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Short-term electric load forecasting using computational intelligence methods

Autor
Jurado, S.; Peralta, J.; Nebot, M.; Mugica, F.; Cortez, P.
Tipus d'activitat
Presentació treball a congrés
Nom de l'edició
2013 IEEE International Conference in Fuzzy Systems
Any de l'edició
2013
Data de presentació
2013-07-09
Llibre d'actes
2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2013)
Pàgina inicial
1
Pàgina final
8
DOI
https://doi.org/10.1109/FUZZ-IEEE.2013.6622523 Obrir en finestra nova
URL
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6622523&tag=1 Obrir en finestra nova
Resum
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 justi¿ed with several experiments carried out, u...
Paraules clau
Artificial neural networks, Evolutionary computation, Forecast, Random forest, Support vector machines, Time series
Grup de recerca
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
SOCO - Soft Computing

Participants