Resumen

VELASQUEZ HENAO, Juan David  y  FRANCO CARDONA, Carlos Jaime. Prediction of the prices of electricity contracts using a neuronal network with dynamic architecture. Innovar [online]. 2010, vol.20, n.36, pp.7-14. ISSN 0121-5051.

Contracts in unregulated electricity markets are a tool to protect the agents from volatility; in this context, price predictions are a key input to enable customers to make strategic and operational decisions. In this article, average prices are predicted for contracts signed in the Colombian electricity market, using a neuronal network with dynamic architecture known as DAN2. The model developed is able to capture the intrinsic dynamic of series of prices and forecast the price for the following month with greater precision than the classic ARIMA methodology, for forecasting horizons of 12 and 24 months.

Palabras clave : Electricity prices; artificial neuronal networks; prediction; time series.

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