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Open AccessArticle

Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants

1
Graduate Program in Computer Sciences, Federal University of Technology–Parana (UTFPR), Ponta Grossa 84017-220, Brazil
2
Institute of Computing, State University of Campinas (UNICAMP), Campinas 13083-852, Brazil
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Departamento de Sistemas de Computação, Centro de Informática, Universidade Federal de Pernambuco, (UFPE), Recife 50740-560, Brazil
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Polytechnic School of Pernambuco, University of Pernambuco, Recife 50100-010, Brazil
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Venidera Pesquisa e Desenvolvimento, Campinas 13070-173, Brazil
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Department of Civil, Chemical and Environmental Engineering, University of Genoa (UNIGE), 16126 Genoa, Italy
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Department of Biotechnology, Catholic University of Pernambuco (UNICAP), Recife 50050-900, Brazil
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Advanced Institute of Technology and Innovation (IATI), Recife 50751-310, Brazil
*
Author to whom correspondence should be addressed.
Energies 2020, 13(18), 4769; https://doi.org/10.3390/en13184769
Received: 2 July 2020 / Revised: 1 September 2020 / Accepted: 4 September 2020 / Published: 12 September 2020
(This article belongs to the Special Issue Environmental and Energetic Valorization of Renewable Resources)
Estimating future streamflows is a key step in producing electricity for countries with hydroelectric plants. Accurate predictions are particularly important due to environmental and economic impact they lead. In order to analyze the forecasting capability of models regarding monthly seasonal streamflow series, we realized an extensive investigation considering: six versions of unorganized machines—extreme learning machines (ELM) with and without regularization coefficient (RC), and echo state network (ESN) using the reservoirs from Jaeger’s and Ozturk et al., with and without RC. Additionally, we addressed the ELM as the combiner of a neural-based ensemble, an investigation not yet accomplished in such context. A comparative analysis was performed utilizing two linear approaches (autoregressive model (AR) and autoregressive and moving average model (ARMA)), four artificial neural networks (multilayer perceptron, radial basis function, Elman network, and Jordan network), and four ensembles. The tests were conducted at five hydroelectric plants, using horizons of 1, 3, 6, and 12 steps ahead. The results indicated that the unorganized machines and the ELM ensembles performed better than the linear models in all simulations. Moreover, the errors showed that the unorganized machines and the ELM-based ensembles reached the best general performances. View Full-Text
Keywords: monthly seasonal streamflow series forecasting; artificial neural networks; Box-Jenkins models; ensemble monthly seasonal streamflow series forecasting; artificial neural networks; Box-Jenkins models; ensemble
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MDPI and ACS Style

Belotti, J.; Siqueira, H.; Araujo, L.; Stevan, S.L., Jr.; de Mattos Neto, P.S.; Marinho, M.H.N.; de Oliveira, J.F.L.; Usberti, F.; Leone Filho, M.A.; Converti, A.; Sarubbo, L.A. Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants. Energies 2020, 13, 4769.

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