A NON-LINEAR MODEL FOR FORECASTING THE MONTHLY DEMAND FOR ELECTRICITY IN COLOMBIA (Article published in Spanish)

Authors

  • Juan David Velásquez H. Doctor en Ingeniería- Sistemas Energéticos, Universidad Nacional de Colombia, sede Medellín, Colombia. Profesor asociado, Escuela de Sistemas, Facultad de Minas, Universidad Nacional de Colombia, Colombia.
  • Carlos Jaime Franco G. Doctor en Ingeniería- Recursos Hidráulicos, Universidad Nacional de Colombia, sede Medellín, Colombia. Profesor asociado, Escuela de Sistemas, Facultad de Minas, Universidad Nacional de Colombia, Colombia.
  • Hernán Alonso García Estudiante, Ingeniaría de Sistemas, Universidad Nacional de Colombia, sede Medellín, Colombia.

DOI:

https://doi.org/10.1016/S0123-5923(09)70079-8

Keywords:

Demand, forecast, neural networks, ARIMA

Abstract

This article provides a comparison of the performance of an ARIMA model, a multilayer perceptron, and an autoregressive neural network for forecasting the monthly demand for electricity in Colombia for the following month. The available data were divided into two different sets, i.e. one set for estimating the model parameters, and the other for evaluating the forecast ability outside the range of the sample calibration data. The results show that the autoregressive neural network is able to forecast the demand more accurately than the other two models when the total available data are considered.

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References

Abdel-Aal, R. y Al-Garni, A. (1997). Forecasting monthly electric energy consumption in eastern Saudi Arabia using univariate time series analysis. Energy, 22(11), 1059–1069.

Abdel-Aal, R., Al-Garni, A. y Al-Nassar, Y. (1997). Modelling and forecasting monthly electric energy consumption in eastern Saudi Arabia using abductive networks. Energy, 22(9), 911–921.

Al-Saba, T. y El-Amin, I. (1999). Artificial neural networks as applied to long-term demand forecasting. Artificial Intelligence in Engineering, 13(2), 189–197.

Barrientos, A.F., Olaya, J. y González, V.M. (2007). Un modelo spline para el pronóstico de la demanda de energía eléctrica. Revista Colombiana de Estadística, 30(2), 187-202.

Beenstock, M., Goldin, E. y Nabot, D. (1999). The demand for electricity in Israel. Energy Economics, 21(2), 168–183.

Benavente, J., Galetovic, A., Sanhueza, R. y Serra, P. (2005). Estimando la demanda residencial por electricidad en Chile: El consumo es sensible al precio. Cuadernos de Economía, 42, 31–61.

Box, G.E.P. y Jenkins, G.M. (1976). Time Series Analysis: Forecasting and Control. San Francisco, CA: Prentice Hall.

Castaño, E. (2008). Reconstrucción de datos de series de tiempo: una aplicación a la demanda horaria de electricidad. Revista Colombiana de Estadística, 30(2), 247–263.

Chaveza, S.G., Bernata, J.X. y Coallab, H.L. (1999). Forecasting of energy production and consumption in Asturias (northern Spain). Energy, 24(3), 183–198.

Clements, M.P., Franses, P.H. y Swanson, N.R. (2004). Forecasting economic and financial time-series with non-linear models. International Journal of Forecasting, 20(2), 169-183.

Conejo, A.J., Contreras, J., Espínola, R. y Plazas, M.A. (2005). Forecasting electricity prices for a day-ahead pool-based electric energy market. International Journal of Forecasting, 21(3), 4 -35 462.

Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics of Control: Signals and Systems, 2, 202–314.

Ediger, V. y Tatlidil, H. (2002). Forecasting the primary energy demand in turkey and analysis of cyclic patterns. Energy Conversion and Management, 43(4), 473–487.

Egelioglu, F., Mohamad, A. y Guven, H. (2001). Economic variables and electricity consumption in northern Cyprus. Energy, 26(4), 355–362.

Franco, C.J., Velásquez, J.D. y Olaya, Y. (2008). Caracterización de la demanda mensual de electricidad en Colombia usando un modelo de componentes no observables. Cuadernos de Administración, 21 36, 221-235

Funahashi, K. (1989). On the approximate realization of continuous mappings by neural networks. Neural Neworks, 2, 183–192.

Ghiassi, M., Saidane, H. y Zimbra, D.K. (2005). A dynamic artificial neural network model for forecasting time series events. International Journal of Forecasting, 21, 341-362.

Harris, J. y Liu, L.M. (1993). Dynamic structural analysis and forecasting of residential electricity consumption. International Journal of Forecasting, 9(4), 437–455.

Harvey, A. (1989). Forecasting, structural time series models and the Kalman filter. Cambridge, MA: Cambridge University Press.

