Dynamically optimizing parameters in support vector regression: An application of electricity load forecasting

Proceedings of the Annual Hawaii International Conference on System Sciences

Volumn 2, Issue , 2006, Pages

  Hsu, Chin Chia ^a,b   Wu, Chih Hung ^c   Chen, Shih Chien ^c   Peng, Kang Lin ^d  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

^b ST JOHN'S UNIVERSITY   (Taiwan)

^c Takming College ^*  (Taiwan)

^d MINGHSIN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

PARAMETER OPTIMIZATION; REAL VALUED GENETIC ALGORITHM (RGA); SUPPORT VECTOR REGRESSION (SVR);

GENETIC ALGORITHMS; MATHEMATICAL MODELS; OPTIMIZATION; PARAMETER ESTIMATION; PROBLEM SOLVING; REAL TIME SYSTEMS; REGRESSION ANALYSIS; VECTORS;

LEARNING SYSTEMS;

EID: 33749605642     PISSN: 15301605     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/HICSS.2006.132     Document Type: Conference Paper

References (32)

1. Adewuya, A. A., New methods in genetic search with real-valued chromosomes. Master's thesis, Cambridge: Massachusetts Institute of Technology, 1996.
2. Campbell, C., "Kernel Methods: A Survey of Current Techniques", Neurocomputing, 2002, Vol. 48, 63-84.
3. Cao, L., "Support vector machines experts for time series forecasting", Neurocomputing, 2003, Vol. 51, 321-339.
4. Cao, Y. J., and Wu, Q. H., "Optimization of control parameters in genetic algorithms: a stochastic approach", International Journal of Systems Science, 1999, May99, 30:5, 551-559.
5. Chen, B. J., Chang, M. W., and Lin, C. J., "Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001", IEEE Transactions on Power Systems, 2004, To appear.
6. Christianini, N., and Shawe-Taylor, J., An Introduction to Support Vector Machines, Cambridge, England: Cambridge University Press, 2000.
7. Clements, M. P., and Galvao, A. B., "A comparison of tests of nonlinear cointegration with application to the predictability of US interest rates using the term structure", International Journal of Forecasting, 2004, 20, 219-236.
8. Cristianini, N., Campell, C., and Taylor, J. S., "Dynamically adapting kernels in Support Vector Machines", In Advances in Neural Information Processing Systems, 1999, 11, 204-210.
9. Duan, K., Keerthi, S. S., and Poo, A. N., "Evaluation of Simple Performance Measures for Tuning SVM Hyperparameters", Neurocomputing, 2003, Vol. 51, 41-59.
10. Fogel, D. B., "An Introduction to Simulated Evolutionary Optimization", IEEE Transactions on Neural Networks, 1994, 5:1, 3-14.
11. Goldberg, D. E. Genetic Algorithms in Search, Optimization & Machine Learning, Addision-Wesley, Reading, MA, 1989.
12. Harvey, A. C., and Koopman, S. J., "Forecasting hourly electricity demand using time-varying splines", Journal of American Statistical Association, 1993, Vol. 88, 1228-1236.
13. Haupt, R. L., and Haupt, S. E., Practical Genetic Algorithms, Wiley Interscience Publication, 1998.
14. Hearst, M. A., Dumais, S. T., Osman, E., Platt, J., and Scholkopf, B., "Support Vector Machines", IEEE Intelligent System, 1998, Vol.13(4), 18-28.
15. Hippert, H. S., and Pedreira, C. E., "Neural Networks for Short-term Load forecasting: A review and Evaluation", IEEE Transcations on Power Systems, 2001, 16:1, 44-55.
16. Huang, Y. P., and Huang, C. H., "Real-valued Genetic Algorithms for Fuzzy Grey Prediction System", Fuzzy Sets and Systems, 1997, 87, 265-276.
17. Kim, K., "Financial time series forecasting using support vector machines", Neurocomputing, 2003, 55:1, 307-319.
18. Kulkarni, A., Jayaraman, V. K., and Kulkarni, B. D., "Control of chaotic dynamical systems using support vector machines", Physics Letters A, 2003, 317:5, 429-435.
19. Mattera, D., and Haykin, S., "Support vector machines for dynamic reconstruction of a chaotic system", In B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods - Support Vector Learning, Cambridge, MA, MIT Press, 1999, 211-242.
20. McCall J., and Petrovski, A. "A Decision Support System for Cancer Chemotherapy Using Genetic Algorithms," Proceedings of the International Conference on Computational Intelligence for Modeling, Control and Automation, 1999, 65-70.
21. Min, J. H., and Lee, Y. C., "Bankruptcy Prediction Using Support Vector Machine with Optimal Choice of Kernel Function Parameters", Expert Systems with Applications, 2005, 28, 603-614.
22. Mukherjee, S., Osuna, E., and Girosi, F., "Nonlinear Prediction of Chaotic Time Series using a Support Vector Machine", Proceedings NNSP'97, 1997.
23. Müller, K.-R., Smola, A., Rätsch, G., Schölkopf, B., Kohlmorgen, J., and Vapnik, V., "Predicting Time Series with Support Vector Machines", Proceedings ICANN'97, 1997, 999.
24. Pelckmans, K., Suykens, J. A. K., Van Gestel, T., De Brabanter, J., Lukas, L., Hamers, B., De Moor, B., and Vandewalle, J., LS-SVMlab Toolbox User's Guide version 1.4, November, 2002.
25. Ramanathan, R., Engle, R., Granger, C. W. J., VahidAraghi, F., and Brace, C. "Short-run forecast of electricity loads and peaks", International Journal of Forecasting, 1997,13:2, 161-174. Software available at http://www.esat.kuleuven.ac.be/sista/lssvmlab/
26. Suykens, J. A. K., Van Gestel, T., De Brabanter, J., De Moor, B., and Vandewalle, J., Least Squares Support Vector Machines, World Scientific, 2002.
27. Tay, Francis E. H., and Cao, L., "Application of support vector machines in financial time series forecasting", Omega, 2001, 29:4, 309-317.
28. Taylor, J. W., and Buizza, R., "Using weather ensemble predictions in electricity demand forecasting", International Journal of Forecasting, 2003, Vol.19, 57-70.
29. Wright, M. http://www.clopinet.com/isabelle/Projects/SVM/applist.html, 2003.
30. http://neuron.tuke.sk/competition/reports/BerthaGuijarro.pdf
31. http://neuron.tuke.sk/competition/reports/DaliborZivcak.pdf
32. http://neuron.tuke.sk/competition/reports/DavidEsp.pdf