A novel wind speed forecasting method based on ensemble empirical mode decomposition and GA-BP neural network

IEEE Power and Energy Society General Meeting

Volumn , Issue , 2013, Pages

  Wang, Yamin ^a   Wang, Shouxiang ^a   Zhang, Na ^a  

^a TIANJIN UNIVERSITY   (China)

Author keywords

EEMD; EMD; GA BP Neural Network; Genetic Algorithm; Wind Speed Forecasting

Indexed keywords

EEMD; EMD; EMPIRICAL MODE DECOMPOSITION; ENSEMBLE EMPIRICAL MODE DECOMPOSITION; ENSEMBLE EMPIRICAL MODE DECOMPOSITIONS (EEMD); GA-BP NEURAL NETWORKS; WIND ENERGY CONVERSION SYSTEM; WIND SPEED FORECASTING;

ELECTRIC UTILITIES; FORECASTING; GENETIC ALGORITHMS; NEURAL NETWORKS; WIND; WIND POWER;

SIGNAL PROCESSING;

EID: 84893188504     PISSN: 19449925     EISSN: 19449933     Source Type: Conference Proceeding    
DOI: 10.1109/PESMG.2013.6672195     Document Type: Conference Paper

References (18)

1. L. Landberg, "A mathematical look at a physical power prediction model", Wind Energy, vol. l, pp. 23-28, Sep. 1998.
2. J. S. Hong, "Evaluation of the high-resolution model forecasts over the Taiwan area during GEVIEX", Weather and Forecasting, vol. 18, pp. 23-28, 1998.
3. th Conference on Probability and Statistics in the Atmospheric Science, American Meteorological Society Annual Meeting Seattle, Washington, 2004.
4. T. H. M. El-Fouly, E. F. El-Saadany and M. M. A. Salama, "Grey predictor for wind energy conversion systems output power prediction", IEEE Transaction on Power System, vol. 21, no. 3, Aug. 2006.
5. G. N. Kariniotakis, G. S. Stavrakakis and E. F. Nogaret, "Wind power forecasting using advanced neural network models", IEEE Transactions on Energy Conversion, vol. II, pp. 762-767, 1996.
6. A. More and M. C. Deo, "Forecasting wind with neural networks", Marine Structures, vol. 16, pp. 35-49, 2003.
7. G. Li and J. Shi, "On comparing three artificial neural networks for wind speed forecasting", Applied Energy, vol. 87, pp. 2313-2320, 2010.
8. M. Monfared, H. Rastegar and H. M. Kojabadi, "A new strategy for wind speed forecasting using artificial intelligent methods", Renewable Energy, vol. 34, pp. 845-848, 2009.
9. W. Tong, Z. Mingcai, Y. Qihao, and Z. Huyuan, "Comparing the applications of EMD and EEMD on time frequency analysis of seismic signal", Journal of Applied Geophysics, vol. 83, pp. 29-34, 2012.
10. Y. Lei, Z. He and Y. Zi, "Application of the EEMD method to rotor fault diagnosis of rotating machinery", Mechanical Systems and Signal Processing, vol. 23, pp. 1327-1338, 2009.
11. Y. Zhou, T. Tao, X. Mei, G. Jiang, and N. Sun, "Feed-axis gearbox condition monitoring using built-in position sensors and EEMD method", Robotics and Computer-integrated Manufacturing, vol. 27, pp. 785-793, 2011.
12. M. J. Lin and Y. Jeng, "Application of the VLF-EM method with EEMD to the study of a mud volcano in southern Taiwan", Geomorphology, vol. 119, pp. 97-110, 2010.
13. N. E. Huang, Z. Shen and S. R. Long, "The Empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis", Proceedings of the Royal Society of London, vol. 454, pp. 903 995, 1998.
14. Z. H. Wu and N. E. Huang, "Ensemble empirical mode decomposition: a noise assisted data analysis method", Advances in Adaptive Data Analysis, vol. I, pp. 1-41, 2009.
15. P. Flandrin, G. Rilling, and P. Goncalves, "Empirical mode decomposition as a filter bank", IEEE Signal Processing Letters, vol. II, pp. 112 114, Feb. 2004.
16. Z. H. Wu and N. E. Huang, "A study of the characteristics of white noise using the empirical mode decomposition method", Proceedings of the Royal Society of London, vol. 460 A, pp. 1597-1611, 2004.
17. S. A. Kalogirou, "Arti ficial neural networks in renewable energy systems applications: a review", Renewable and Sustainable Energy Reviews, vol. 5, pp. 373-401, 2001.
18. O. J. Montana and L. Davis, "Training feed Forward neural network using genetic algorithm", in Proc. the lIth International Joint Conference on Artificial intelligence, 1989, pp. 762 767.