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Neural networks for engineering systems forecasting

Abstract

Knowing in advance the variables of the electrical power systems as well as other engineering systems is very important. The use of neural networks for problems that are difficult to model becomes an easier task. Recently the use o hybrid models with neural networks and ARIMA of Box&Jenkins brings good results exploring the advantages of one and other. This project intends to develop some software to predict load, stream flow and prices. Load forecasting is going to use ARIMA of Box&Jenkins (for linear part) and ART family neural network (for nonlinear part). The stream flow prediction will use an ART family neural network and the same one for the prices also including the GRNN from MATLAB. The results will be compared from benchmarks of the literature, as well as other results developed by the neural network group. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
SANTOS, FRANCISCO L.; REIS, MARIA T.; FORTES, CONCEICAO J. E. M.; LOTUFO, ANNA D.; NEVES, DIOGO R. C. B.; POSEIRO, P.; MACIEL, GERALDO E. Performance of a Fuzzy ARTMAP Artificial Neural Network in Characterizing the Wave Regime at the Port of Sines (Portugal). Journal of Coastal Research, v. 32, n. 6, p. 1362-1373, NOV 2016. Web of Science Citations: 1.

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