Forecasting wind speed using empirical mode decomposition and Elman neural network

Applied Soft Computing Journal

Volumn 23, Issue , 2014, Pages 452-459

  Wang, Jujie ^a,b   Zhang, Wenyu ^b   Li, Yaning ^b   Wang, Jianzhou ^b   Dang, Zhangli ^b  

^a NANJING UNIVERSITY OF INFORMATION SCIENCE AND TECHNOLOGY   (China)

^b LANZHOU UNIVERSITY   (China)

Author keywords

Elman neural network; EMD; Hybrid model; PACF; Wind speed prediction

Indexed keywords

COMPLEX NETWORKS; FORECASTING; NEURAL NETWORKS; SIGNAL PROCESSING;

ELMAN NEURAL NETWORK; EMD; HYBRID MODEL; PACF; WIND SPEED PREDICTION;

WIND;

EID: 84905494402     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2014.06.027     Document Type: Article

References (42)

1. L. Ma, S.Y. Luan, C.W. Jiang, H.L. Liu, and Y. Zhang A review on the forecasting of wind speed and generated power Renew. Sustain. Energy Rev. 13 2009 915 920
2. A. Shiu, and P.L. Lam Electricity consumption and economic growth in China Energy Policy 32 1 2004 47 54 (Pubitemid 36996891)
3. J.J. Chen Development of offshore wind power in China Renew. Sustain. Energy Rev. 15 9 2011 5013 5020
4. Z.H. Guo, J. Zhao, W.Y. Zhang, and J.Z. Wang A corrected hybrid approach for wind speed prediction in Hexi Corridor of China Energy 36 3 2011 1668 1679
5. E. Erdem, and J. Shi ARMA based approaches for forecasting the tuple of wind speed and direction Appl. Energy 88 2011 1405 1414
6. H. Liu, H.Q. Tian, and Y.F. Li Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction Appl. Energy 98 2012 415 424
7. M. Khalid, and A.V. Savkin A method for short-term wind power prediction with multiple observation points IEEE Trans. Pow. Syst. 27 2 2012 579 586
8. H.P. Liu, J. Shi, and E. Erdem Prediction of wind speed time series using modified Taylor Kriging method Energy 35 2010 4870 4879
9. A. Costa, A. Crespo, J. Navarro, G. Lizcano, H. Madsen, and E. Feitosa A review on the young history of the wind power short-term prediction Renew. Sustain. Energy Rev. 12 6 2008 1725 1744 (Pubitemid 351626911)
10. J.M. Morales, R. Mínguez, and A.J. Conejo A methodology to generate statistically dependent wind speed scenarios Appl. Energy 87 3 2010 843 855
11. D.A. Fadare The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria Appl. Energy 87 3 2010 934 942
12. L. Landberg Short-term prediction of the power production from wind farms J. Wind Eng. Ind. Aerodyn. 80 1-2 1999 207 220 (Pubitemid 30024264)
13. L. Landberg A mathematical look at a physical power prediction model Wind Energy 1 1 1998 23 28
14. G.H. Riahy, and M. Abedi Short term wind speed forecasting for wind turbine applications using linear prediction method Renew. Energy 33 1 2008 35 41 (Pubitemid 350008057)
15. R.G. Kavasseri, and K. Seetharaman Day-ahead wind speed forecasting using f-ARIMA models Renew. Energy 34 2009 1388 1393
16. K. Lalarukh, and Z.J. Yasmin Time series models to simulate and forecast hourly averaged wind speed in Quetta: Pakistan Solar Energy 61 1 1997 23 32
17. J.L. Torres, A. García, M. De Blas, and A. De Francisco Forecast of hourly average wind speed with ARMA models in Navarre Sol. Energy 79 1 2005 65 77
18. P. Flores, A. Tapia, and G. Tapia Application of a control algorithm for wind speed prediction and active power generation Renew. Energy 30 4 2005 523 536 (Pubitemid 39447654)
19. S. Salcedo-Sanz, A.M. Pérez-Bellido, E.G. Ortiz-García, A. Portilla-Figueras, L. Prieto, and D. Paredes Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction Renew. Energy 34 6 2009 1451 1457
