Skip to main content

Advertisement

SpringerLink
Log in

A new insight to the wind speed forecasting: robust multi-stage ensemble soft computing approach based on pre-processing uncertainty assessment

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In this research, monthly wind speed time series of the Kirsehir was investigated using the stand-alone, hybrid and ensemble models. The artificial neural networks, Gaussian process regression, support vector machines and multivariate adaptive regression splines were employed as stand-alone machine learning models, while the discrete wavelet transform was utilized as a pre-processing technique to create hybrid models. Moreover, for the first time in wind speed predictions, we generated a multi-stage ensemble model by using the M5 Model Tree (M5) algorithm to increase the model accuracies. Two major tasks considered to be necessary, in which the first is to obtain the lag times by using autocorrelation functions, and the latter is to determine the optimum mother wavelet as well as the decomposition level to reduce the uncertainties in wavelet modeling. The results revealed that the hybrid wavelet models outperformed the stand-alone models, while a significant improvement was also observed in M5 ensemble models as the highest Nash–Sutcliffe efficiency coefficient values were obtained in M5 hybrid wavelet multi-stage ensemble models for each lead time prediction. The findings of the study were assessed with respect to the various performance indicators and Kruskal–Wallis test to indicate whether the results are statically significant. The proposed multi-stage ensemble framework also benchmarked with the classical tree-based ensembles, such as Random forest, AdaBoost and XGBoost.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Abbreviations

ACF:

Autocorrelation Function

AI:

Artificial Intelligence

ANFIS:

Adaptive Neuro-Fuzzy Inference Systems

ANN:

Artificial Neural Networks

BFGS:

Broyden-Fletcher-Goldfarb-Shanno

BPNN:

Back-Propagation Neural Networks

CCO:

Crisscross Optimization

DBN:

Deep Belief Network

DWT:

Discrete Wavelet Transform

EEMD:

Ensemble Empirical Mode Decomposition

ELM:

Extreme Learning Machines

EMD:

Empirical Mode Decomposition

FEEMD:

Fast Ensemble Empirical Mode Decomposition

FFBP:

Feed-Forward Back-Propagation

GCV:

Generalized Cross-Validation

GPD:

Gaussian Probability Distribution

GPR:

Gaussian Process Regression

LSSVM:

Least Square Support Vector Machine

M5:

M5 Model Tree

M5-ST:

M5 Model Tree Stand-Alone Ensemble Models

M5-W:

M5 Model Tree Hybrid Wavelet Ensemble Models

MAE:

Mean Absolute Error

MARS:

Multivariate Adaptive Regression Splines

MGM:

Turkish State Meteorological Service

ML:

Machine Learning

MLP:

Multilayer Perceptron

MOCS:

Multi-Objective Cuckoo Search

NSE:

Nash–Sutcliffe Efficiency Coefficient

PACF:

Partial Autocorrelation Function

PI:

Performance Index

RBF:

Radial Basis Function

RELM:

Regularized Extreme Learning Machine

RMSE:

Root-Mean-Square Error

SA-ST:

Simple Average Stand-Alone Ensemble Models

SA-W:

Simple Average Hybrid Wavelet Ensemble Models

SDR:

Standard Deviation Reduction

SVM:

Support Vector Machines

W-ANN:

Hybrid Wavelet Artificial Neural Networks

WA-ST:

Weighted Average Stand-Alone Ensemble Models

WA-W:

Weighted Average Hybrid Wavelet Ensemble Models

W-GPR:

Hybrid Wavelet Gaussian Process Regression

WI:

Wilmott’s Refined Index

W-MARS:

Hybrid Wavelet Multivariate Adaptive Regression Splines

WPT:

Wavelet Packet Transform

W-SVM:

Hybrid Wavelet Support Vector Machines

References

  1. Tut Haklıdır FS (2020) The importance of long-term well management in geothermal power systems using fuzzy control: A Western Anatolia (Turkey) case study. Energy. https://doi.org/10.1016/j.energy.2020.118817

