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

Jônatas Belotti, Hugo Siqueira, Lilian Araujo, Sérgio L. Stevan, Paulo S.G. de Mattos Neto, Manoel H. N. Marinho, João Fausto L. de Oliveira, Fábio Usberti, Marcos de Almeida Leone Filho, Attilio Converti and Leonie Asfora Sarubbo
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Jônatas Belotti: Graduate Program in Computer Sciences, Federal University of Technology–Parana (UTFPR), Ponta Grossa 84017-220, Brazil
Hugo Siqueira: Graduate Program in Computer Sciences, Federal University of Technology–Parana (UTFPR), Ponta Grossa 84017-220, Brazil
Lilian Araujo: Graduate Program in Computer Sciences, Federal University of Technology–Parana (UTFPR), Ponta Grossa 84017-220, Brazil
Sérgio L. Stevan: Graduate Program in Computer Sciences, Federal University of Technology–Parana (UTFPR), Ponta Grossa 84017-220, Brazil
Paulo S.G. de Mattos Neto: Departamento de Sistemas de Computação, Centro de Informática, Universidade Federal de Pernambuco, (UFPE), Recife 50740-560, Brazil
Manoel H. N. Marinho: Polytechnic School of Pernambuco, University of Pernambuco, Recife 50100-010, Brazil
João Fausto L. de Oliveira: Polytechnic School of Pernambuco, University of Pernambuco, Recife 50100-010, Brazil
Fábio Usberti: Institute of Computing, State University of Campinas (UNICAMP), Campinas 13083-852, Brazil
Marcos de Almeida Leone Filho: Venidera Pesquisa e Desenvolvimento, Campinas 13070-173, Brazil
Attilio Converti: Department of Civil, Chemical and Environmental Engineering, University of Genoa (UNIGE), 16126 Genoa, Italy
Leonie Asfora Sarubbo: Department of Biotechnology, Catholic University of Pernambuco (UNICAP), Recife 50050-900, Brazil

Energies, 2020, vol. 13, issue 18, 1-22

Abstract: 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.

Keywords: monthly seasonal streamflow series forecasting; artificial neural networks; Box-Jenkins models; ensemble (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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