Prediction interval estimation for wind farm power generation forecasts using support vector machines

Proceedings of the International Joint Conference on Neural Networks

Volumn 2015-September, Issue , 2015, Pages

  Shrivastava, Nitin Anand ^a   Khosravi, Abbas ^b   Panigrahi, B K ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY DELHI   (India)

^b DEAKIN UNIVERSITY   (Australia)

Author keywords

Prediction interval; support vector machine; uncertainty; wind power

Indexed keywords

COMPLEX NETWORKS; ELECTRIC POWER GENERATION; ELECTRIC POWER SYSTEM INTERCONNECTION; ELECTRIC UTILITIES; FORECASTING; STATISTICAL MECHANICS; SUPPORT VECTOR MACHINES; UNCERTAINTY ANALYSIS; WIND POWER;

COMPUTATIONAL BURDEN; LOWER AND UPPER BOUNDS; MARKET PARTICIPANTS; OPTIMAL MANAGEMENT; POWER GENERATION FORECASTS; PREDICTION INTERVAL; UNCERTAINTY; UNCERTAINTY QUANTIFICATIONS;

WEATHER FORECASTING;

EID: 84951133044     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2015.7280670     Document Type: Conference Paper

References (33)

1. U. D. of Energy. (2008, Jul.) 20 percent wind energy by 2030: Increasing wind energys contribution to U. S. electricity supply doe/go-02008-2567, jul. 2008 @ONLINE. [Online]. Available: http://www. nrel. gov/docs/fy080sti/41869. pdf
2. L. Landberg and S. Watson, "Short-term prediction of 10caJ wind conditions," Boundary-Layer Meteorology, vol. 70, no. 1-2, pp. 171-195, 1994.
3. L. Landberg, G. Giebel, H. A. Nielsen, T. Nielsen, and H. Madsen, "Short-term prediction: An overview," Wind Energy, vol. 6, no. 3, pp. 273-280, 2003. [Online]. Available: http://dx. doi. org/IO. 1002/we. 96
4. M. Lange and U. Focken, P hysical Approach to Short-term Wind Power P rediction. Heidelberg, Germany: Springer-Verlag, 2005.
5. S. Rajagopalan and S. Santoso, "Wind power forecasting and error analysis using the autoregressive moving average modeling," in IEEE Power Energy Society General Meeting, 2009., July 2009, pp. 1-6.
6. M. Mohandes, T. Halawani, S. Rehman, and A. A. Hussain, "A neural networks approach for wind speed prediction," Renewable Energy, vol. 13, no. 3, pp. 345-354, 1998.
7. G. Kariniotakis, G. Stavrakakis, and E. F. Nogaret, "Wind power forecasting using advanced neural networks models," IEEE Transactions on Energy Conversion, vol. 11, no. 4, pp. 762-767, Dec 1996.
8. M. Mohandes, T. Halawani, S. Rehman, and A. A. Hussain, "A locally recurrent fuzzy neural network with application to the wind speed prediction using spatial correlation," Neurocomputing, vol. 70, no. 79, pp. 1525-1542, 2007.
9. -, "Support vector machines for wind speed prediction," Renewable Energy, vol. 29, no. 6, pp. 939-947, 2004.
10. A. Khosravi, S. Nahavandi, and D. Creighton, "A neural networkGARCH-based method for construction of prediction intervals," Electric Power Systems Research, vol. 96, no. 0, pp. 185-193, 2013.
11. 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 Transaction on Industry Application, vol. 43, no. 6, pp. 1441-1448, Nov 2007.
12. A. KU, "Forecasting to understand uncertainty in electricity prices," Platts energy business & technology, vol.-, no.-, pp. 59-64, May/June 2002.
13. J. G. D. Gooijer and R. J. Hyndman, "25 years of time series forecasting," International Journal of Forecasting, vol. 22, no. 3, pp. 443-473, 2006, twenty five years of forecasting.
14. N. Meade and T. Islam, "Prediction intervals for growth curve forecasts," Journal of Forecasting, vol. 14, no. 5, pp. 413-430, 1995. [Online]. Available: http://dx. doi. orgllO. 1002/for. 3980140502
