Short-term electricity prices forecasting based on support vector regression and Auto-regressive integrated moving average modeling

Energy Conversion and Management

Volumn 51, Issue 10, 2010, Pages 1911-1917

  Che, Jinxing ^a   Wang, Jianzhou ^a  

^a Lan Zhou University   (China)

Author keywords

ARIMA; Artificial neural networks; Competitive market; Price forecasting; Support vector regression

Indexed keywords

ARIMA MODELS; ARTIFICIAL NEURAL NETWORK; AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE; BIDDING STRATEGY; BOUNDARY VALUES; COMPETITIVE MARKETS; ELECTRICITY PRICES; HYBRID MODEL; LINEAR MODELING; LOSS FUNCTIONS; MEAN ABSOLUTE PERCENTAGE ERROR; NONLINEAR MODELING; NONLINEAR PATTERN; POWER PRODUCER; PREDICTION TOOLS; REGRESSION ESTIMATION; ROOT MEAN SQUARE ERRORS; SHORT-TERM ELECTRICITY PRICES FORECASTING; SUPPORT VECTOR REGRESSIONS;

COSTS; ELECTRIC LOAD FORECASTING; MATHEMATICAL MODELS; NONLINEAR ANALYSIS; REGRESSION ANALYSIS; SIGNAL ENCODING; TIME SERIES ANALYSIS; ULTRAHIGH MOLECULAR WEIGHT POLYETHYLENES; VECTORS;

NEURAL NETWORKS;

EID: 77955322938     PISSN: 01968904     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.enconman.2010.02.023     Document Type: Article

References (33)

1. H. Geman, and A. Roncoroni Understanding the fine structure of electricity prices J Bus 79 2006 1225 1261
2. G. Swinand, C. Rufin, and C. Sharma Valuing assets using real options: an application to deregulated electricity markets J Appl Corp Finan 17 2005 55 67
3. Jesús Crespo Cuaresma, Jaroslava Hlouskova, Stephan Kossmeier, and Michael Obersteiner Forecasting electricity spot-prices using linear univariate time-series models Appl Energy 77 2004 87 106
4. Ivars Peterson Bach to chaos: chaotic variations on a classical theme Sci News December 24 1994 428
5. Raquel Gareta, Luis M. Romeo, and Antonia Gil Forecasting of electricity prices with neural networks Int J Energy Convers Manage 47 2006 1770 1778
6. J.P.S. Catalao, S.J.P.S. Mariano, V.M.F. Mendes, and L.A.F.M. Ferreira Short-term electricity prices forecasting in a competitive market: a neural network approach Electr Power Syst Res 77 2007 1297 1304
7. Raúl Pino, José Parreno, Alberto Gomez, and Paolo Priore Forecasting next-day price of electricity in the Spanish energy market using artificial neural networks Eng Appl Artif Intel 21 2008 53 62
8. V. Vahidinasab, S. Jadid, and A. Kazemi Day-ahead price forecasting in restructured power systems using artificial neural networks Electr Power Syst Res 78 2008 1332 1342
9. M.T. Hagan, and M.B. Menhaj Training feedforward networks with the Marquardt algorithm IEEE Trans Neural Networ 5 6 1994 989 993
10. N. Amjady Day-ahead price forecasting of electricity markets by a new fuzzy neural network IEEE Trans Power Syst 21 2006 887 896
11. C.I. Kim, I.K. Yu, Y.H. Song, and Kohonen Neural network and wavelet transform based approach to short-term load forecasting Electr Power Syst Res 63 2002 169 176
12. Nima Amjady, and Farshid Keynia Day ahead price forecasting of electricity markets by a mixed data model and hybrid forecast method Int J Electr Power Energy Syst 30 2008 533 546
13. Lapedes R, Farber R. Nonlinear signal processing using neural networks: prediction and system modeling. Technical report LA-VR87-2662, Los Alamos National Laboratory, Los Alamos, New Mexico, 1987.
14. Robert J. Van Eyden The application of neural networks in the forecasting of share prices 1996 Finance and Technology Publishing
15. Ping-Feng Pai, and Chih-Sheng Lin A hybrid ARIMA and support vector machines model in stock price forecasting Omega 33 2005 497 505
16. J.W. Dong, and J.G. Huang Study of the parameters used in calculating correlative dimension based on G-P algorithm Appl Res Comput 21 2004 01 0048
17. Vera Kurkova Kolmogorov's theorem and multilayer neural networks 1992 Elsevier Science Ltd. Oxford, UK p. 501-6
18. J. Stark, D.S. Broomhead, M.E. Davies, and J. Huke Takens embedding theorems for forced and stochastic systems Nonlinear Anal 30 December 1997 5303 5314
19. Boser BE, Guyon IM, Vapnik VN. A training algorithm for optimal margin classifiers. In: ACMCOLT'92, Pittsburgh, PA, 1992, p. 144-52.
20. A.J. Smola, and B. Scholkopf A tutorial on support vector regression Statistics and computing vol. 14 2004 Kluwer Academic Pub. 199 222
21. C.J.C. Burges A tutorial on support vector machines for pattern recognition Journal of Data Mining and Knowledge Discovery 2 2 1998
22. N. Cristianini, and J. Shawe-Taylor An introduction to support vector machines and other kernel-based learning methods 1999 Cambridge University Press New York, NY
23. V. Vapnik The nature of statistical learning theory 1995 Springer NY
24. Bermolen P, Rossi D. Support vector regression for link load prediction. Comput Netw 2008; doi:10.1016/j.comnet.2008.09.018.
25. M. Aizerman, E. Braverman, and L. Rozonoer Theoretical foundations of the potential function method in pattern recognition learning Automat Rem Contr 25 1964 821 837
26. G.E.P. Box, and G. Jenkins Time series analysis, forecasting and control 1970 Holden-Day San Francisco, CA
27. H. Akaike A new look at the statistical model identification IEEE Trans Automat Contr 19 6 1974 716 723
28. About SAS. < http://www.sas.com/ >.
29. A.J. Conejo, J. Contreras, R. Espínola, and M.A. Plazas Forecasting electricity prices for a day-ahead pool-based electric energy market Int J Forecast 21 3 2005 435 462 (Pubitemid 40935945)
30. C.P. Rodriguez, and G.J. Anders Energy price forecasting in the Ontario competitive power system market IEEE Trans Power Syst 19 1 2004 366 374
31. California electricity market data. < http://www.ucei.berkeley.edu/ datamine/px-mcp.html >.
32. The description and download of Matlab 7.1. < http://www.mathworks. com/ >.
33. C.W.J. Granger Combining forecasts - twenty years later Int J Forecast 8 1989 167 173