Prediction Interval Estimation of Electricity Prices Using PSO-Tuned Support Vector Machines

IEEE Transactions on Industrial Informatics

Volumn 11, Issue 2, 2015, Pages 322-331

  Shrivastava, Nitin Anand ^a   Khosravi, Abbas ^b   Panigrahi, Bijaya Ketan ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY DELHI   (India)

^b DEAKIN UNIVERSITY   (Australia)

Author keywords

Electricity market; Particle swarm optimization; Prediction interval; Support vector machines; Uncertainty

Indexed keywords

COMMERCE; COSTS; ELECTRIC INDUSTRY; FORECASTING; POWER MARKETS; STATISTICAL MECHANICS; SUPPORT VECTOR MACHINES; UNCERTAINTY ANALYSIS;

COMPUTATIONAL BURDEN; ELECTRICITY MARKET; LOWER AND UPPER BOUNDS; NEURAL NETWORKS (NNS); OBJECTIVE FUNCTIONS; PREDICTION INTERVAL; TRADITIONAL APPROACHES; UNCERTAINTY;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 84926435343     PISSN: 15513203     EISSN: None     Source Type: Journal    
DOI: 10.1109/TII.2015.2389625     Document Type: Article

References (31)

1. A. Khosravi and S. Nahavandi, "Load forecasting using interval type-2 fuzzy logic systems: Optimal type reduction," IEEE Trans. Ind. Informat., vol. 10, no. 2, pp. 1055-1063, May 2014.
2. C. Borges, Y. Penya, and I. Fernandez, "Evaluating combined load forecasting in large power systems and smart grids," IEEE Trans. Ind. Informat., vol. 9, no. 3, pp. 1570-1577, Aug. 2013.
3. M. Zhou, Z. Yan, Y. X. Ni, G. Li, and Y. Nie, "Electricity price forecasting with confidence-interval estimation through an extended arima approach," Proc. Gener. Transmiss. Distrib., vol. 153, no. 2, pp. 187-195, 2006.
4. S. K. Aggarwal, L. M. Saini, and A. Kumar, "Electricity price forecasting in deregulated markets: A review and evaluation," Elect. Power Energy Syst., vol. 31, no. 1, pp. 13-22, 2009.
5. A. Khosravi, S. Nahavandi, and D. Creighton, "A neural network-garch-based method for construction of prediction intervals," Elect. Power Syst. Res., vol. 96, pp. 185-193, 2013.
6. A. Ku, "Forecasting to understand uncertainty in electricity prices," Platts Energy Bus. Technol., vol. 1, no. 1, pp. 59-64, May/Jun. 2002.
7. A. Khosravi, S. Nahavandi, and D. Creighton, "Quantifying uncertainties of neural network-based electricity price forecasts," Appl. Energy, vol. 112, pp. 120-129, 2013.
8. T. Heskes, "Practical confidence and prediction intervals," in Advances in Neural Information Processing Systems 9. Cambridge, MA, USA: MIT Press, 1997, pp. 176-182.
9. C. M. Bishop, Neural Networks for Pattern Recognition. New York, NY, USA: Oxford Univ. Press, 1995.
10. J. T. G. Hwang and A. A. Ding, "Prediction intervals for artificial neural networks," J. Amer. Stat. Assoc., vol. 92, no. 438, pp. 748-757, 1997.
11. 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., 1994, vol. 1, pp. 55-60.
12. A. Khosravi, S. Nahavandi, D. Creighton, and A. Atiya, "Lower upper bound estimation method for construction of neural network-based prediction intervals," IEEE Trans. Neural Netw., vol. 22, no. 3, pp. 337-346, Mar. 2011.
13. C. J. C. Burges, "A tutorial on support vector machines for pattern recognition," Data Min. Knowl. Discovery, vol. 2, pp. 121-167, 1998.
