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Short-term electric load forecasting using computational intelligence methods

Author
Jurado, S.; Peralta, J.; Nebot, M.; Mugica, F.; Cortez, P.
Type of activity
Presentation of work at congresses
Name of edition
2013 IEEE International Conference in Fuzzy Systems
Date of publication
2013
Presentation's date
2013-07-09
Book of congress proceedings
2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2013)
First page
1
Last page
8
DOI
https://doi.org/10.1109/FUZZ-IEEE.2013.6622523 Open in new window
URL
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6622523&tag=1 Open in new window
Abstract
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...
Keywords
Artificial neural networks, Evolutionary computation, Forecast, Random forest, Support vector machines, Time series
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
SOCO - Soft Computing

Participants