Probabilistic Wind Generation Forecast Based on Sparse Bayesian Classification and Dempster-Shafer Theory

IEEE Transactions on Industry Applications

Volumn 52, Issue 3, 2016, Pages 1998-2005

  Yang, Ming ^a,b   Lin, You ^a   Han, Xueshan ^a  

^a SHANDONG UNIVERSITY   (China)

^b ARGONNE NATIONAL LABORATORY   (United States)

Author keywords

Dempster Shafer theory; nonparametric approach; probabilistic wind generation forecast

Indexed keywords

BARIUM COMPOUNDS; DISTRIBUTION FUNCTIONS; ERRORS; PROBABILISTIC LOGICS; PROBABILITY DISTRIBUTIONS; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES; WIND POWER;

CONDITIONAL PROBABILITIES; DEMPSTER-SHAFER THEORY; GENERALIZATION CAPABILITY; NONPARAMETRIC APPROACHES; PROBABILISTIC FORECASTS; QUANTILE REGRESSION; SPARSE BAYESIAN CLASSIFIERS; WIND GENERATION FORECAST;

FORECASTING;

EID: 84973340593     PISSN: 00939994     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIA.2016.2518995     Document Type: Article

References (29)

1. S. E. Thor and P. Weis-Taylor, "Long-term research and development needs for wind energy for the time frame 2000-2020," Wind Energy, vol. 5, no. 1, pp. 73-75, 2002.
2. A. Costa et al., "A review on the young history of the wind power shortterm prediction," Renew. Sustain. Energy Rev., vol. 12, no. 6, pp. 1725-1744, 2008.
3. G. Giebel et al., "The state-of-the-art in short-term prediction of wind power: A literature overview, 2nd edition," ANEMOS. plus, Roskilde, Denmark, Tech. Rep., Jan. 2011.
4. T. Hong, P. Pinson, and S. Fan, "Global energy forecasting competition 2012," Int. J. Forecast., vol. 30, pp. 357-363, 2014.
5. A. Bracale and P. De Falco, "An advanced bayesian method for shortterm probabilistic forecasting of the generation of wind power," Energies, vol. 8, no. 9, pp. 10293-10314, 2015.
6. P. Pinson and G. Kariniotakis, "Conditional prediction intervals of wind power generation," IEEE Trans. Power Syst., vol. 25, no. 4, pp. 1845-1856, Nov. 2010.
7. C. Wan et al., "Probabilistic forecasting of wind power generation using extreme learning machine," IEEE Trans. Power Syst., vol. 29, no. 3, pp. 1033-1044, May 2014.
8. Y. Zhang, J. Wang, and X. Wang, "Review on probabilistic forecasting of wind power generation," Renew. Sustain. Energy Rev., vol. 32, pp. 255-270, 2014.
9. M. Yang, S. Fan, and W. J. Lee, "Probabilistic short-term wind power forecast using componential sparse Bayesian learning," IEEE Trans. Ind. Appl., vol. 49, no. 6, pp. 2783-2792, Nov./Dec. 2013.
10. M. Yang et al., "One parametric approach for short-term JPDF forecast of wind generation," IEEE Trans. Ind. Appl., vol. 50, no. 4, pp. 2837-2843, Jul./Aug. 2014.
11. P. Kou, D. Liang, F. Gao, and L. Gao, "Probabilistic wind power forecasting with online model selection and warped Gaussian process," Energy Convers. Manage., vol. 84, pp. 649-663, 2014.
12. H. Bludszuweit, J. A. Domínguez-Navarro, and A. Llombart, "Statistical analysis of wind power forecast error," IEEE Trans. Power Syst., vol. 23, no. 3, pp. 983-991, Aug. 2008.
13. Z. S. Zhang et al., "A versatile probability distribution model for wind power forecast errors and its application in economic dispatch," IEEE Trans. Power Syst., vol. 28, no. 3, pp. 3114-3125, Aug. 2013.
14. P. Pinson, "Very-short-term probabilistic forecasting of wind power with generalized logit-normal distributions," J. R. Stat. Soc., Appl. Stat., vol. 61, no. 4, pp. 555-576, 2012.
15. A. U. Haque, M. H. Nehrir, and P. Mandal, "A hybrid intelligent model for deterministic and quantile regression approach for probabilistic wind power forecasting," IEEE Trans. Power Syst., vol. 29, no. 4, pp. 1-10, Jul. 2014.
16. J. Juban, L. Fugon, and G. Kariniotakis, "Probabilistic short-term wind power forecasting based on kernel density estimators," in Proc. Eur. Wind Energy Conf. (EWEC), 2007, pp. 1-11.
17. G. Sideratos and N. D. Hatziargyriou, "Probabilistic wind power forecasting using radial basis function neural networks," IEEE Trans. Power Syst., vol. 27, no. 4, pp. 1788-1796, Nov. 2012.
18. C. Cortes and V. Vapnik, "Support vector machine," Mach. Learn., vol. 20, no. 3, pp. 273-297, 1995.
19. S. M. Zhu, M. Yang, and X. S. Han, "Short-term generation forecast of wind farm using SVM-GARCH approach," in Proc. IEEE Int. Conf. Power Syst. Technol. (POWERCON), 2012, pp. 1-6.
20. C. Chatfield, The Analysis of Time Series: An Introduction. Boca Raton, FL, USA: CRC Press, 2013.
21. M. E. Tipping, "Sparse Bayesian learning and the relevance vector machine," J. Mach. Learn. Res., vol. 1, pp. 211-244, 2001.
22. N. Wilson. "Algorithms for Dempster-Shafer theory," in Handbook of Defeasible Reasoning and Uncertainty Management Systems. New York, NY, USA: Springer, 2000, pp. 421-475.
23. H. Madsen et al., "Standardizing the performance evaluation of shortterm wind power prediction models," Wind Eng., vol. 29, no. 6, pp. 475-489, 2005.
24. W. H. Williams and M. L. Goodman, "A simple method for the construction of empirical confidence limits for economic forecasts," J. Amer. Stat. Assoc., vol. 66, no. 336, pp. 752-754, 1971.
25. T. Gneiting, B. Fadoua, and E. R. Adrian, "Probabilistic forecasts, calibration and sharpness," J. R. Stat. Soc., Stat. Method., vol. 69, pp. 243-268, 2007.
26. T. Hong, P. Pinson, S. Fan, H. Zareipour, A. Troccoli, and R. J. Hyndman, "Probabilistic energy forecasting: State-of-the-art 2015," Int. J. Forecast., 2016.
27. U. Focken, M. Lange, K. Mönnich, H. P. Waldl, H. G. Beyer, and A. Luig, "Short-term prediction of the aggregated power output of wind farms-A statistical analysis of the reduction of the prediction error by spatial smoothing effects," J. Wind Eng. Ind. Aerodyn., vol. 90, no. 3, pp. 231-46, 2002.
28. M. Yang et al., "Multi-dimensional scenario forecast for generation of multiple wind farms," J. Mod. Power Syst. Clean Energy, vol. 3, no. 3, pp. 361-370, 2015.
29. P. Li, X. Guan, and J. Wu, "Aggregated wind power generation probabilistic forecasting based on particle filter," Energy Convers. Manage., vol. 96, pp. 579-587, May 2015.