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Article

Selection of Temporal Lags for Predicting Riverflow Series from Hydroelectric Plants Using Variable Selection Methods

by
Hugo Siqueira
1,
Mariana Macedo
2,
Yara de Souza Tadano
3,
Thiago Antonini Alves
4,
Sergio L. Stevan, Jr.
1,
Domingos S. Oliveira, Jr.
5,
Manoel H.N. Marinho
6,
Paulo S.G. de Mattos Neto
5,
 João F. L. de Oliveira
6,
Ivette Luna
7,
Marcos de Almeida Leone Filho
8,
Leonie Asfora Sarubbo
9,10 and
Attilio Converti
11,*
1
Department of Electronics, Federal University of Technology–Parana (UTFPR), Ponta Grossa (PR) 84017-220, Brazil
2
BioComplex Lab, Department of Computer Science, University of Exeter, Exeter EX4 4PY, UK
3
Department of Mathematic, Federal University of Technology–Parana (UTFPR), Ponta Grossa (PR) 84017-220, Brazil
4
Department of Mechanical Engineering, Federal University of Technology–Parana (UTFPR), Ponta Grossa (PR) 84017-220, Brazil
5
Departamento de Sistemas de Computação, Centro de Informática, Universidade Federal de Pernambuco (UFPE), Recife (PE) 50670-901, Brazil
6
Polytechnic School of Pernambuco, University of Pernambuco (UPE), Recife (PE) 50720-001, Brazil
7
Department of Economic Theory, Institute of Economics, State University of Campinas (UNICAMP), Campinas (SP) 13083-857, Brazil
8
Venidera Pesquisa e Desenvolvimento, Campinas 13070-173, Brazil
9
Department of Biotechnology, Catholic University of Pernambuco (UNICAP), Recife (PE) 50050-900, Brazil
10
Advanced Institute of Technology and Innovation (IATI), Recife (PE) 50070-280, Brazil
11
Department of Civil, Chemical and Environmental Engineering, University of Genoa (UNIGE), 16145 Genoa, Italy
 
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*
Author to whom correspondence should be addressed.
Energies 2020, 13(16), 4236; https://doi.org/10.3390/en13164236
Submission received: 14 July 2020 / Revised: 10 August 2020 / Accepted: 13 August 2020 / Published: 16 August 2020
(This article belongs to the Special Issue Valorisation of Wastes: Environmental Sustainability and Production of Biofuels by Advanced Technologies)
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Abstract

The forecasting of monthly seasonal streamflow time series is an important issue for countries where hydroelectric plants contribute significantly to electric power generation. The main step in the planning of the electric sector’s operation is to predict such series to anticipate behaviors and issues. In general, several proposals of the literature focus just on the determination of the best forecasting models. However, the correct selection of input variables is an essential step for the forecasting accuracy, which in a univariate model is given by the lags of the time series to forecast. This task can be solved by variable selection methods since the performance of the predictors is directly related to this stage. In the present study, we investigate the performances of linear and non-linear filters, wrappers, and bio-inspired metaheuristics, totaling ten approaches. The addressed predictors are the extreme learning machine neural networks, representing the non-linear approaches, and the autoregressive linear models, from the Box and Jenkins methodology. The computational results regarding five series from hydroelectric plants indicate that the wrapper methodology is adequate for the non-linear method, and the linear approaches are better adjusted using filters.
Keywords: monthly forecasting; autoregressive model; wrapper; bio-inspired metaheuristics extreme learning machines neural networks monthly forecasting; autoregressive model; wrapper; bio-inspired metaheuristics extreme learning machines neural networks
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MDPI and ACS Style

Siqueira, H.; Macedo, M.; Tadano, Y.d.S.; Alves, T.A.; Stevan, S.L., Jr.; Oliveira, D.S., Jr.; Marinho, M.H.N.; Neto, P.S.G.d.M.; Oliveira,  .F.L.d.; Luna, I.; et al. Selection of Temporal Lags for Predicting Riverflow Series from Hydroelectric Plants Using Variable Selection Methods. Energies 2020, 13, 4236. https://doi.org/10.3390/en13164236

AMA Style

Siqueira H, Macedo M, Tadano YdS, Alves TA, Stevan SL Jr., Oliveira DS Jr., Marinho MHN, Neto PSGdM, Oliveira  FLd, Luna I, et al. Selection of Temporal Lags for Predicting Riverflow Series from Hydroelectric Plants Using Variable Selection Methods. Energies. 2020; 13(16):4236. https://doi.org/10.3390/en13164236

Chicago/Turabian Style

Siqueira, Hugo, Mariana Macedo, Yara de Souza Tadano, Thiago Antonini Alves, Sergio L. Stevan, Jr., Domingos S. Oliveira, Jr., Manoel H.N. Marinho, Paulo S.G. de Mattos Neto,  João F. L. de Oliveira, Ivette Luna, and et al. 2020. "Selection of Temporal Lags for Predicting Riverflow Series from Hydroelectric Plants Using Variable Selection Methods" Energies 13, no. 16: 4236. https://doi.org/10.3390/en13164236

APA Style

Siqueira, H., Macedo, M., Tadano, Y. d. S., Alves, T. A., Stevan, S. L., Jr., Oliveira, D. S., Jr., Marinho, M. H. N., Neto, P. S. G. d. M., Oliveira,  . F. L. d., Luna, I., Filho, M. d. A. L., Sarubbo, L. A., & Converti, A. (2020). Selection of Temporal Lags for Predicting Riverflow Series from Hydroelectric Plants Using Variable Selection Methods. Energies, 13(16), 4236. https://doi.org/10.3390/en13164236

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