Forecasting the Price of Energy in Spain’s Electricity Production Market

Resumen

In this job, short-term forecasts are calculated for the Energy Price in the Electricity Production Market of Spain. The methodology used to achieve these forecasts is based on Artificial Neural Networks, which have been used succesfully in recent years in many forecasting applications. To gauge the quality of forecasts, they have been compared with those obtained with the Box-Jenkins ARIMA models, another well-known forecasting methodology. Energy Price time series are usually composed of too many data, which can be a problem if we are looking for a short period of time to reach an adequate forecast. In this job, a training method for Neural Nets is proposed, which is based on making a previous selection for the Multilayer Perceptron (MLP) training samples, using an ART-type neural network. The MLP is then trained and finally used to calculate forecasts. Palabras clave: Electricity Market, Forecasting Time Series, Neural Networks