MODELADO DEL PRECIO SPOT DE LA ELECTRICIDAD EN BRASIL USANDO UNA RED NEURONAL AUTORREGRESIVA ELECTRICITY SPOT PRICE MODELLING IN BRASIL USING AN AUTOREGRESSIVE NEURAL NETWORK

An autoregressive neural network model is estimated for the monthly Brazilian electricity spot price, which describes the prices dynamics better than a linear autoregressive model and a classical multilayer perceptron using the same input and neurons in the hidden layer. The proposed model is specified using a statistical procedure based on a likelihood ratio test. The model passes a battery of diagnostic tests. The proposed specification procedure allows us to select the number of units in hidden layer and the inputs to the neural network based on statistical tests, taking into account the number of data and the model fitting to the price time series. The final model specification demonstrates that the price for the next month is a nonlinear function of the current price, the current energy inflow, and the energy saved in the equivalent reservoir in the current month and two months ago.

[1]  Norman R. Swanson,et al.  A Model Selection Approach to Real-Time Macroeconomic Forecasting Using Linear Models and Artificial Neural Networks , 1997, Review of Economics and Statistics.

[2]  Clive W. J. Granger,et al.  Testing for neglected nonlinearity in time series models: A comparison of neural network methods and alternative tests , 1993 .

[3]  Alexandar Angelus,et al.  Electricity Price Forecasting in Deregulated Markets , 2001 .

[4]  J. van Leeuwen,et al.  Neural Networks: Tricks of the Trade , 2002, Lecture Notes in Computer Science.

[5]  Simon Cowan,et al.  Regulatory Reform: Economic Analysis and British Experience , 1994 .

[6]  Anil K. Bera,et al.  A test for normality of observations and regression residuals , 1987 .

[7]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[8]  Ying-Yi Hong,et al.  A neuro-fuzzy price forecasting approach in deregulated electricity markets , 2005 .

[9]  Faye Steiner.  Regulation, Industry Structure, and Performance in the Electricity Supply Industry , 2000 .

[10]  D. Pilipović,et al.  Energy Risk: Valuing and Managing Energy Derivatives , 1997 .

[11]  H. White,et al.  An additional hidden unit test for neglected nonlinearity in multilayer feedforward networks , 1989, International 1989 Joint Conference on Neural Networks.

[12]  Chung-Ming Kuan,et al.  Forecasting exchange rates using feedforward and recurrent neural networks , 1992 .

[13]  J. Contreras,et al.  Forecasting electricity prices for a day-ahead pool-based electric energy market , 2005 .

[14]  Saeed Heravi,et al.  Linear versus neural network forecasts for European industrial production series , 2004 .

[15]  Z. Dong,et al.  Electricity market price spike forecast with data mining techniques , 2005 .

[16]  R. Engle.  Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[17]  Norman R. Swanson,et al.  Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models , 1997 .

[18]  Timothy Masters,et al.  Practical neural network recipes in C , 1993 .

[19]  Timo Teräsvirta,et al.  POWER OF THE NEURAL NETWORK LINEARITY TEST , 1993 .