Predição dos preços médios mensais de contratos despachados no mercado atacadista de eletricidade na Colômbia usando máquinas de vetores de suporte
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Jun 1, 2010
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Resumo
A predição dos preços da eletricidade nos mercados liberalizados e desregulados tem sido considerada uma tarefa difícil, devido à quantidade e complexidade de fatores que governam os preços. Neste artigo prognosticam-se os preços médios mensais dos contratos despachados no mercado elétrico da Colômbia usando uma inovadora rede neural, conhecida como máquina de vetores de suporte. Comparam-se os prognósticos obtidos com um perceptron multicamada e um modelo ARIMA. Os resultados obtidos mostram que a máquina de vetores de suporte captura de melhor forma a dinâmica intrínseca da série de tempo e é capaz de prognosticar com maior precisão para um horizonte de 12 meses adiante.
Keywords
comparative studies, non-linear series, prediction, electricity prices, neural networksestudos comparativos, séries não lineares, predição, preços de eletricidade, redes neuraisestudios comparativos, series no lineales, predicción, precios de electricidad, redes neuronales
References
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Franses, P. H. and van Djck, D. (2000). Non-linear time series models in empirical finance. Cambridge: Cambridge University Press.
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Tay, F. E. H. and Cao, L. (2001). Application of support vector machines in financial time series forecasting. Omega, 29, 309-317.
Teräsvirta, T. (1994). Specification, estimation, and evaluation of smooth transition autoregressive models. Journal of the American Statistical Association, 89, 208-218.
Lin, C. and Granger, C. (1993). Power of the neural network linearity test. Journal of Time Series Analysis, 14, 209-220.
Thissen, U.; van Brakel, R.; de Weijer, A. P.; Melssen, W. J. and Buydens, L. M. C. (2003). Using vector support machines for time series prediction. Chemometrics and Intelligent Laboratory Systems, 69, 35-49.
Tong, G. (1978). On a threshold model. En: C. Chen (Ed.), Pattern recognition and signal processing. Amsterdam: Sijhoff & Noordhoff.
Vapnik, V. N. (1995). The nature of statistical learning theory. New York: Springer.
Statistical learning theory. (1998). New York: Wiley.
Vapnik, V. N.; Golowich, S. E. and Smola, A. J. (1996). Support vector method for function approximation, regression estimation, and signal processing. Advances in Neural Information Processing Systems, 9, 281-287.
Velásquez, J. D.; Dyner, I. y Souza, R. C. (2007). ¿Por qué es tan difícil obtener buenos pronósticos de los precios de la electricidad en mercados competitivos? Cuadernos de Administración, 20 (34), 259-282.
Zhang, G.; Patuwo, B. and Hu, M. (1998). Forecasting with artificial neural networks: the state of the art. International Journal of Forecasting, 14, 35-62.
Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2, 121-167.
Chen, R. and Tsay, R. (1993). Functional-coefficient autoregressive models. Journal of the American Statistical Association, 88, 298-308.
Franses, P. H. and van Djck, D. (2000). Non-linear time series models in empirical finance. Cambridge: Cambridge University Press.
Gunn, S. R. (1997). Support vector machines for classification and regression (Technical report). Southampton, UK: Image Speech and Intelligent Systems Research Group, University of Southampton.
Kaastra, I. and Boyd, M. (1996). Designing a neural network for forecasting financial and economic series. Neurocomputing, 10, 215-236.
Masters, T. (1993). Practical neural network recipes in C++. New York: Academic Press.
Neural, novel and hybrid algorithms for time series prediction. (1995). New York: John Wiley and Sons.
Mukherjee, S.; Osuna, E. and Girosi, F. (1997). Nonlinear prediction of chaotic time series using support vector machines. Documento presentado en IEEE NNSP'97, Amelia Island, FL, Estados Unidos.
Muller, K. R.; Smola, A.; Ratsch, G.; Scholkopf, B.; Kohlmorgen, J. and Vapnik, V. N. (1997). Predicting time series with support vector machines. En: W. Gerstner, A. Germond, M. Hasler, and J.-D. Nicoud (Eds.), Proceedings of ICANN'97, Springer LNCS 1327, Berlin, 999-1004.
Osowski, S. and Garanty, K. (2007). Forecasting of the daily meteorological pollution using wavelets and support vector machine. Engineering Applications of Artificial Intelligence, 20, 745-755.
Platt, L. (1998). Fast training of SVM using sequential optimization. En: B. Scholkopf, B. Burges y A. Smola (Eds.), Advances in Kernel methods-support vector learning (pp. 185-208). Cambridge: MIT Press.
Scholkopf, B. and Smola, A. (2002). Learning with Kernels. Cambridge, MA: MIT Press.
Tay, F. E. H. and Cao, L. (2001). Application of support vector machines in financial time series forecasting. Omega, 29, 309-317.
Teräsvirta, T. (1994). Specification, estimation, and evaluation of smooth transition autoregressive models. Journal of the American Statistical Association, 89, 208-218.
Lin, C. and Granger, C. (1993). Power of the neural network linearity test. Journal of Time Series Analysis, 14, 209-220.
Thissen, U.; van Brakel, R.; de Weijer, A. P.; Melssen, W. J. and Buydens, L. M. C. (2003). Using vector support machines for time series prediction. Chemometrics and Intelligent Laboratory Systems, 69, 35-49.
Tong, G. (1978). On a threshold model. En: C. Chen (Ed.), Pattern recognition and signal processing. Amsterdam: Sijhoff & Noordhoff.
Vapnik, V. N. (1995). The nature of statistical learning theory. New York: Springer.
Statistical learning theory. (1998). New York: Wiley.
Vapnik, V. N.; Golowich, S. E. and Smola, A. J. (1996). Support vector method for function approximation, regression estimation, and signal processing. Advances in Neural Information Processing Systems, 9, 281-287.
Velásquez, J. D.; Dyner, I. y Souza, R. C. (2007). ¿Por qué es tan difícil obtener buenos pronósticos de los precios de la electricidad en mercados competitivos? Cuadernos de Administración, 20 (34), 259-282.
Zhang, G.; Patuwo, B. and Hu, M. (1998). Forecasting with artificial neural networks: the state of the art. International Journal of Forecasting, 14, 35-62.
Como Citar
Velásquez H., J. D., Franco C., C. J., & Olaya M., Y. (2010). Predição dos preços médios mensais de contratos despachados no mercado atacadista de eletricidade na Colômbia usando máquinas de vetores de suporte. Cuadernos De Administración, 23(40). https://doi.org/10.11144/Javeriana.cao23-40.pppm
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