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Fifth International Conference on Hybrid Intelligent Systems (HIS'05)
Application of Data Mining Techniques as a Complement to Natural Inflow Uni-variable Stochastic Forecasting - A Case Study : The Igua?u River Basin
Rio de Janeiro, Brazil
December 06-December 09
ISBN: 0-7695-2457-5
ASCII Text
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Marcio Cataldi, Luiz Guilherme, Ferreira Guilhon, Carla da C. Lopes Achao, "Application of Data Mining Techniques as a Complement to Natural Inflow Uni-variable Stochastic Forecasting - A Case Study : The Igua?u River Basin," Hybrid Intelligent Systems, International Conference on, pp. 9-16, Fifth International Conference on Hybrid Intelligent Systems (HIS'05), 2005.
 
 
BibTex
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@article{ 10.1109/ICHIS.2005.24,
author = {Marcio Cataldi and Luiz Guilherme and Ferreira Guilhon and Carla da C. Lopes Achao},
title = {Application of Data Mining Techniques as a Complement to Natural Inflow Uni-variable Stochastic Forecasting - A Case Study : The Igua?u River Basin},
journal ={Hybrid Intelligent Systems, International Conference on},
volume = {0},
year = {2005},
isbn = {0-7695-2457-5},
pages = {9-16},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICHIS.2005.24},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Hybrid Intelligent Systems, International Conference on
TI - Application of Data Mining Techniques as a Complement to Natural Inflow Uni-variable Stochastic Forecasting - A Case Study : The Igua?u River Basin
SN - 0-7695-2457-5
SP9
EP16
A1 - Marcio Cataldi,
A1 - Luiz Guilherme,
A1 - Ferreira Guilhon,
A1 - Carla da C. Lopes Achao,
PY - 2005
KW - Data Mining; Bayesian Networks; Stochastic Models; Inflow Forecasts
VL - 0
JA - Hybrid Intelligent Systems, International Conference on
ER -
 
Marcio Cataldi, Operador Nacional do Sistema El?trico (ONS), Brazil
Luiz Guilherme, Operador Nacional do Sistema El?trico (ONS), Brazil
Ferreira Guilhon, Operador Nacional do Sistema El?trico (ONS), Brazil
Carla da C. Lopes Achao, Planning Engenharia e Consultoria, Brazil
This paper presents the results obtained from the utilization of a public dominion software that, through Data Mining and Neural Networks with Bayesian training is capable of laying the foundation for the selection of the most appropriate natural in- flow forecast used in the PREVIVAZ stochastic modeling system. This technique utilizes precipitation information, forecasted and observed, a well as verified natural inflow data recorded over the weeks that precede the actual forecast target made at the water courses at the Foz do Areia and Jord?o hydroelectric plants located in the Igua?u River Basin. The results obtained indicate that the usage of these tools can provide a simple and efficient solution to reduce natural inflow forecast errors on a weekly forecast basis for the Igua?u River Basin
Index Terms:
Data Mining; Bayesian Networks; Stochastic Models; Inflow Forecasts
Citation:
Marcio Cataldi, Luiz Guilherme, Ferreira Guilhon, Carla da C. Lopes Achao, "Application of Data Mining Techniques as a Complement to Natural Inflow Uni-variable Stochastic Forecasting - A Case Study : The Igua?u River Basin," his, pp.9-16, Fifth International Conference on Hybrid Intelligent Systems (HIS'05), 2005
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