Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/5930
Título: Short-term Wind Speed Forecasting using Support Vector Machines
Autor: Pinto, Tiago
Ramos, Sérgio
Sousa, Tiago
Vale, Zita
Palavras-chave: Artificial neural networks
Short-term forecasting
Support vector machines
Wind speed forecasting
Data: Dez-2014
Editora: IEEE
Relatório da Série N.º: CIDUE;2014
Resumo: Wind speed forecasting has been becoming an important field of research to support the electricity industry mainly due to the increasing use of distributed energy sources, largely based on renewable sources. This type of electricity generation is highly dependent on the weather conditions variability, particularly the variability of the wind speed. Therefore, accurate wind power forecasting models are required to the operation and planning of wind plants and power systems. A Support Vector Machines (SVM) model for short-term wind speed is proposed and its performance is evaluated and compared with several artificial neural network (ANN) based approaches. A case study based on a real database regarding 3 years for predicting wind speed at 5 minutes intervals is presented.
URI: http://hdl.handle.net/10400.22/5930
DOI: 10.1109/CIDUE.2014.7007865
Versão do Editor: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=7007865&queryText%3D10.1109%2FCIDUE.2014.7007865
Aparece nas colecções:ISEP – GECAD – Comunicações em eventos científicos

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