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Water Flow Forecasting Based on River Tributaries Using Long Short-Term Memory Ensemble Model
- 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.
- Subjects :
- Technology
Control and Optimization
Water flow
Energy Engineering and Power Technology
water flow forecasting
Hydroelectricity
Streamflow
Tributary
ensemble model
Electrical and Electronic Engineering
Engineering (miscellaneous)
Hydropower
Hydrology
geography
geography.geographical_feature_category
Ensemble forecasting
Renewable Energy, Sustainability and the Environment
business.industry
Main river
Recurrent neural network
Environmental science
business
long short-term memory
LSTM
Energy (miscellaneous)
energy
Subjects
Details
- Language :
- English
- ISSN :
- 19961073
- Volume :
- 14
- Issue :
- 7707
- Database :
- OpenAIRE
- Journal :
- Energies
- Accession number :
- edsair.doi.dedup.....2432a476dc1260bc9463803902e55170