Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/1444
Título: Short-term wind forecasting for energy resources scheduling
Autor: Ramos, Sérgio
Soares, João
Silva, Marco
Vale, Zita
Palavras-chave: Artificial neural network
Wind power and speed forecasting
Short - term forecasting
Short - term scheduling
Data: 2012
Editora: EWEA
Resumo: This paper proposes a wind power forecasting methodology based on two methods: direct wind power forecasting and wind speed forecasting in the first phase followed by wind power forecasting using turbines characteristics and the aforementioned wind speed forecast. The proposed forecasting methodology aims to support the operation in the scope of the intraday resources scheduling model, namely with a time horizon of 5 minutes. This intraday model supports distribution network operators in the short-term scheduling problem, in the smart grid context. A case study using a real database of 12 months recorded from a Portuguese wind power farm was used. The results show that the straightforward methodology can be applied in the intraday model with high wind speed and wind power accuracy. The wind power forecast direct method shows better performance than wind power forecast using turbine characteristics and wind speed forecast obtained in first phase.
URI: http://hdl.handle.net/10400.22/1444
Versão do Editor: http://proceedings.ewea.org/annual2012/allfiles2/1143_EWEA2012presentation.pdf
Aparece nas colecções:ISEP – GECAD – Comunicações em eventos científicos

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