15. T. Heskes, "Practical confidence and prediction intervals," in Advances in Neural Information P rocessing Systems 9. MIT press, 1997, pp. l76-182.
16. A. Khosravi, S. Nahavandi, and D. Creighton, "Quantifying uncertainties of neural network-based electricity price forecasts," Applied Energy, vol. 112, no. 0, pp. 120-129, 2013. [Online]. Available: http://www. sciencedirect. com/science/article/pii/S0306261913004881
17. C. M. Bishop, Neural Networks for Pattern Recognition. New York, NY, USA: Oxford University Press, Inc., 1995.
18. J. T. G. Hwang and A. A. Ding, "Prediction intervals for artificial neural networks," Journal of the American Statistical Association, vol. 92, no. 438, pp. 748-757, 1997.
19. D. Nix and A. Weigend, "Estimating the mean and variance of the target probability distribution," in Proc. IEEE Int. Conf Neural Netw. World Congr. Comput. Intell., vol. I, 1994, pp. 55-60 voLl.
20. A. Khosravi, S. Nahavandi, D. Creighton, and A. Atiya, "Lower upper bound estimation method for construction of neural network-based prediction intervals," IEEE Transactions on Neural Networks, vol. 22, no. 3, pp. 337-346, 2011.
21. P. Pinson, H. A. Nielsen, J. K. Miler, H. Madsen, and G. N. Kariniotakis, "Non-parametric probabilistic forecasts of wind power: required properties and evaluation," Wind Energy, vol. 10, no. 6, pp. 497-516,2007. [Online]. Available: http://dx. doi. org/1O. 1002/we. 230
22. H. Bludszuweit, J. Dominguez-Navarro, and A. L1ombart, "Statistical analysis of wind power forecast error," IEEE Transactions on Power Systems, vol. 23, no. 3, pp. 983-991, Aug 2008.
23. C. Monteiro, R. Bessa, V. Miranda, A. Botterud, J. Wang, and G. Conzelmann, "Wind power forecasting: State-of-the-art 2009," Argonne Nat. Lab., Tech. Rep. Rep. ANL/DIS-lO-l., November 2009.
24. J. B. Bremnes, "Probabilistic wind power forecasts using local quantile regression," Wind Energy, vol. 7, no. 1, pp. 47-54, 2004. [Online]. Available: http://dx. doi. org/10. 1002/we. 107
25. A. Khosravi, S. Nahavandi, and D. Creighton, "Prediction intervals for short-term wind farm power generation forecasts," IEEE Transactions on Sustainable Energy, vol. 4, no. 3, pp. 602-610, July 2013.
26. A. Khosravi and S. Nahavandi, "Combined non parametric prediction intervals for wind power generation," IEEE Transactions on Sustainable Energy, vol. 4, no. 4, pp. 849-856, Oct 2013.
27. N. Shrivastava, A. Khosravi, and B. Panigrahi, "Prediction interval estimation of electricity prices using PSO tuned support vector machines," IEEE Transactions on Industrial Informatics, vol. 11, no. 2, pp. 322-331, April 2015.
28. A. J. Smola and B. Schiilkopf, "A tutorial on support vector regression," Statistics and Computing, vol. 14, no. 3, pp. 199-222, Aug. 2004.
29. C.-c. Chang and C.-J. Lin, "LIBSYM: A library for support vector machines," ACM Transactions on Intelligent Systems and Technology, vol. 2, pp. 27:1-27:27,2011.
30. J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proceedings International Joint Conference on Neural Networks, vol. 4, 1995, pp. 1942-1948.
31. C. J. C. Burges, "A tutorial on support vector machines for pattern recognition," Data Mining and Knowledge Discovery, vol. 2, pp. 121-167, 1998.
32. A. Khosravi, S. Nahavandi, D. Creighton, and A. Atiya, "Comprehensive review of neural network-based prediction intervals and new advances," IEEE Transactions on Neural Networks, vol. 22, no. 9, pp. 1341-1356, 2011.
33. R. L. Winkler, "A decision-theoretic approach to interval estimation," Journal of the American Statistical Association, vol. 67, no. 337, pp. 187-191, 1972.