Short-term wind power forecasting based on T-S fuzzy model

Asia-Pacific Power and Energy Engineering Conference, APPEEC

Volumn 2016-December, Issue , 2016, Pages 414-418

  Liu, Fang ^a   Li, Ranran ^a   Li, Yong ^b   Cao, Yijia ^b   Panasetsky, Daniil ^c   Sidorov, Denis ^c  

^a CENTRAL SOUTH UNIVERSITY   (China)

^b HUNAN UNIVERSITY   (China)

^c ENERGY SYSTEMS INSTITUTE   (Russian Federation)

Author keywords

Fuzzy c means (FCM); Recursive least squares method (RLS); T S fuzzy model; Wind power forecasting

Indexed keywords

CLUSTERING ALGORITHMS; FORECASTING; FUZZY SYSTEMS; LEAST SQUARES APPROXIMATIONS; SIGNAL PROCESSING; SUPPORT VECTOR MACHINES; WIND; WIND EFFECTS; WIND POWER;

EMPIRICAL MODE DECOMPOSITION; FUZZY C MEAN; FUZZY C-MEANS CLUSTERING ALGORITHMS; RECURSIVE LEAST SQUARES METHOD; SHORT-TERM WIND POWER FORECASTING; T-S FUZZY MODELS; TEMPERATURE AND PRESSURES; WIND POWER FORECASTING;

WEATHER FORECASTING;

EID: 85009944221     PISSN: 21574839     EISSN: 21574847     Source Type: Conference Proceeding    
DOI: 10.1109/APPEEC.2016.7779537     Document Type: Conference Paper

References (14)

1. J. Wang, J. Hu, K. Ma, Y. Zhang, "A self-adaptive hybrid approach for wind speed forecasting," Renewable Energy, vol. 78, pp. 374-385, Jun. 2015
2. M. Li, "SVM with improved grid search and its application to wind power prediction," Machine Learning and Cybernetics (iCMLC), vol. 2, pp. 603-609, Jul. 2013
3. J. Yan, "Reviews on uncertainty analysis of wind power forecasting," Renewable and Sustainable Energy Reviews, vol. 52, pp. 1322-1330, Dec. 2015
4. Z. Men, E. Yee, F.S. Lien, D. Wen, Y. Chen, "Short-term wind speed and power forecasting using an ensemble of mixture density neural networks," Renewable Energy, vol. 87, pp. 203-211, Mar. 2016
5. Z. Liang, J. Liang, C. Wang, X. Dong, X. Miao, "Short-term wind power combined forecasting based on error forecast correction," Energy Conversion and Management, vol. 119, pp. 215-226, July. 2016
6. H. Chitsaz, N. Amjady, H. Zareipour, "Wind power forecast using wavelet neural network trained by improved Clonal selection algorithm," Energy Conversion and Management, vol. 89, pp. 588-598, Jan. 2015
7. G. J. Osorio, J. C. O. Matias, and J. P. S. Catalllo, "Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evol.utionary particle swarm optimization, wavelet transform and mutual information," Renewable Energy, vol. 75, pp. 301-307, Mar. 2015
8. W. Long, and Q. Wang, "Two Methods of Correlation Coefficient on Compositional Data," Procedia Computer Science, vol. 18, pp. 1757-1763, May. 2013
9. J Wu, B Zhang, H Li, Z Li, Y Chen, X Miao, "Statistical distribution for wind power forecast error and its application to determine optimal size of energy storage system," international Journal of Electrical Power & Energy Systems, vol. 55, pp. 100-107, Sept. 2013
10. RRB. De. Aquino, T. B. Ludermir. N. O. Nobrega, "Forecasting models of wind power in Northeastern of Brazil," in 2013 iEEE Neural NehVorks (UCNN) Conf., pp. 1-8
11. A. U. Haque, M. H. Nehrir, P. MandaI, "A hybrid intelligent model for deterministic and quantile regression approach for probabilistic wind power forecasting," iEEE Trans. Power Systems, vol. 29, no. 4, pp. 1663-1672, Jan. 2014
12. A. U. Haque, P. MandaI, M. H. Nehrir, "A hybrid intelligent framework for wind power forecasting engine," in 2014 iEEE Electrical Power and Energy Conj, pp. 184-189
13. M. Baris, S. Buhan, T. Demirci, Y. Arslan, "Improved Wind Power Forecasting Using Combination Methods," in 2015 iEEE 14th international Conference on Machine Learning and Applications (iCMLA), pp. 1142-1147
14. C. Wei and M. Yuan, "Forecasting wind power in the Mai Liao Wind Farm based on the multi-layer percept ron artificial neural network model with improved simplified swarm optimization," international Journal of Electrical Power & Energy Systems, vol. 55, pp. 741-748, Feb. 2014.