    Article  Google Scholar 

  2. Puah BK, Chong LW, Wong YW et al (2021) A regression unsupervised incremental learning algorithm for solar irradiance prediction. Renew Energy 164:908–925. https://doi.org/10.1016/j.renene.2020.09.080

    Article  Google Scholar 

  3. Negnevitsky M, Potter CW (2006) Innovative short-term wind generation prediction techniques. 2006 IEEE PES Power Syst Conf Expo PSCE 2006 - Proc 60–65. https://doi.org/10.1109/PSCE.2006.296250

  4. Li G, Shi J (2010) On comparing three artificial neural networks for wind speed forecasting. Appl Energy 87:2313–2320. https://doi.org/10.1016/j.apenergy.2009.12.013

    Article  Google Scholar 

  5. Faria DL, Castro R, Philippart C, Gusmao A (2009) Wavelets pre-filtering in wind speed prediction. In: 2009 International Conference on Power Engineering, Energy and Electrical Drives. IEEE, pp 168–173

  6. Kisi O, Shiri J, Makarynskyy O (2011) Wind speed prediction by using different wavelet conjunction models. Int J Ocean Clim Syst 2:189–208. https://doi.org/10.1260/1759-3131.2.3.189

    Article  Google Scholar 

  7. Liu H, Tian H, Chen C, Li Y (2013) An experimental investigation of two Wavelet-MLP hybrid frameworks for wind speed prediction using GA and PSO optimization. Int J Electr Power Energy Syst 52:161–173. https://doi.org/10.1016/j.ijepes.2013.03.034

    Article  Google Scholar 

  8. Duran MA, Filik UB (2015) Short-term wind speed prediction using several artificial neural network approaches in Eskisehir. In: 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA). IEEE, pp 1–4

  9. Jiang P, Li P (2017) Research and application of a new hybrid wind speed forecasting model on BSO algorithm. J Energy Eng 143:04016019. https://doi.org/10.1061/(ASCE)EY.1943-7897.0000362

    Article  Google Scholar 

  10. Mi X, Zhao S (2020) Wind speed prediction based on singular spectrum analysis and neural network structural learning. Energy Convers Manag 216:112956. https://doi.org/10.1016/j.enconman.2020.112956

    Article  Google Scholar 

  11. Yue Y, Zhao Y, Zhao H, Wang H (2017) Short-term wind speed combined prediction for wind farms. In: 2017 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, pp 18–22

  12. Liu M-D, Ding L, Bai Y-L (2021) Application of hybrid model based on empirical mode decomposition, novel recurrent neural networks and the ARIMA to wind speed prediction. Energy Convers Manag 233:113917. https://doi.org/10.1016/j.enconman.2021.113917

    Article  Google Scholar 

  13. Zhang Y, Zhao Y, Kong C, Chen B (2020) A new prediction method based on VMD-PRBF-ARMA-E model considering wind speed characteristic. Energy Convers Manag 203:112254. https://doi.org/10.1016/j.enconman.2019.112254

    Article  Google Scholar 

  14. Li F, Liao H (2018) An intelligent method for wind power forecasting based on integrated power slope events prediction and wind speed forecasting. IEEJ Trans Electr Electron Eng 13:1099–1105. https://doi.org/10.1002/tee.22671

    Article  Google Scholar 

  15. Li F, Ren G, Lee J (2019) Multi-step wind speed prediction based on turbulence intensity and hybrid deep neural networks. Energy Convers Manag 186:306–322. https://doi.org/10.1016/j.enconman.2019.02.045

    Article  Google Scholar 

  16. Ma T, Wang C, Wang J et al (2019) Particle-swarm optimization of ensemble neural networks with negative correlation learning for forecasting short-term wind speed of wind farms in western China. Inf Sci (Ny) 505:157–182. https://doi.org/10.1016/j.ins.2019.07.074

    Article  MathSciNet  Google Scholar 

  17. Jaseena KU, Kovoor BC (2020) A Wavelet-based hybrid multi-step Wind Speed Forecasting model using LSTM and SVR. Wind Eng. https://doi.org/10.1177/0309524X20964762

