Title :
Periodic ARMA models applied to weekly streamflow forecasts
Author :
Maceira, M.E.P. ; Damazio, J.M. ; Ghirardi, A.O. ; Dantas, H.M.
Author_Institution :
CEPEL, UERJ, Rio de Janeiro, Brazil
Abstract :
This paper presents a weekly streamflow forecasting model based on linear ARMA (p, q) models, considering both periodic and nonperiodic models. For each week, fifty possible models are automatically analyzed. The best modeling and parameter estimation are chosen based on the minimum square mean forecast error of the whole time series. The proposed model, which has been validated by the Brazilian Multi-Utility Hydrological Studies Working Group, is illustrated in case studies with several hydraulic plants of the Brazilian Southern, Southeastern, North and Northeastern systems.
Keywords :
autoregressive moving average processes; hydroelectric power stations; hydrothermal power systems; parameter estimation; power system planning; time series; Brazilian Multi-Utility Hydrological Studies Working Group; hydraulic plants; hydrothermal system; inflow forecast; linear ARMA models; minimum square mean forecast error; nonperiodic models; operation planning; p, q models; parameter estimation; periodic ARMA models; stochastic models; time series; weekly streamflow forecasts; Costs; Parameter estimation; Predictive models; Production planning; Production systems; Reservoirs; Rivers; Stochastic processes; Topology; Uncertainty;
Conference_Titel :
Electric Power Engineering, 1999. PowerTech Budapest 99. International Conference on
Conference_Location :
Budapest, Hungary
Print_ISBN :
0-7803-5836-8
DOI :
10.1109/PTC.1999.826517
