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

Hugo Siqueira, Mariana Macedo, Yara de Souza Tadano, Thiago Antonini Alves, Sergio L. Stevan, Domingos S. Oliveira, Manoel H.N. Marinho, Paulo S.G. de Mattos Neto, João F. L. de Oliveira, Ivette Luna, Marcos de Almeida Leone Filho, Leonie Asfora Sarubbo and Attilio Converti
Additional contact information
Hugo Siqueira: Department of Electronics, Federal University of Technology–Parana (UTFPR), Ponta Grossa (PR) 84017-220, Brazil
Mariana Macedo: BioComplex Lab, Department of Computer Science, University of Exeter, Exeter EX4 4PY, UK
Yara de Souza Tadano: Department of Mathematic, Federal University of Technology–Parana (UTFPR), Ponta Grossa (PR) 84017-220, Brazil
Thiago Antonini Alves: Department of Mechanical Engineering, Federal University of Technology–Parana (UTFPR), Ponta Grossa (PR) 84017-220, Brazil
Sergio L. Stevan: Department of Electronics, Federal University of Technology–Parana (UTFPR), Ponta Grossa (PR) 84017-220, Brazil
Domingos S. Oliveira: Departamento de Sistemas de Computação, Centro de Informática, Universidade Federal de Pernambuco (UFPE), Recife (PE) 50670-901, Brazil
Manoel H.N. Marinho: Polytechnic School of Pernambuco, University of Pernambuco (UPE), Recife (PE) 50720-001, Brazil
Paulo S.G. de Mattos Neto: Departamento de Sistemas de Computação, Centro de Informática, Universidade Federal de Pernambuco (UFPE), Recife (PE) 50670-901, Brazil
João F. L. de Oliveira: Polytechnic School of Pernambuco, University of Pernambuco (UPE), Recife (PE) 50720-001, Brazil
Marcos de Almeida Leone Filho: Venidera Pesquisa e Desenvolvimento, Campinas 13070-173, Brazil
Leonie Asfora Sarubbo: Department of Biotechnology, Catholic University of Pernambuco (UNICAP), Recife (PE) 50050-900, Brazil
Attilio Converti: Department of Civil, Chemical and Environmental Engineering, University of Genoa (UNIGE), 16145 Genoa, Italy

Energies, 2020, vol. 13, issue 16, 1-35

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 (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 (7)

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