    Article  Google Scholar 

  18. Liu H, Wu H, Li Y (2020) Multi-step wind speed forecasting model based on wavelet matching analysis and hybrid optimization framework. Sustain Energy Technol Assess 40:100745. https://doi.org/10.1016/j.seta.2020.100745

    Article  Google Scholar 

  19. Chen X, Li Y, Zhang Y et al (2021) A novel hybrid model based on an improved seagull optimization algorithm for short-term wind speed forecasting. Processes 9:387. https://doi.org/10.3390/pr9020387

    Article  Google Scholar 

  20. Sun S, Fu J, Li A, Zhang P (2021) A new compound wind speed forecasting structure combining multi-kernel LSSVM with two-stage decomposition technique. Soft Comput 25:1479–1500. https://doi.org/10.1007/s00500-020-05233-8

    Article  Google Scholar 

  21. Liu D, Niu D, Wang H, Fan L (2014) Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm. Renew Energy 62:592–597. https://doi.org/10.1016/j.renene.2013.08.011

    Article  Google Scholar 

  22. Liu H, Tian HQ, Pan DF, Li YF (2013) Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks. Appl Energy 107:191–208. https://doi.org/10.1016/j.apenergy.2013.02.002

    Article  Google Scholar 

  23. Meng A, Ge J, Yin H, Chen S (2016) Wind speed forecasting based on wavelet packet decomposition and artificial neural networks trained by crisscross optimization algorithm. Energy Convers Manag 114:75–88. https://doi.org/10.1016/j.enconman.2016.02.013

    Article  Google Scholar 

  24. Wang JZ, Wang Y, Jiang P (2015) The study and application of a novel hybrid forecasting model - A case study of wind speed forecasting in China. Appl Energy 143:472–488. https://doi.org/10.1016/j.apenergy.2015.01.038

    Article  Google Scholar 

  25. Wang S, Zhang N, Wu L, Wang Y (2016) Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method. Renew Energy 94:629–636. https://doi.org/10.1016/j.renene.2016.03.103

    Article  Google Scholar 

  26. Liu H, Tian HQ, Liang XF, Li YF (2015) Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks. Appl Energy 157:183–194. https://doi.org/10.1016/j.apenergy.2015.08.014

    Article  Google Scholar 

  27. Sun W, Liu M (2016) Wind speed forecasting using FEEMD echo state networks with RELM in Hebei, China. Energy Convers Manag 114:197–208. https://doi.org/10.1016/j.enconman.2016.02.022

    Article  Google Scholar 

  28. Wang HZ, Wang GB, Li GQ et al (2016) Deep belief network based deterministic and probabilistic wind speed forecasting approach. Appl Energy 182:80–93. https://doi.org/10.1016/j.apenergy.2016.08.108

    Article  Google Scholar 

  29. Doucoure B, Agbossou K, Cardenas A (2016) Time series prediction using artificial wavelet neural network and multi-resolution analysis: application to wind speed data. Renew Energy 92:202–211. https://doi.org/10.1016/j.renene.2016.02.003

    Article  Google Scholar 

  30. Erdemir G, Zengin AT, Akinci TC (2020) Short-term wind speed forecasting system using deep learning for wind turbine applications. Int J Electr Comput Eng 10:5779–5784

    Google Scholar 

  31. Ma X, Jin Y, Dong Q (2017) A generalized dynamic fuzzy neural network based on singular spectrum analysis optimized by brain storm optimization for short-term wind speed forecasting. Appl Soft Comput J 54:296–312. https://doi.org/10.1016/j.asoc.2017.01.033

    Article  Google Scholar 

  32. Yu C, Li Y, Zhang M (2017) An improved wavelet transform using singular spectrum analysis for wind speed forecasting based on Elman neural network. Energy Convers Manag 148:895–904. https://doi.org/10.1016/j.enconman.2017.05.063

