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Development of a model for short-term load forecasting with neural networks and its application to the electrical Spanish market

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5 Author(s)
M. López ; Universidad Miguel Hernández de Elche (UMH) ; S. Valero ; C. Senabre ; J. Aparicio
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The study presented in this paper used Kohonen's Self-Organized Maps, which is one of the more uncommon techniques based on neural networks in load forecasting. The aim of this study is not only to show that this technique is capable of producing accurate short-term load forecasting results which should not be neglected, but also to provide a deep and thorough analysis of these results in order to extract solid conclusions about the inner design of the network, the selection of variables and also about the training periods. In addition, an application for the Spanish electricity market is developed.

Published in:

2011 8th International Conference on the European Energy Market (EEM)

Date of Conference:

25-27 May 2011