Long Memory Models to Generate Synthetic Hydrological Series
Joint Authors
Pereira, Guilherme Armando de Almeida
Castro Souza, Reinaldo
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-07-16
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
In Brazil, much of the energy production comes from hydroelectric plants whose planning is not trivial due to the strong dependence on rainfall regimes.
This planning is accomplished through optimization models that use inputs such as synthetic hydrologic series generated from the statistical model PAR(p) (periodic autoregressive).
Recently, Brazil began the search for alternative models able to capture the effects that the traditional model PAR(p) does not incorporate, such as long memory effects.
Long memory in a time series can be defined as a significant dependence between lags separated by a long period of time.
Thus, this research develops a study of the effects of long dependence in the series of streamflow natural energy in the South subsystem, in order to estimate a long memory model capable of generating synthetic hydrologic series.
American Psychological Association (APA)
Pereira, Guilherme Armando de Almeida& Castro Souza, Reinaldo. 2014. Long Memory Models to Generate Synthetic Hydrological Series. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-500907
Modern Language Association (MLA)
Pereira, Guilherme Armando de Almeida& Castro Souza, Reinaldo. Long Memory Models to Generate Synthetic Hydrological Series. Mathematical Problems in Engineering No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-500907
American Medical Association (AMA)
Pereira, Guilherme Armando de Almeida& Castro Souza, Reinaldo. Long Memory Models to Generate Synthetic Hydrological Series. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-500907
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-500907