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Short-term demand and energy price forecasting

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2 Author(s)
Contreras, J. ; ETS de Ingenieros Industriales, Castilla Univ. ; Santos, J.R.

This paper is devoted to describe several forecasting techniques to predict market prices and demands in day-ahead electric energy markets. Price forecasting is performed using time series procedures, such as ARIMA, dynamic regression and transfer function methodologies. Demand forecasting is performed using time series procedures, artificial intelligence and combinations of several methods. Relevant conclusions are drawn on the effectiveness and flexibility of the considered techniques

Published in:

Electrotechnical Conference, 2006. MELECON 2006. IEEE Mediterranean

Date of Conference:

16-19 May 2006