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

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  • Jônatas Belotti

    (Graduate Program in Computer Sciences, Federal University of Technology–Parana (UTFPR), Ponta Grossa 84017-220, Brazil
    Institute of Computing, State University of Campinas (UNICAMP), Campinas 13083-852, 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
    Advanced Institute of Technology and Innovation (IATI), Recife 50751-310, Brazil)

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.

Suggested Citation

  • 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 & Att, 2020. "Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants," Energies, MDPI, vol. 13(18), pages 1-22, September.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4769-:d:412800
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    References listed on IDEAS

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    1. Cao, Qing & Ewing, Bradley T. & Thompson, Mark A., 2012. "Forecasting wind speed with recurrent neural networks," European Journal of Operational Research, Elsevier, vol. 221(1), pages 148-154.
    2. Hui Wang & Jingxuan Sun & Jianbo Sun & Jilong Wang, 2017. "Using Random Forests to Select Optimal Input Variables for Short-Term Wind Speed Forecasting Models," Energies, MDPI, vol. 10(10), pages 1-13, October.
    3. Rendon-Sanchez, Juan F. & de Menezes, Lilian M., 2019. "Structural combination of seasonal exponential smoothing forecasts applied to load forecasting," European Journal of Operational Research, Elsevier, vol. 275(3), pages 916-924.
    4. Yslene Kachba & Daiane Maria de Genaro Chiroli & Jônatas T. Belotti & Thiago Antonini Alves & Yara de Souza Tadano & Hugo Siqueira, 2020. "Artificial Neural Networks to Estimate the Influence of Vehicular Emission Variables on Morbidity and Mortality in the Largest Metropolis in South America," Sustainability, MDPI, vol. 12(7), pages 1-15, March.
    5. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    6. Wei Dong & Qiang Yang & Xinli Fang, 2018. "Multi-Step Ahead Wind Power Generation Prediction Based on Hybrid Machine Learning Techniques," Energies, MDPI, vol. 11(8), pages 1-19, July.
    7. Caston Sigauke & Murendeni Maurel Nemukula & Daniel Maposa, 2018. "Probabilistic Hourly Load Forecasting Using Additive Quantile Regression Models," Energies, MDPI, vol. 11(9), pages 1-21, August.
    8. Kim, Deockho & Hur, Jin, 2018. "Short-term probabilistic forecasting of wind energy resources using the enhanced ensemble method," Energy, Elsevier, vol. 157(C), pages 211-226.
    9. Zhang, G. Peter & Qi, Min, 2005. "Neural network forecasting for seasonal and trend time series," European Journal of Operational Research, Elsevier, vol. 160(2), pages 501-514, January.
    10. Fernando Mainardi Fan & Dirk Schwanenberg & Rodolfo Alvarado & Alberto Assis dos Reis & Walter Collischonn & Steffi Naumman, 2016. "Performance of Deterministic and Probabilistic Hydrological Forecasts for the Short-Term Optimization of a Tropical Hydropower Reservoir," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(10), pages 3609-3625, August.
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    Cited by:

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    2. Filelis - Papadopoulos, Christos K. & Kyziropoulos, Panagiotis E. & Morrison, John P. & O‘Reilly, Philip, 2022. "Modelling and forecasting based on recursive incomplete pseudoinverse matrices," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 197(C), pages 358-376.

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