Hybrid wind speed prediction model based on recurrent long short-term memory neural network and support vector machine models

Abstract

Renewable energy has gained its significance in the recent years due to the increasing power demand and the requirement in various distribution and utilization sectors. To meet the energy demand, renewable energy resources which include wind and solar have attained significant attractiveness and remarkable expansions are carried out all over the world to enhance the power generation using wind and solar energy. This research paper focuses on predicting the wind speed so that it results in forecasting the possible wind power that can be generated from the wind resources which facilitates to meet the growing energy demand. In this work, a recurrent neural network model called as long short-term memory network model and variants of support vector machine models are used to predict the wind speed for the considered locations where the windmill has been installed. Both these models are tuned for the weight parameters and kernel variational parameters using the proposed hybrid particle swarm optimization algorithm and ant lion optimization algorithm. Experimental simulation results attained prove the validity of the proposed work compared with the methods developed in the early literature.

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References

  1. Ahmed A, Khalid M (2018) An intelligent framework for short-term multi-step wind speed forecasting based on Functional Networks. Appl Energy 225:902–911

    Article  Google Scholar 

  2. Akinci TC (2011) Short term wind speed forecasting with ANN in Batman, Turkey. Electron Electr Eng 1(107):41–45

    Google Scholar 

  3. Arsie I, Marano V, Rizzo G, Giuseppe Savino G, Moran M (2006) Energy and economic evaluation of a hybrid CAES/wind power plant with neural network-based wind speed forecasting. In: Proceedings of the ECOS conference

  4. Begam KM, Deepa SN (2019) Optimized nonlinear neural network architectural models for multistep wind speed forecasting. Comput Electr Eng 78:32–49

    Article  Google Scholar 

  5. Chen Z, Xue Z, Zhang L, Ji T, Li M, Wu Q (2018a) Analyzing the correlation and predictability of wind speed series based on mutual information. IEEJ Trans Electr Electron Eng 13(12):1829–1830

    Article  Google Scholar 

  6. Chen J, Zeng GQ, Zhou W, Du W, Lu KD (2018b) Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization. Energy Convers Manag 165:681–695

    Article  Google Scholar 

  7. Cheng L, Zang H, Ding T, Sun R, Wang M, Wei Z, Sun G (2018) Ensemble recurrent neural network based probabilistic wind speed forecasting approach. Energies 11(8):1958

    Article  Google Scholar 

  8. Cheng Y, Zhang H, Liu Z, Chen L, Wang P (2019) Hybrid algorithm for short-term forecasting of PM2. 5 in China. Atmos Environ 200:264–279

    Article  Google Scholar 

  9. Fonte PM, Silva GX, Quadrado JC (2005) Wind speed prediction using artificial neural networks. In: Proceedings of the sixth WSEAS international conference on neural networks, pp 134–139

  10. Gendeel M, Yuxian Z, Aoqi H (2018) Performance comparison of ANNs model with VMD for short-term wind speed forecasting. IET Renew Power Gener 12(12):1424–1430

    Article  Google Scholar 

  11. Han Q, Wu H, Hu T, Chu F (2018) Short-term wind speed forecasting based on signal decomposing algorithm and hybrid linear/nonlinear models. Energies 11(11):2976

    Article  Google Scholar 

  12. He Q, Wang J, Lu H (2018) A hybrid system for short-term wind speed forecasting. Appl Energy 226:756–771

    Article  Google Scholar 

  13. Hochreiter S, Schmidhuber J (1997) LSTM can solve hard long time lag problems. In: Advances in neural information processing systems, pp 473–479

  14. Hong YY, Wu CP (2010) Hour-ahead wind power and speed forecasting using market basket analysis and radial basis function network. In: Proceedings of the international conference on power system technology, pp 1–6

  15. Hu YL, Chen L (2018) A nonlinear hybrid wind speed forecasting model using LSTM network, hysteretic ELM and Differential Evolution algorithm. Energy Convers Manag 173:123–142

    Article  Google Scholar 

  16. Huang CJ, Kuo PH (2018) A short-term wind speed forecasting model by using artificial neural networks with stochastic optimization for renewable energy systems. Energies 11(10):2777

    Article  Google Scholar 

  17. Huang Y, Liu S, Yang L (2018) Wind speed forecasting method using EEMD and the combination forecasting method based on GPR and LSTM. Sustainability 10(10):3693

    Article  Google Scholar 

  18. Hunter D, Yu H, Pukish MS, Janusz K, Wilamowski BM (2012) Selection of proper neural network sizes and architectures—a comparative study. IEEE Trans Ind Inf 8(2):228–240

