Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/31409

TitleShort-term electric load forecasting using computational intelligence methods
Author(s)Jurado, Sergio
Peralta, J.
Nebot, Àngela
Mugica, Francisco
Cortez, Paulo
KeywordsArtificial neural networks
Evolutionary computation
Support vector machines
Random forest
Time series
Forecast
Issue dateJul-2013
PublisherIEEE
JournalIeee International Conference on Fuzzy Systems
Abstract(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.
TypeConference paper
URIhttp://hdl.handle.net/1822/31409
ISBN978-1-4244-6917-8
DOI10.1109/FUZZ-IEEE.2013.6622523
ISSN1098-7584
Publisher versionThe original publication is available at http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6622523
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

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