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

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

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 issu...

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Bibliographic Details
Main Authors: 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, Attilio Converti
Format: Article
Language:English
Published: MDPI AG 2020-08-01
Series:Energies
Subjects:
monthly forecasting
autoregressive model
wrapper
bio-inspired metaheuristics extreme learning machines neural networks
Online Access:https://www.mdpi.com/1996-1073/13/16/4236

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https://www.mdpi.com/1996-1073/13/16/4236

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