Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/1392
Título: A data-mining based methodology for wind forecasting
Autor: Ramos, Sérgio
Soares, João
Vale, Zita
Morais, H.
Palavras-chave: Artificial neural network
Data-mining
Multilayer perceptron
Wind speed forecasting
Data: 2011
Editora: IEEE
Resumo: In many countries the use of renewable energy is increasing due to the introduction of new energy and environmental policies. Thus, the focus on the efficient integration of renewable energy into electric power systems is becoming extremely important. Several European countries have already achieved high penetration of wind based electricity generation and are gradually evolving towards intensive use of this generation technology. The introduction of wind based generation in power systems poses new challenges for the power system operators. This is mainly due to the variability and uncertainty in weather conditions and, consequently, in the wind based generation. In order to deal with this uncertainty and to improve the power system efficiency, adequate wind forecasting tools must be used. This paper proposes a data-mining-based methodology for very short-term wind forecasting, which is suitable to deal with large real databases. The paper includes a case study based on a real database regarding the last three years of wind speed, and results for wind speed forecasting at 5 minutes intervals.
URI: http://hdl.handle.net/10400.22/1392
ISBN: 978-1-4577-0809-1
978-1-4577-0808-4
Versão do Editor: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6082223
Aparece nas colecções:ISEP – GECAD – Comunicações em eventos científicos

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