The electric energy planning of the National Interconnected System - SIN has a close correlation between present stocks of water in the reservoirs of hydroelectric plants and future inflows. For time periods above one month, inflows scenarios are generated without incorporating any kind of climate information. The inclusion of such information can improve the representation of future hydrological conditions. The objective of this study is to evaluate the gain that can be achieved with the use of precipitation information on generating reservoir inflows scenarios. We generated reservoir inflows scenarios to the Grande River basin using univariate autoregressive modeling – AR, which considers only past inflows, and multivariate modeling – ARx, which also considers precipitation information. Statistical indices were calculated to evaluate the performance of the scenarios generated using the two types of autoregressive models by comparing the predicted inflow with the observed inflow. The results highlighted that both the univariate and multivariate modeling represented well the seasonal behavior of the inflows, however the univariate methodology presented difficulties in capturing the corresponding natural variability. Statistical indices showed the best performance of the ARx model, indicating that the inclusion of the precipitation information proved to be an important addition to generate reservoir inflows scenarios.

AVALIAÇÃO DO DESEMPENHO DA GERAÇÃO DE CENÁRIOS DE AFLUÊNCIAS EM RESERVATÓRIOS UTILIZANDO PREVISÕES DE PRECIPITAÇÃO POR CONJUNTO

Cossich Marcial de Farias, W.;
2015

Abstract

The electric energy planning of the National Interconnected System - SIN has a close correlation between present stocks of water in the reservoirs of hydroelectric plants and future inflows. For time periods above one month, inflows scenarios are generated without incorporating any kind of climate information. The inclusion of such information can improve the representation of future hydrological conditions. The objective of this study is to evaluate the gain that can be achieved with the use of precipitation information on generating reservoir inflows scenarios. We generated reservoir inflows scenarios to the Grande River basin using univariate autoregressive modeling – AR, which considers only past inflows, and multivariate modeling – ARx, which also considers precipitation information. Statistical indices were calculated to evaluate the performance of the scenarios generated using the two types of autoregressive models by comparing the predicted inflow with the observed inflow. The results highlighted that both the univariate and multivariate modeling represented well the seasonal behavior of the inflows, however the univariate methodology presented difficulties in capturing the corresponding natural variability. Statistical indices showed the best performance of the ARx model, indicating that the inclusion of the precipitation information proved to be an important addition to generate reservoir inflows scenarios.
2015
Cossich Marcial de Farias, W.; Cataldi, M.; Correa Rotunno Filho, O.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/790713
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