Heravi, C., Osborn, D.R. y Birchenhall, C.R. (2004). Linear versus neural network forecasts for European industrial production series. International Journal of Forecasting, 20(3), 435-446.

Hornik, K., Stinchcombe, M. y White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2, 359–366.

Kaastra, I. y Boyd, M. (1996). Designing a neural network for forecasting financial and economic series. Neurocomputing, 10, 215–236.

Lee, T.H., White, H. y Granger, C.W.J. (1993). Testing for neglected nonlinearity in time series models. Journal of Econometrics, 56, 269-290.

Masters, T. (1993). Practical Neural Network Recipes in C++ (1ra. ed.). San Diego, CA: Academic Press Professional.

Masters, T. (1995). Neural, Novel and Hybrid Algorithms for Time Series Prediction (1ra ed.). New York, NY: John Wiley and Sons.

Medina, S. y García, J. (2005). Predicción de demanda de energía en Colombia mediante un sistema de inferencia difuso neuronal. Revista Energética, 33, 15–24.

Mirasgedis, S., Sarafidis, Y., Georgopoulou, E., Lalas, D., Moschovits, M., Karagiannis, F. y Papakonstantinou, D. (2006). Models for mid-term electricity demand forecasting incorporating weath er influences. Energy, 31(2–3), 208–227.

Mohamed, Z. y Bodger, P. (2005). Forecasting electricity consumption in New Zealand using economic and demographic variables. Energy, 30(10), 1833–1843.

Murillo, J., Trejos, A. y Carvajal, P. (2003). Estudio del pronóstico de la demanda de energía eléctrica utilizando modelos de series de tiempo. Scientia et Technica, 23, 37–42.

Murray, F. y Ringwood, J. (1994). Improvement of electricity consumption forecasts using temperature inputs. Simulation Practice and Theory, 2(2), 121–139.

Nasr, G., Badr, E. y Dibeh, G. (2000). Econometric modelling of electricity consumption in post-war Lebanon. Energy Economics, 22(6), 627–640.

Nasr, G., Badr, E. y Joun, C. (2003). Backpropagation neural networks for modelling gasoline consumption. Energy Conversion and Management, 44, 893–905.

ONeill, B. y Desai, M. (2005). Accuracy of past projections of us energy consumption. Energy Policy, 33(8), 979–993.

Saab, S., Badr, E. y Nasr, G. (2001). Univariate modelling and forecasting of energy consumption: the case of electricity in Lebanon. Energy, 26(1), 1–14.

Sarle, W. (1994). Neural networks and statistical models. Documento presentado en The Nineteenth Annual SAS Users Group International Conference, Cary, NC, Estados Unidos.

Steiner, F. (2000). Regulation, industry structure and performance in the electricity supply industry. OECD Economics Department Working Papers, 238.

Stoft, S. (2002). Power System Economics. New York, NY; Wiley-Interscience.

Swanson, N. y White, H. (1997a). Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models. International Journal of Forecasting, 13 (4), 439-461.

Swanson, N. y White, H. (1997b). A model selection approach to real time macroeconomic forecasting using linear models and artificial neural networks. The Review of Economics and Statistics, 79 -4 , 540-550.

Teräsvirta, T., Lin, C.F. y Granger, C.W.J. (1993). Power of the neural network linearity test. Journal of Time Series Analysis, 14, 209-220.

Teräsvirta, T., van Dijk, D. y Medeiros, M. C. (2005). Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination. Interna tio nal Journal of Forecasting, 21(4), 755-774.

Tserkezos, E. (1992). Forecasting residential electricity consumption in Greece using monthly and quarterly data. Energy Economics, 14(3), 226–232.

Unidad de Planeación Minero-Energética – UPME. (2004). Plan de Expansión Preliminar 2004–2018. Bogotá, Colombia: Autor.

White, H. (1989). An additional hidden unit test for neglected nonlinearity in multilayer feedforward networks. Proceedings of the International Joint Conference on Neural Networks, 2, 451-455.

Zhang, G., Patuwo, B. y Hu, M. (1998). Forecasting with artificial neural networks: the state of the art. International Journal of Forecasting, 14, 35–62.

Zhang, G. y Qi, M. (2005). Neural network forecasting for seasonal and trend time series. European Journal of Operational Research, 160(2), 501-514.

Published

2009-09-30

Issue

Section

Research articles

How to Cite

A NON-LINEAR MODEL FOR FORECASTING THE MONTHLY DEMAND FOR ELECTRICITY IN COLOMBIA (Article published in Spanish). (2009). Estudios Gerenciales, 25(112), 37-54. https://doi.org/10.1016/S0123-5923(09)70079-8