An Empirical Analysis of MLP Neural Networks Applied to Streamflow Forecasting

Author

de Lima, D.B. ; Lima, M.D.C.E. ; Saigado, R.M.

Author_Institution

Univ. Fed. de Alfenas (UNIFAL-MG), Alfenas, Brazil

Volume

9

Issue

3

fYear

2011

fDate

6/1/2011 12:00:00 AM

Firstpage

295

Lastpage

301

Abstract

Nowadays, in Brazil there is a large energy potential that comes from hydro mineral sources, which most part of the electricity consumed comes from this source. According to this, it is important emphasize that the decision-making related with planning of the operation of the reservoirs of hydroelectric plants has been done based mainly on preview knowledge of the flow. Thereby, this work aims to conduct an exploratory study about the Artificial Neural Networks type MLP to estimate which is the best setting to perform the stream flow forecast. This study was applied to the Rio Grande basin, in addition, with the achieved results, it was possible to observe that the search of appropriate parameters shows significant gains in the execution of the forecasts and can to reduce the error level obtained.

Keywords

hydroelectric power stations; neural nets; power system planning; MLP neural networks; empirical analysis; hydroelectric plants; streamflow forecasting; Adaptation model; Artificial neural networks; Forecasting; RNA; Reactive power; Time series analysis; Visualization; MLP Neural Networks Configuration; Power Systems Planning; Streamflow Forecasting; Time Series Forecasting;

fLanguage

English

Journal_Title

Latin America Transactions, IEEE (Revista IEEE America Latina)

Publisher

ieee

ISSN

1548-0992

Type

jour

DOI

10.1109/TLA.2011.5893775

Filename

5893775