One parametric approach for short-term JPDF forecast of wind generation

IEEE Transactions on Industry Applications

Volumn 50, Issue 4, 2014, Pages 2837-2843

  Yang, Ming ^a   Zhu, Simeng ^a   Liu, Meng ^b   Lee, Wei Jen ^b  

^a SHANDONG UNIVERSITY   (China)

^b UNIVERSITY OF TEXAS AT ARLINGTON   (United States)

Author keywords

Joint probability density function (JPDF) forecast; power systems; sparse Bayesian learning (SBL); support vector machine (SVM); wind power forecast

Indexed keywords

COVARIANCE MATRIX; ELECTRIC POWER UTILIZATION; ELECTRIC UTILITIES; PROBABILITY DENSITY FUNCTION; PROBABILITY DISTRIBUTIONS; STANDBY POWER SYSTEMS; SUPPORT VECTOR MACHINES; WIND POWER;

CORRELATION MATRIX; GAUSSIAN ASSUMPTION; JOINT PROBABILITY DENSITY FUNCTION; PARAMETRIC APPROACH; SPARSE BAYESIAN LEARNING (SBL); TIME-DOMAIN CORRELATION; VARIANCE INFORMATION; WIND POWER FORECAST;

FORECASTING;

EID: 84904689247     PISSN: 00939994     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIA.2014.2300188     Document Type: Article

References (21)

1. A. Costa et al., "A review on the young history of the wind power short term prediction," Renew. Sustainable Energy Rev., vol. 12, no. 6, pp. 1725-1744, Aug. 2008. (Pubitemid 351626911)
2. G. H. Riahy and M. Abedi, "Short term wind speed forecasting for wind turbine application using linear prediction method," Renew. Energy, vol. 33, no. 1, pp. 35-41, Jan. 2008. (Pubitemid 350008057)
3. L. Liu, H. Q. Tian, C. Chen, and Y. F. Li, "A hybrid statistical method to predict wind speed and wind power," Renew. Energy, vol. 35, no. 8, pp. 1857-1861, Aug. 2010.
4. G. Giebel, L. Landberg, T. S. Nielsen, and H. Madsen, "The Zephyr project-The next generation prediction system," in Proc. Eur. Wind Energy Conf., 2001, pp. 777-780.
5. K. Methaprayoon, C. Yingvivatanapong, W. J. Lee, and J. Liao, "An integration of ANN wind power estimation into unit commitment considering the forecasting uncertainty," IEEE Trans. Ind. Appl., vol. 43, no. 6, pp. 1441-1448, Nov./Dec. 2007. (Pubitemid 350201129)
6. S. Fan, J. R. Liao, R. Yokoyama, L. Chen, and W. J. Lee, "Forecasting the wind generation using a two-stage network based on meteorological information," IEEE Trans. Energy Convers., vol. 24, no. 2, pp. 474-482, Jun. 2009.
7. G. N. Kariniotakis, G. S. Stavrakakis, and E. F. Nogaret, "Wind power forecasting using advanced neural networks models," IEEE Trans. Energy Convers., vol. 11, no. 4, pp. 762-767, Dec. 1996. (Pubitemid 126771432)
8. F. Bouffard and F. D. Galiana, "Stochastic security for operations planning with significant wind power generation," IEEE Trans. Power Syst., vol. 23, no. 2, pp. 306-316, May 2008. (Pubitemid 351695808)
9. J. Liang, S. Grijalva, and R. G. Harley, "Increased wind revenue and system security by trading wind power in energy and regulation reserve markets," IEEE Trans. Sustain. Energy, vol. 2, no. 3, pp. 340-347, Jul. 2011.
10. J. B. Bremnes, "Probabilistic wind power forecasts using local quantile regression," Wind Energy, vol. 7, no. 1, pp. 47-54, Jan.-Mar. 2004. (Pubitemid 40290428)
11. J. Juban, N. Siebert, and G. N. Kariniotakis, "Probabilistic short-term wind power forecasting for the optimal management of wind generation," in Proc. IEEE Lausanne Power Technol. Conf., 2007, pp. 683-686.
12. B. W. Silverman, "Density estimation for statistics and data analysis," in Monographs on Statistics and Applied Probability. London, U.K.: Chapman & Hall, 1986.
13. 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.
14. M. E. Tipping and A. C. Faul, "Fast marginal likelihood maximization for sparse Bayesian models," in Proc. 9th Int. Workshop Artif. Intell. Stat. Conf., 2003, pp. 1-13.
15. T. Gneiting, F. Balabdaoui, and A. E. Raftery, "Probabilistic forecasts, calibration and sharpness," J. R. Stat. Soc. B, Stat. Methodol., vol. 69, no. 2, pp. 243-268, Apr. 2007.
16. P. Pinson, G. Papaefthymiou, B. Bklockl, and H. A. Nielsen, "Generation of statistical scenarios of short-term wind power production," in Proc. IEEE Lausanne Power Technol. Conf., 2007, pp. 491-496.
17. C. Cortes and V. Vapnik, "Support vectormachine," Mach. Learn., vol. 20, no. 3, pp. 273-297, Sep. 1995.
18. S. M. Zhu, M. Yang, and X. S. Han, "Short-term generation forecast of wind farm using SVM-GARCH approach," in Proc. IEEE POWERCON Technol., 2012, pp. 1-6.
19. M. E. Tipping, "Sparse Bayesian learning and the relevance vector machine," J. Mach. Learn. Res., vol. 1, no. 1, pp. 211-244, Sep. 2001. (Pubitemid 33687203)
20. J. Wang, M. Shahidehpour, and Z. Li, "Security-constrained unit commitment with volatile wind power generation," IEEE Trans. Power Syst., vol. 23, no. 3, pp. 1319-1327, Aug. 2008.
21. W. H. Williams and M. L. Goodman, "A simple method for the construction of empirical confidence limits for economic forecasts," Amer. Stat. Assoc., vol. 66, no. 336, pp. 752-754, Dec. 1971.