Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/31409
Título: | Short-term electric load forecasting using computational intelligence methods |
Autor(es): | Jurado, Sergio Peralta, J. Nebot, Àngela Mugica, Francisco Cortez, Paulo |
Palavras-chave: | Artificial neural networks Evolutionary computation Support vector machines Random forest Time series Forecast |
Data: | Jul-2013 |
Editora: | IEEE |
Revista: | Ieee International Conference on Fuzzy Systems |
Resumo(s): | Accurate time series forecasting is a key issue to support individual and organizational decision making. In this paper, we introduce several methods for short-term electric load forecasting. All the presented methods stem from computational intelligence techniques: Random Forest, Nonlinear Autoregressive Neural Networks, Evolutionary Support Vector Machines and Fuzzy Inductive Reasoning. The performance of the suggested methods is experimentally justified with several experiments carried out, using a set of three time series from electricity consumption in the real-world domain, on different forecasting horizons. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/31409 |
ISBN: | 978-1-4244-6917-8 |
DOI: | 10.1109/FUZZ-IEEE.2013.6622523 |
ISSN: | 1098-7584 |
Versão da editora: | The original publication is available at http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6622523 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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2013-fuzz.pdf | 1,11 MB | Adobe PDF | Ver/Abrir |