    Article  Google Scholar 

  33. Luo L, Li H, Wang J, Hu J (2021) Design of a combined wind speed forecasting system based on decomposition-ensemble and multi-objective optimization approach. Appl Math Model 89:49–72. https://doi.org/10.1016/j.apm.2020.07.019

    Article  MathSciNet  MATH  Google Scholar 

  34. Yang XS, Deb S (2009) Cuckoo search via Lévy flights. 2009 World Congr Nat Biol Inspired Comput NABIC 2009 - Proc 210–214. https://doi.org/10.1109/NABIC.2009.5393690

  35. Başakın EE, Ekmekcioğlu Ö (2021) Letter to the Editor “Estimation of global solar radiation data based on satellite-derived atmospheric parameters over the urban area of Mashhad, Iran.” Environ Sci Pollut Res 28:19530–19532. https://doi.org/10.1007/s11356-021-13201-4

    Article  Google Scholar 

  36. Ekmekcioğlu Ö, Başakın EE, Özger M (2020) Tree-based nonlinear ensemble technique to predict energy dissipation in stepped spillways. Eur J Environ Civ Eng. https://doi.org/10.1080/19648189.2020.1805024

    Article  Google Scholar 

  37. Khatibi R, Ghorbani MA, Pourhosseini FA (2017) Stream flow predictions using nature-inspired Firefly Algorithms and a Multiple Model strategy – Directions of innovation towards next generation practices. Adv Eng Informatics 34:80–89. https://doi.org/10.1016/j.aei.2017.10.002

    Article  Google Scholar 

  38. Khatibi R, Nadiri AA (2020) Inclusive Multiple Models (IMM) for predicting groundwater levels and treating heterogeneity. Geosci Front. https://doi.org/10.1016/j.gsf.2020.07.011

    Article  Google Scholar 

  39. Khatibi R, Ghorbani MA, Naghshara S et al (2020) A framework for ‘Inclusive Multiple Modelling’ with critical views on modelling practices – Applications to modelling water levels of Caspian Sea and Lakes Urmia and Van. J Hydrol 587:124923. https://doi.org/10.1016/j.jhydrol.2020.124923

    Article  Google Scholar 

  40. Nourani V, Elkiran G, Abba SI (2018) Wastewater treatment plant performance analysis using artificial intelligence – an ensemble approach. Water Sci Technol 78:2064–2076. https://doi.org/10.2166/wst.2018.477

    Article  Google Scholar 

  41. Elkiran G, Nourani V, Abba SI (2019) Multi-step ahead modelling of river water quality parameters using ensemble artificial intelligence-based approach. J Hydrol 577:123962. https://doi.org/10.1016/j.jhydrol.2019.123962

    Article  Google Scholar 

  42. Nourani V, Gökçekuş H, Umar IK (2020) Artificial intelligence based ensemble model for prediction of vehicular traffic noise. Environ Res 180:108852. https://doi.org/10.1016/j.envres.2019.108852

    Article  Google Scholar 

  43. Nadiri AA, Razzagh S, Khatibi R, Sedghi Z (2021) Predictive groundwater levels modelling by Inclusive Multiple Modelling (IMM) at multiple levels. Earth Sci Informatics. https://doi.org/10.1007/s12145-021-00572-y

    Article  Google Scholar 

  44. MGM (2020) Statistics. https://www.mgm.gov.tr

  45. Genç MS, Çelik M, Karasu I (2012) A review on wind energy and wind-hydrogen production in Turkey: a case study of hydrogen production via electrolysis system supplied by wind energy conversion system in Central Anatolian Turkey. Renew Sustain Energy Rev 16:6631–6646. https://doi.org/10.1016/j.rser.2012.08.011

    Article  Google Scholar 

  46. Özger M, Başakın EE, Ekmekcioğlu Ö, Hacısüleyman V (2020) Comparison of wavelet and empirical mode decomposition hybrid models in drought prediction. Comput Electron Agric 179:105851. https://doi.org/10.1016/j.compag.2020.105851