    Article  Google Scholar 

  19. Iqdour R, Zeroual A (2006) The MLP neural networks for predicting wind speed. In: Proceedings of the second international symposium on communications, control and signal processing

  20. Jayaraj S, Padmakumari K, Sreevalsan E, Arun P (2004) Wind speed and power prediction using artificial neural networks. In: Proceedings of the European wind energy conference

  21. Junli W, Xingjie L, Jian Q (2010) Wind speed and power forecasting based on RBF neural network. Proc Int Conf Comput Appl Syst Model 5:298–301

    Google Scholar 

  22. Kennedy J (2011) Particle swarm optimization. In: Encyclopedia of machine learning, Springer, Boston, pp 760–766

  23. Kim W, Hasegawa O (2018) Simultaneous forecasting of meteorological data based on a self-organizing incremental neural network. J Adv Comput Intell Intell Inform 22(6):900–906

    Article  Google Scholar 

  24. Korprasertsak N, Leephakpreeda T (2018) Short-term forecasting models of wind speed for airborne wind turbines: a comparative study. Int J Mech Eng Robot Res 7(3):250–256

    Google Scholar 

  25. Lanzhen L, Fan S (2010) The study on short-time wind speed prediction based on time series neural network algorithm. In: Proceedings of the Asia Pacific power and energy engineering conference, pp 1–5

  26. Li F, Liao HY (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(8):1099–1105

    Article  Google Scholar 

  27. Li Y, Shi H, Han F, Duan Z, Liu H (2019) Smart wind speed forecasting approach using various boosting algorithms, big multi-step forecasting strategy. Renew Energy 135:540–553

    Article  Google Scholar 

  28. Liera PC, Baizan FMC, Feito JL, Valle GDV (2006) Local short-term prediction of wind speed: a neural network analysis. In: Proceedings of the IEMSS conference, pp 124–129

  29. Lin WM, Hong CM (2011) A new Elman neural network-based control algorithm for adjustable-pitch variable-speed wind-energy conversion systems. IEEE Trans Power Electron 26(2):473–481

    Article  Google Scholar 

  30. Liu Y, Zhang S, Chen X, Wang J (2018a) Artificial combined model based on hybrid nonlinear neural network models and statistics linear models—research and application for wind speed forecasting. Sustainability 10(12):4601

    Article  Google Scholar 

  31. Liu H, Mi X, Li Y (2018b) Smart deep learning based wind speed prediction model using wavelet packet decomposition, convolutional neural network and convolutional long short term memory network. Energy Convers Manag 166:120–131

    Article  Google Scholar 

  32. Liu H, Mi X, Li Y, Duan Z, Xu Y (2019) Smart wind speed deep learning based multi-step forecasting model using singular spectrum analysis, convolutional Gated Recurrent Unit network and Support Vector Regression. Renew Energy 143:842–854

    Article  Google Scholar 

  33. Luo X, Sun J, Wang L, Wang W, Zhao W, Wu J, Wang JH, Zhang Z (2018) Short-term wind speed forecasting via stacked extreme learning machine with generalized correntropy. IEEE Trans Ind Inf 14(11):4963–4971

    Article  Google Scholar 

  34. 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

    Article  Google Scholar 

  35. Mi X, Liu H, Li Y (2019) Wind speed prediction model using singular spectrum analysis, empirical mode decomposition and convolutional support vector machine. Energy Convers Manag 180:196–205

    Article  Google Scholar 

  36. Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98

    Article  Google Scholar 

  37. Mohandes MA, Rehman S, Halawani TO (1998) A neural network approach for wind speed prediction. Renew Energy 13(3):345–354

    Article  Google Scholar 

  38. More A, Deo MC (2003) Forecasting wind with neural networks. Mar Struct 16(1):35–49

    Article  Google Scholar 

  39. Moreno SR, dos Santos Coelho L (2018) Wind speed forecasting approach based on Singular Spectrum Analysis and Adaptive Neuro Fuzzy Inference System. Renew Energy 126:736–754

    Article  Google Scholar 

  40. Niu Z, Yu Z, Li B, Tang W (2018) Short-term wind power forecasting model based on deep gated recurrent unit neural network. Electric Power Autom Equip 38(5):36–42

    Google Scholar 

  41. Prema V, Rao KU (2018) Interactive graphical user interface (GUI) for wind speed prediction using wavelet and artificial neural network. J Inst Eng (India) Ser B 99(5):467–477

    Article  Google Scholar 

  42. Qin Q, Lai X, Zou J (2019) Direct multistep wind speed forecasting using LSTM neural network combining EEMD and fuzzy entropy. Appl Sci 9(1):126