20. E. Cadenas, and W. Rivera Short term wind speed forecasting in La Venta: Oaxaca, México, using artificial neural networks Renew. Energy 34 1 2009 274 278
21. A. Sfetsos A comparison of various forecasting techniques applied to mean hourly wind speed time series Renew. Energy 21 1 2000 23 35 (Pubitemid 30306417)
22. T.G. Barbounis, and J.B. Theocharis A locally recurrent fuzzy neural network with application to the wind speed prediction using spatial correlation Neurocomputing 70 7-9 2007 1525 1542 (Pubitemid 46336755)
23. M.A. Mohandes, T.O. Halawani, S. Rehman, and A.A. Hussain Support vector machines for wind speed prediction Renew. Energy 29 2004 939 947 (Pubitemid 38242182)
24. Y. Chauvin, and D.E. Rumelhart Backpropagation: Theory, Architectures, and Applications 1995 Lawrence Erlbaum Associates New Jersey
25. S. Haykin Neural Network: a Comprehensive Foundation 1999 Prentice-Hall Englewood Cliffs, NJ
26. P.D. McNelis Neural Networks in Finance: Gaining Predictive Edge in the Market 2004 Academic Press New York
27. M.T. Hagan, H.B. Demuth, and M.H. Beale Neural Network Design 1996 PWS Publishing Boston
28. A. Graves, M. Liwicki, S. Fernandez, R. Bertolami, H. Bunke, and J. Schmidhuber A novel connectionist system for unconstrained handwriting recognition IEEE Trans. Pattern Anal. Mach. Intell. 31 5 2009 855 868
29. M. Ardalani-Farsa, and S. Zolfaghari Chaotic time series prediction with residual analysis method using hybrid Elman-NARX neural networks Neurocomputing 73 2010 2540 2553
30. R. Chandra, and M.J. Zhang Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction Neurocomputing 86 2012 116 123
31. M. Cacciola, G. Megali, D. Pellicano, and F.C. Morabito Elman neural networks for characterizing voids in welded strips: a study Neural Comput. Appl. 21 2012 869 875
32. N.E. Huang, Z. Shen, S.R. Long, M.C. Wu, H.H. Shih, and Q.N. Zheng et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis Proc. R. Soc. Lond. A - Math. Phys. Eng. Sci. 454 1998 903 995 (Pubitemid 128733183)
33. C.F. Chen, M.C. Lai, and C.C. Yeh Forecasting tourism demand based on empirical mode decomposition and neural network Knowl. Based Syst. 26 2012 281 287
34. J.S. Cheng, D.J. Yu, and Y. Yang A fault diagnosis approach for roller bearings based on EMD method and AR model Mech. Syst. Signal Process. 20 2 2006 350 362
35. X. Zhang, K.K. Lai, and S.Y. Wang A new approach for crude oil price analysis based on Empirical Mode Decomposition Energy Econ. 30 3 2008 905 918 (Pubitemid 351292427)
36. H.L. Liang, S.L. Bressler, R. Desimone, and P. Fries Empirical mode decomposition: a method for analyzing neural data Neurocomputing 65-66 2005 801 807 (Pubitemid 40613619)
37. J. Elman Finding structure in time Cognitive 15 1990 179 211
38. E. Cadenas, and W. Rivera Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks Renew. Energy 34 2009 274 278
39. H. Wang, and W. Zhao ARIMA model estimated by particle swarm optimization algorithm for consumer price index forecasting, lecture notes in computer Science Artif. Intell. Comput. Intell. 5855 2009 48 58
40. Z.H. Guo, W.G. Zhao, H.Y. Lu, and J.Z. Wang Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model Renew. Energy 37 2012 241 249
41. A.P. Plumb, R.C. Rowe, P. York, and M. Brown Optimisation of the predictive ability of artificial neural network (ANN) models: a comparison of three ANN programs and four classes of training algorithm Eur. J. Pharm. Sci. 25 2005 395 405 (Pubitemid 40853867)
42. V.N. Vapnik Statistical Learning Theory 1998 John Wiley & Sons, Inc. Canada