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A forecasting model for the price of electricity in Colombia using neurofuzzy networks is proposed. Two network structures including the price series in the first and the price series plus the reserve water levels in the latter are used. The results are compared with two neural networks structures and a Generalized Autoregressive Conditional Heteroscedasticity model (GARCH). Historical data were supplied by the Company XM of the ISA Group; data for 120 days were used as for training the network and the following 31 days were used for testing the predictive capabilities of the model. The GARCH model shows better adjustment within the training period for the prices series as input, but the neurofuzzy networks have better forecasting performance for one and for two input variables. [ABSTRACT FROM AUTHOR]

Liberalization and deregulation processes around the world have transformed electrical power industries and provoked a much more complex price behavior. This article analyzes the difficulties that researchers and professionals have encountered when they attempt to predict electricity prices, for the purpose of contributing to identifying the most important obstacles that hinder good forecasting, in particular, those related to market complexity, human limitations in information processing, organizational policies, methodological problems, and limited experiences. The article also poses some useful questions, to define future research on price forecasting. [ABSTRACT FROM AUTHOR]

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