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

Authors :
Rafael Viana de Carvalho
Arlindo Rodrigues Galvão Filho
Diogo Fernandes Costa Silva
Clarimar José Coelho
Filipe de Souza Lima Ribeiro
Source :
Energies, Vol 14, Iss 7707, p 7707 (2021), Energies, Volume 14, Issue 22
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

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 of water flow at Jirau Hydroelectric Power Plant (HPP), installed on the Madeira River in Brazil. The ensemble strategy consists of combining three long short-term memory (LSTM) networks that model the Madeira River and two of its tributaries: Mamoré and Abunã rivers. The historical data from streamflow of the Madeira river and its tributaries are used to validate the ensemble LSTM model, where each time series of river tributaries are modeled separated by LSTM models and the result used as input for another LSTM model in order to forecast the streamflow of the main river. The experimental results present low errors for training and test sets for individual LSTM networks and ensemble model. In addition, these results were compared with the operational forecasts performed by Jirau HPP. The proposed model showed better accuracy in four of the five scenarios tested, which indicates a promising approach to be explored in water flow forecasting based on river tributaries.

Details

Language :
English
ISSN :
19961073
Volume :
14
Issue :
7707
Database :
OpenAIRE
Journal :
Energies
Accession number :
edsair.doi.dedup.....2432a476dc1260bc9463803902e55170