14. J. Carney, P. Cunningham, and U. Bhagwan, "Confidence and prediction intervals for neural network ensembles," in Proc. Int. Joint Conf. Neural Netw., Washington, DC, USA, Jul. 1999, vol. 2, pp. 1215-1218.
15. R. Dybowski and S. J. Roberts, "Confidence intervals and prediction intervals for feed-forward neural networks," in Clinical Applications of Artificial Neural Networks. Cambridge, U.K.: Cambridge Univ. Press, 2001, pp. 298-326.
16. L. Zhang, P. Luh, and K. Kasiviswanathan, "Energy clearing price prediction and confidence interval estimation with cascaded neural networks,"IEEE Trans. Power Syst., vol. 18, no. 1, pp. 99-105, Feb. 2003.
17. L. Zhang and P. Luh, "Neural network-based market clearing price prediction and confidence interval estimation with an improved extended kalman filter method," IEEE Trans. Power Syst., vol. 20, no. 1, pp. 59-66, Feb. 2005.
18. K. De Brabanter, J. De Brabanter, J. A. K. Suykens, and B. De Moor, "Approximate confidence and prediction intervals for least squares support vector regression," IEEE Trans. Neural Netw., vol. 22, no. 1, pp. 110-120, Jan. 2011.
19. Q. Cheng, J. Tezcan, and J. Cheng, "Confidence and prediction intervals for semiparametric mixed-effect least squares support vector machine,"Pattern Recognit. Lett., vol. 40, pp. 88-95, 2014.
20. J. Zhao, Z. Dong, Z. Xu, and K.Wong, "A statistical approach for interval forecasting of the electricity price," IEEE Trans. Power Syst., vol. 23, no. 2, pp. 267-276, May 2008.
21. C. Cortes and V. Vapnik. (1995). Support-vector networks. Mach. Learn. [Online]. vol. 20, no. 3, pp. 273-297. Available: http://dx.doi.org/10.1007/BF00994018
22. H. Drucker, C. J. C. Burges, L. Kaufman, A. Smola, and V. Vapnik, "Support vector regression machines," in Advances in Neural Information Process Systems, M. C. Mozer, M. I. Jordan, T. Petsche, Eds. Cambridge, MA, USA: MIT Press, 1996, pp. 155-161.
23. L. Saini, S. Aggarwal, and A. Kumar, "Parameter optimisation using genetic algorithm for support vector machine-based price-forecasting model in national electricity market," IET Gener. Transmiss. Distrib., vol. 4, no. 1, pp. 36-49, Jan. 2010.
24. C.-C. Chang and C.-J. Lin, "LIBSVM: A library for support vector machines," ACM Trans. Intell. Syst. Technol., vol. 2, pp. 27:1-27:27, 2011.
25. J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proc. IEEE Int. Conf. Neural Netw., 1995, vol. 4, pp. 1942-1948.
26. A. Khosravi, S. Nahavandi, D. Creighton, and A. Atiya, "Comprehensive review of neural network-based prediction intervals and new advances,"IEEE Trans. Neural Netw., vol. 22, no. 9, pp. 1341-1356, Sep. 2011.
27. K. P. Bennett and C. Campbell, "Support vector machines: Hype or hallelujah?" SIGKDD Explor. Newslett., vol. 2, no. 2, pp. 1-13, Dec. 2000.
28. C.-C. Chang and C.-J. Lin. (2001). A practical guide to support vector classification [Online]. Available: http://www.csie.ntu.edu.tw/~cjlin/libsvm
29. C.-L. Huang and C.-J. Wang, "A ga-based feature selection and parameters optimization for support vectormachines," Expert Syst. Appl., vol. 31, no. 2, pp. 231-240, 2006.
30. G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, "Extreme learning machine: Theory and applications," Neurocomputing, vol. 70, pp. 489-501, 2006.
31. R. Koenker and K. F. Hallock, "Quantile regression," J. Econ. Perspect., vol. 15, no. 4, pp. 143-156, 2001.