Previsão de vazões mensais para o sistema interligado nacional (SIN) utilizando modelos periódicos autorregresivos endógenos (PAR) e exógenos (PARX) com a utilização de informações climáticas

• Autor(a) principal: Silveira, Cleiton da Silva
• Data de Publicação: 2017
• Outros Autores: Alexandre, Alan Michell Barros, Souza Filho, Francisco de Assis de, Vasconcelos Júnior, Francisco das Chagas, Cabral, Samuellson Lopes
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da Universidade Federal do Ceará (UFC)
• Texto Completo: http://www.repositorio.ufc.br/handle/riufc/55040
• topic: Setor elétrico; Previsão sazonal; Índices climáticos; Monthly streamflow forecast; Climate information; National Interconnected System

This study aims to find a seasonal streamflow forecast model simultaneous to all stations of SIN using periodic autoregressive models with exogenous variables (PARX) using climate indexes. Comparing the results from PAR and PARX Models, this research analyzes the impact on forecasts by using climate information. The proposed models for streamflow forecast has been carried out using natural streamflow data from Operador Nacional do Sistema (ONS) and statistical techniques (such as multiple linear regression and stepwise method to choose explanatory variables). On 27 climate indexes utilized, 4 of them are suggested in this work. The performance analysis methodology is based on the ELECTRE method further the NASH coefficient, the mean absolute percentage error, the multi-criteria distance and correlation. Forecasts with one month lead, the PAR models present better results for most stations of SIN within seasons DJF, MAM, and JJA, while for SON season there is greater efficiency from PARX model. This kind of model shows better performance during dry season in the basins at Northern Brazil – Amazonas and Araguaia-Tocantins; Central-Eastern Brazil – Eastern Atlantic and the most rivers located in the Paraná basin.