    Article  Google Scholar 

  47. Panchal G, Ganatra A, Kosta YP, Panchal D (2011) Behaviour analysis of multilayer perceptrons with multiple hidden neurons and hidden layers. Int J Comput Theory Eng 3:332–337. https://doi.org/10.7763/ijcte.2011.v3.328

    Article  Google Scholar 

  48. Altunkaynak A (2007) Forecasting surface water level fluctuations of lake van by artificial neural networks. Water Resour Manag 21:399–408. https://doi.org/10.1007/s11269-006-9022-6

    Article  Google Scholar 

  49. Bueno S, Salmeron JL (2009) Benchmarking main activation functions in fuzzy cognitive maps. Expert Syst Appl 36:5221–5229. https://doi.org/10.1016/j.eswa.2008.06.072

    Article  Google Scholar 

  50. Roushangar K, Alizadeh F (2018) Investigating effect of socio-economic and climatic variables in urban water consumption prediction via Gaussian process regression approach. Water Sci Technol Water Supply 18:84–93. https://doi.org/10.2166/ws.2017.100

    Article  Google Scholar 

  51. Shabani S, Samadianfard S, Sattari MT et al (2020) Modeling pan evaporation using Gaussian process regression K-nearest neighbors random forest and support vector machines; comparative analysis. Atmosphere (Basel). https://doi.org/10.3390/ATMOS11010066

    Article  Google Scholar 

  52. Fang D, Zhang X, Yu Q et al (2018) A novel method for carbon dioxide emission forecasting based on improved Gaussian processes regression. J Clean Prod 173:143–150. https://doi.org/10.1016/j.jclepro.2017.05.102

    Article  Google Scholar 

  53. Akbari M, Salmasi F, Arvanaghi H et al (2019) Application of Gaussian process regression model to predict discharge coefficient of gated piano key weir. Water Resour Manag 33:3929–3947. https://doi.org/10.1007/s11269-019-02343-3

    Article  Google Scholar 

  54. Li X, Yuan C, Li X, Wang Z (2020) State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression. Energy 190:116467. https://doi.org/10.1016/j.energy.2019.116467

    Article  Google Scholar 

  55. Rasmussen CE (2003) Gaussian processes in machine learning. In: Advanced lectures on machine learning. Springer

  56. Cortes C, Vapnik V (1995) Support vector networks. Mach Learn 20:293–297. https://doi.org/10.1109/64.163674

    Article  MATH  Google Scholar 

  57. Başakın EE, Ekmekcioğlu Ö, Ozger M (2019) Drought analysis with machine learning methods. Pamukkale Univ J Eng Sci 25:985–991. https://doi.org/10.5505/pajes.2019.34392

    Article  Google Scholar 

  58. Friedman JH (1991) Multivariate adaptive regression splines. Ann Stat 19:1–67

    MathSciNet  MATH  Google Scholar 

  59. Keshtegar B, Heddam S, Kisi O, Zhu SP (2019) Modeling total dissolved gas (TDG) concentration at Columbia river basin dams: high-order response surface method (H-RSM) vs. M5Tree, LSSVM, and MARS. Arab J Geosci. https://doi.org/10.1007/s12517-019-4687-3

    Article  Google Scholar 

  60. Adnan RM, Liang Z, Heddam S et al (2020) Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs. J Hydrol 586:124371. https://doi.org/10.1016/j.jhydrol.2019.124371

    Article  Google Scholar 

  61. Mirabbasi R, Kisi O, Sanikhani H, Gajbhiye Meshram S (2019) Monthly long-term rainfall estimation in Central India using M5Tree, MARS, LSSVR, ANN and GEP models. Neural Comput Appl 31:6843–6862. https://doi.org/10.1007/s00521-018-3519-9

    Article  Google Scholar 

  62. Craven P, Wahba G (1978) Smoothing noisy data with spline functions - Estimating the correct degree of smoothing by the method of generalized cross-validation. Numer Math 31:377–403. https://doi.org/10.1007/BF01404567

