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Free keywords:
comparative evaluation framework, synthetic streamflow scenario generation, multivariate multistage probabilistic forecast
Abstract:
Synthetic streamflow scenario generation is particularly important in countries like Brazil, where hydroelectric power generation plays a key role and properly handling the uncertainty of future streamflow is crucial. This paper showcases a collaborative effort within the Brazilian electrical sector to enhance streamflow scenario models, focusing on horizons up to one year. Five institutions proposed diverse methodologies, and their effectiveness was evaluated using a comparative framework. The results reveal the strengths and areas for improvement in each model. GHCen emerged as the top performer, excelling in both short-term and moving average analyses, while the PARX model demonstrated superior performance in specific regions. The PAR(p)-A, which is the official methodology in Brazil, was the second-best model in the moving average analysis. This research offers valuable insights for countries facing similar hydrothermal scheduling and scenario generation challenges.
