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

Diogo F. Costa Silva, Arlindo R. Galvão Filho, Rafael V. Carvalho, Filipe de Souza L. Ribeiro and Clarimar J. Coelho
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Diogo F. Costa Silva: Institute of Informatics, Federal University of Goiás, Goiânia 74690-900, Brazil
Arlindo R. Galvão Filho: Institute of Informatics, Federal University of Goiás, Goiânia 74690-900, Brazil
Rafael V. Carvalho: Master’s School of Production and Systems Engineering (MEPROS), Pontifical Catholic University of Goiás, Goiânia 74605-220, Brazil
Filipe de Souza L. Ribeiro: Operational Department, Jirau Hidroeletric Power Plant, Energia Sustentável do Brasil, Porto Velho 76840-000, Brazil
Clarimar J. Coelho: Master’s School of Production and Systems Engineering (MEPROS), Pontifical Catholic University of Goiás, Goiânia 74605-220, Brazil

Energies, 2021, vol. 14, issue 22, 1-12

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.

Keywords: water flow forecasting; energy; ensemble model; long short-term memory; LSTM (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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