    Article  Google Scholar 

  43. Qu Z, Mao W, Zhang K, Zhang W, Li Z (2019) Multi-step wind speed forecasting based on a hybrid decomposition technique and an improved back-propagation neural network. Renew Energy 133:919–929

    Article  Google Scholar 

  44. Salfate I, López-Caraballo CH, Sabín-Sanjulián C, Lazzús JA, Vega P, Cuturrufo F, Marín J (2018) 24-hours wind speed forecasting and wind power generation in La Serena (Chile). Wind Eng 42(6):607–623

    Article  Google Scholar 

  45. Santhosh M, Venkaiah C, Kumar DV (2018) Ensemble empirical mode decomposition based adaptive wavelet neural network method for wind speed prediction. Energy Convers Manag 168:482–493

    Article  Google Scholar 

  46. Sheela KG, Deepa SN (2013) A new Algorithm to find number of hidden neurons in Radial Basic Function networks for wind speed prediction in Renewable Energy Systems. CEAI 15(3):30–37

    Google Scholar 

  47. Shi K, Qiao Y, Zhao W, Wang Q, Liu M, Lu Z (2018) An improved random forest model of short-term wind-power forecasting to enhance accuracy, efficiency, and robustness. Wind Energy 21(12):1383–1394

    Article  Google Scholar 

  48. Silva GX, Fonte PM, Quadrado JC (2006) Radial basis function networks for wind speed prediction. In: Proceedings of the fifth international conference on artificial intelligence, knowledge engineering, and data bases, pp 286–290

  49. Tian C, Hao Y, Hu J (2018) A novel wind speed forecasting system based on hybrid data preprocessing and multi-objective optimization. Appl Energy 231:301–319

    Article  Google Scholar 

  50. Ulkat D, Günay ME (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 Appl 30(10):3037–3048

    Article  Google Scholar 

  51. Wang J, Hu J (2015) A robust combination approach for short-term wind speed forecasting and analysis—Combination of the ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vector Machine) and LSSVM (Least Square SVM) forecasts using a GPR (Gaussian Process Regression) model. Energy 93:41–56

    Article  Google Scholar 

  52. Wang J, Li Y (2018) Multi-step ahead wind speed prediction based on optimal feature extraction, long short term memory neural network and error correction strategy. Appl Energy 230:429–443

    Article  Google Scholar 

  53. Wang J, Zhang N, Lu H (2019) A novel system based on neural networks with linear combination framework for wind speed forecasting. Energy Convers Manag 181:425–442

    Article  Google Scholar 

  54. Xiaojuan H, Xiyun Y, Juncheng L (2010) Short-time wind speed prediction for wind farm based on improved neural network. In: Proceedings of the eighth world congress on intelligent control and automation, pp 5891–5894

  55. Xingpei L, Yibing L, Weidong X (2009) Wind speed prediction based on genetic neural network. In: Proceedings of the fourth IEEE conference on industrial electronics and applications, pp 2448–2451

  56. Yadav AK, Malik H (2019) 10-Min ahead forecasting of wind speed for power generation using nonlinear autoregressive neural network. In: Applications of artificial intelligence techniques in engineering, pp 235–244

  57. Yang Z, Wang J (2018) A hybrid forecasting approach applied in wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm. Energy 160:87–100

    Article  Google Scholar 

  58. Yu C, Li Y, Bao Y, Tang H, Zhai G (2018) A novel framework for wind speed prediction based on recurrent neural networks and support vector machine. Energy Convers Manag 178:137–145

    Article  Google Scholar 

  59. Yu Y, Chen ZM, Li MS, Ji TY, Wu QH (2019) Forecasting a short-term wind speed using a deep belief network combined with a local predictor. IEEJ Trans Electr Electron Eng 14(2):238–244

    Article  Google Scholar 

  60. Zhang Y, Chen B, Zhao Y, Pan G (2018) Wind speed prediction of ipso-bp neural network based on lorenz disturbance. IEEE Access 6:53168–53179

    Article  Google Scholar 

  61. Zhou J, Liu H, Xu Y, Jiang W (2018) A hybrid framework for short term multi-step wind speed forecasting based on variational model decomposition and convolutional neural network. Energies 11(9):2292

    Article  Google Scholar 

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Correspondence to T. Vinothkumar.

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Vinothkumar, T., Deeba, K. Hybrid wind speed prediction model based on recurrent long short-term memory neural network and support vector machine models. Soft Comput 24, 5345–5355 (2020). https://doi.org/10.1007/s00500-019-04292-w

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Keywords

  • LSTM network
  • SVM model
  • Particle swarm optimization
  • Ant lion optimization algorithm
  • Wind speed
  • Prediction accuracy