean Sci Lett 2019;12:107–15. doi:10.1080/16742834.2019.1569455.
[35] Chen Y, He Z, Shang Z, Li C, Li L, Xu M. A novel combined model based on echo state network for multi-step ahead wind speed forecasting: A case study of NREL. Energy Convers Manag 2019;179:13–29. doi:10.1016/j.enconman.2018.10.068.
[36] Nie Y, Bo H, Zhang W, Zhang H. Research on Hybrid Wind Speed Prediction System Based on Artificial Intelligence and Double Prediction Scheme. Complexity 2020;2020. doi:10.1155/2020/9601763.
[37] Foley AM, Leahy PG, Marvuglia A, McKeogh EJ. Current methods and advances in forecasting of wind power generation. Renew Energy 2012;37:1–8. doi:10.1016/j.renene.2011.05.033.
[38] Chang W-Y. A Literature Review of Wind Forecasting Methods. J Power Energy Eng 2014;02:161–8. doi:10.4236/jpee.2014.24023.
[39] Ata R. Artificial neural networks applications in wind energy systems: a review. Renew Sustain Energy Rev 2015;49:534–62. doi:10.1016/j.rser.2015.04.166.
[40] Wang Y, Yu Y, Cao S, Zhang X, Gao S. A review of applications of artificial intelligent algorithms in wind farms. Artif Intell Rev 2020;53:3447–500. doi:10.1007/s10462-019-09768-7.
[41] de Jong R, Sakarya N. The Econometrics of the Hodrick-Prescott filter. Rev Econ Stat 2015;92. doi:https://doi.org/10.1162/REST_a_00523.
[42] Hodrick RJ, Prescott EC. Postwar U.S. Business Cycles: An Empirical Investigation. J Money, Credit Bank 1997;29:1. doi:10.2307/2953682.
[43] Zhang GP, Qi M. Neural network forecasting for seasonal and trend time series. Eur J Oper Res 2005;160:501–14. doi:10.1016/j.ejor.2003.08.037.
[44] Crone SF, Dhawan R. Forecasting seasonal time series with neural networks: A sensitivity analysis of architecture parameters. IEEE Int. Conf. Neural Networks - Conf. Proc., Orlando, Florida, USA: 2007, p. 2099–104. doi:10.1109/IJCNN.2007.4371282.
[45] Claveria O, Monte E, Torra S. Effects of removing the trend and the seasonal component on the forecasting performance of artificial neural network techniques. 2015.
[46] Haykin S. Neural networks-A comprehensive foundation. New York IEEE Press Herrmann, M, Bauer, H-U, Der, R 1994;psychology:pp107-116. doi:10.1017/S0269888998214044.
[47] Vapnik V. The Nature of Statistical Learning Theory. Second. New York: Springer; 2000. doi:10.1007/978-1-4757-2440-0.
[48] Rogers TJ, Gardner P, Dervilis N, Worden K, Maguire AE, Papatheou E, et al. Probabilistic modelling of wind turbine power curves with application of heteroscedastic Gaussian Process regression. Renew Energy 2020;148:1124–36. doi:10.1016/j.renene.2019.09.145.