Water Flow Forecasting Based on River Tributaries Using Long Short-Term Memory Ensemble Model

Water Flow Forecasting Based on River Tributaries Using Long Short-Term Memory Ensemble Model

Water flow forecasts are an essential information for energy production, management and hydropower control. Advanced actions to optimize electricity production can be taken based on predicted information. This work proposes an ensemble strategy using recurrent neural networks to generate a forecast...

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Autores principales: Diogo F. Costa Silva, Arlindo R. Galvão Filho, Rafael V. Carvalho, Filipe de Souza L. Ribeiro, Clarimar J. Coelho
Formato: article
Lenguaje:EN
Publicado: MDPI AG 2021
Materias:
water flow forecasting
energy
ensemble model
long short-term memory
LSTM
Technology
T
Acceso en línea:https://doaj.org/article/da34ca9e167549f990f5e9fef757e55f
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