LAPSE:2023.25872
Published Article
LAPSE:2023.25872
Selection of Temporal Lags for Predicting Riverflow Series from Hydroelectric Plants Using Variable Selection Methods
March 31, 2023
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.
Record ID
Keywords
autoregressive model, bio-inspired metaheuristics extreme learning machines neural networks, monthly forecasting, wrapper
Suggested Citation
Siqueira H, Macedo M, Tadano YDS, Alves TA, Stevan SL Jr, Oliveira DS Jr, Marinho MH, Neto PSDM, Oliveira FLD, Luna I, Filho MDAL, Sarubbo LA, Converti A. Selection of Temporal Lags for Predicting Riverflow Series from Hydroelectric Plants Using Variable Selection Methods. (2023). LAPSE:2023.25872
Author Affiliations
Siqueira H: Department of Electronics, Federal University of Technology−Parana (UTFPR), Ponta Grossa (PR) 84017-220, Brazil [ORCID]
Macedo M: BioComplex Lab, Department of Computer Science, University of Exeter, Exeter EX4 4PY, UK [ORCID]
Tadano YDS: Department of Mathematic, Federal University of Technology−Parana (UTFPR), Ponta Grossa (PR) 84017-220, Brazil [ORCID]
Alves TA: Department of Mechanical Engineering, Federal University of Technology−Parana (UTFPR), Ponta Grossa (PR) 84017-220, Brazil [ORCID]
Stevan SL Jr: Department of Electronics, Federal University of Technology−Parana (UTFPR), Ponta Grossa (PR) 84017-220, Brazil [ORCID]
Oliveira DS Jr: Departamento de Sistemas de Computação, Centro de Informática, Universidade Federal de Pernambuco (UFPE), Recife (PE) 50670-901, Brazil
Marinho MH: Polytechnic School of Pernambuco, University of Pernambuco (UPE), Recife (PE) 50720-001, Brazil
Neto PSDM: Departamento de Sistemas de Computação, Centro de Informática, Universidade Federal de Pernambuco (UFPE), Recife (PE) 50670-901, Brazil [ORCID]
Oliveira FLD: Polytechnic School of Pernambuco, University of Pernambuco (UPE), Recife (PE) 50720-001, Brazil [ORCID]
Luna I: Department of Economic Theory, Institute of Economics, State University of Campinas (UNICAMP), Campinas (SP) 13083-857, Brazil
Filho MDAL: Venidera Pesquisa e Desenvolvimento, Campinas 13070-173, Brazil
Sarubbo LA: Department of Biotechnology, Catholic University of Pernambuco (UNICAP), Recife (PE) 50050-900, Brazil; Advanced Institute of Technology and Innovation (IATI), Recife (PE) 50070-280, Brazil [ORCID]
Converti A: Department of Civil, Chemical and Environmental Engineering, University of Genoa (UNIGE), 16145 Genoa, Italy [ORCID]
Macedo M: BioComplex Lab, Department of Computer Science, University of Exeter, Exeter EX4 4PY, UK [ORCID]
Tadano YDS: Department of Mathematic, Federal University of Technology−Parana (UTFPR), Ponta Grossa (PR) 84017-220, Brazil [ORCID]
Alves TA: Department of Mechanical Engineering, Federal University of Technology−Parana (UTFPR), Ponta Grossa (PR) 84017-220, Brazil [ORCID]
Stevan SL Jr: Department of Electronics, Federal University of Technology−Parana (UTFPR), Ponta Grossa (PR) 84017-220, Brazil [ORCID]
Oliveira DS Jr: Departamento de Sistemas de Computação, Centro de Informática, Universidade Federal de Pernambuco (UFPE), Recife (PE) 50670-901, Brazil
Marinho MH: Polytechnic School of Pernambuco, University of Pernambuco (UPE), Recife (PE) 50720-001, Brazil
Neto PSDM: Departamento de Sistemas de Computação, Centro de Informática, Universidade Federal de Pernambuco (UFPE), Recife (PE) 50670-901, Brazil [ORCID]
Oliveira FLD: Polytechnic School of Pernambuco, University of Pernambuco (UPE), Recife (PE) 50720-001, Brazil [ORCID]
Luna I: Department of Economic Theory, Institute of Economics, State University of Campinas (UNICAMP), Campinas (SP) 13083-857, Brazil
Filho MDAL: Venidera Pesquisa e Desenvolvimento, Campinas 13070-173, Brazil
Sarubbo LA: Department of Biotechnology, Catholic University of Pernambuco (UNICAP), Recife (PE) 50050-900, Brazil; Advanced Institute of Technology and Innovation (IATI), Recife (PE) 50070-280, Brazil [ORCID]
Converti A: Department of Civil, Chemical and Environmental Engineering, University of Genoa (UNIGE), 16145 Genoa, Italy [ORCID]
Journal Name
Energies
Volume
13
Issue
16
Article Number
E4236
Year
2020
Publication Date
2020-08-16
Published Version
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en13164236, Publication Type: Journal Article
Record Map
Published Article
LAPSE:2023.25872
This Record
External Link
doi:10.3390/en13164236
Publisher Version
Download
Meta
Record Statistics
Record Views
63
Version History
[v1] (Original Submission)
Mar 31, 2023
Verified by curator on
Mar 31, 2023
This Version Number
v1
Citations
Most Recent
This Version
URL Here
https://psecommunity.org/LAPSE:2023.25872
Original Submitter
Auto Uploader for LAPSE
Links to Related Works