    Article  MathSciNet  MATH  Google Scholar 

  63. Khosravinia P, Nikpour MR, Kisi O, Yaseen ZM (2020) Application of novel data mining algorithms in prediction of discharge and end depth in trapezoidal sections. Comput Electron Agric 170:105283. https://doi.org/10.1016/j.compag.2020.105283

    Article  Google Scholar 

  64. Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324

    Article  MATH  Google Scholar 

  65. Wang N, Liu Y, Wang J et al (2019) Investigating the potential of using POI and nighttime light data to map urban road safety at the micro-level: a case in Shanghai. China Sustainability 11:4739. https://doi.org/10.3390/su11174739

    Article  Google Scholar 

  66. Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55:119–139. https://doi.org/10.1006/jcss.1997.1504

    Article  MathSciNet  MATH  Google Scholar 

  67. Chen T, Guestrin C (2016) XGBoost. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, USA, pp 785–794

  68. Nourani V, Elkiran G, Abdullahi J (2019) Multi-station artificial intelligence based ensemble modeling of reference evapotranspiration using pan evaporation measurements. J Hydrol 577:123958. https://doi.org/10.1016/j.jhydrol.2019.123958

    Article  Google Scholar 

  69. Quinlan JR (1992) Learning with continuous classes: constructing model trees. Proc 5th Aust Jt Conf Artif Intell World Sci Singapore 343–348

  70. Rezaie-balf M, Naganna SR, Ghaemi A, Deka PC (2017) Wavelet coupled MARS and M5 Model Tree approaches for groundwater level forecasting. J Hydrol 553:356–373. https://doi.org/10.1016/j.jhydrol.2017.08.006

    Article  Google Scholar 

  71. Başakın EE, Ekmekcioğlu Ö, Mohammadi B (2020) Letter to the editor “comparing artificial intelligence techniques for chlorophyll-a prediction in US lakes.” Environ Sci Pollut Res 27:22131–22134. https://doi.org/10.1007/s11356-020-08666-8

    Article  Google Scholar 

  72. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models. Part 1: a discussion of principles. J Hydrol 10:282–290

    Article  Google Scholar 

  73. Willmott CJ, Robeson SM, Matsuura K (2012) A refined index of model performance. Int J Climatol 32:2088–2094. https://doi.org/10.1002/joc.2419

    Article  Google Scholar 

  74. Gandomi AH, Roke DA (2015) Assessment of artificial neural network and genetic programming as predictive tools. Adv Eng Softw 88:63–72. https://doi.org/10.1016/j.advengsoft.2015.05.007

    Article  Google Scholar 

  75. Aghelpour P, Mohammadi B, Biazar SM (2019) Long-term monthly average temperature forecasting in some climate types of Iran, using the models SARIMA, SVR, and SVR-FA. Theor Appl Climatol 138:1471–1480. https://doi.org/10.1007/s00704-019-02905-w

    Article  Google Scholar 

  76. Yaseen ZM, Jaafar O, Deo RC et al (2016) Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq. J Hydrol 542:603–614. https://doi.org/10.1016/j.jhydrol.2016.09.035

    Article  Google Scholar 

  77. Luo X, Yuan X, Zhu S et al (2019) A hybrid support vector regression framework for streamflow forecast. J Hydrol 568:184–193. https://doi.org/10.1016/j.jhydrol.2018.10.064

    Article  Google Scholar 

  78. Moriasi DN, Arnold JG, Van Liew MW et al (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50:885–900

    Article  Google Scholar 

  79. Kang S, Lin H (2007) Wavelet analysis of hydrological and water quality signals in an agricultural watershed. J Hydrol 338:1–14. https://doi.org/10.1016/j.jhydrol.2007.01.047

    Article  Google Scholar 

  80. Shoaib M, Shamseldin AY, Melville BW (2014) Comparative study of different wavelet based neural network models for rainfall-runoff modeling. J Hydrol 515:47–58. https://doi.org/10.1016/j.jhydrol.2014.04.055

    Article  Google Scholar 

  81. Khosravi A, Machado L, Nunes RO (2018) Time-series prediction of wind speed using machine learning algorithms: a case study Osorio wind farm, Brazil. Appl Energy 224:550–566. https://doi.org/10.1016/j.apenergy.2018.05.043

    Article  Google Scholar 

  82. Ferreira M, Santos A, Lucio P (2019) Short-term forecast of wind speed through mathematical models. Energy Rep 5:1172–1184. https://doi.org/10.1016/j.egyr.2019.05.007

    Article  Google Scholar 

  83. Cai H, Jia X, Feng J et al (2020) Gaussian process regression for numerical wind speed prediction enhancement. Renew Energy 146:2112–2123. https://doi.org/10.1016/j.renene.2019.08.018

    Article  Google Scholar 

  84. Hu J, Wang J, Xiao L (2017) A hybrid approach based on the Gaussian process with t-observation model for short-term wind speed forecasts. Renew Energy 114:670–685. https://doi.org/10.1016/j.renene.2017.05.093

    Article  Google Scholar 

  85. Hu J, Wang J (2015) Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression. Energy 93:1456–1466. https://doi.org/10.1016/j.energy.2015.10.041

    Article  Google Scholar 

  86. Santamaría-Bonfil G, Reyes-Ballesteros A, Gershenson C (2016) Wind speed forecasting for wind farms: a method based on support vector regression. Renew Energy 85:790–809. https://doi.org/10.1016/j.renene.2015.07.004

    Article  Google Scholar 

  87. Tian Z (2021) Modes decomposition forecasting approach for ultra-short-term wind speed. Appl Soft Comput 105:107303. https://doi.org/10.1016/j.asoc.2021.107303

    Article  Google Scholar 

  88. Caraka RE, Chen RC, Bakar SA et al (2020) Employing best input SVR robust lost function with nature-inspired metaheuristics in wind speed energy forecasting. IAENG Int J Comput Sci 47:572–584

    Google Scholar 

  89. Sakar CO, Serbes G, Gunduz A et al (2019) A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform. Appl Soft Comput 74:255–263. https://doi.org/10.1016/j.asoc.2018.10.022

    Article  Google Scholar 

  90. Taheri-Garavand A, Ahmadi H, Omid M et al (2015) An intelligent approach for cooling radiator fault diagnosis based on infrared thermal image processing technique. Appl Therm Eng 87:434–443. https://doi.org/10.1016/j.applthermaleng.2015.05.038

    Article  Google Scholar 

  91. Altunkaynak A, Ozger M (2016) Comparison of discrete and continuous wavelet-multilayer perceptron methods for daily precipitation prediction. J Hydrol Eng 21:1–11. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001376

    Article  Google Scholar 

  92. Altunkaynak A, Kartal E (2019) Performance comparison of continuous Wavelet-Fuzzy and discrete Wavelet-Fuzzy models for water level predictions at northern and southern boundary of Bosphorus. Ocean Eng 186:106097. https://doi.org/10.1016/j.oceaneng.2019.06.002

    Article  Google Scholar 

  93. Khelil K, Berrezzek F, Bouadjila T (2020) GA-based design of optimal discrete wavelet filters for efficient wind speed forecasting. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05251-5

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Hydraulics Division, Civil Engineering Department, Istanbul Technical Univ., 34469, Maslak, Istanbul, Turkey

    Eyyup Ensar Başakın, Ömer Ekmekcioğlu & Mehmet Özger

  2. Department of Civil Engineering, Erciyes University, Kayseri, Turkey

    Hatice Çıtakoğlu

Authors
  1. Eyyup Ensar Başakın
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ömer Ekmekcioğlu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hatice Çıtakoğlu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mehmet Özger
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ömer Ekmekcioğlu.

Ethics declarations

Conflict of interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Başakın, E.E., Ekmekcioğlu, Ö., Çıtakoğlu, H. et al. A new insight to the wind speed forecasting: robust multi-stage ensemble soft computing approach based on pre-processing uncertainty assessment. Neural Comput & Applic 34, 783–812 (2022). https://doi.org/10.1007/s00521-021-06424-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06424-6

Keywords

Search

Navigation