Figure 1
Schematic diagram of the HPPs in NIS.
Figure 2
Flowchart of comparative evaluation framework.
Figure 3
Lowest NCRPS model, in t + 1, for each HPP of NIS.
Figure 4
Lowest NCRPS model, for a moving average of twelve months, for each HPP of NIS.
Figure 5
NCRPS of south region HPPs, for the predictive distributions at t + 9.
Figure 6
Boxplots of predictive distributions of twelve-month moving averages performed with CARMA (red) and PAR(p)-A (blue), for Furnas HPP.
Figure 7
Percentage of improvement in HPP and NEI in terms of predictive distributions at each time step (a) and for moving averages (b), using NRMSE as a metric.
Figure 8
NRMSE of north region HPPs, for the predictive distributions at t + 1.
Figure 9
Boxplots of predictive distributions of six-month moving average performed with the MS-PAR(p) (red) and PAR(p)-A (blue), for the Itá HPP.
Figure 10
Boxplots of predictive distributions of six-month moving average performed with the MS-PAR(p) (red) and PAR(p)-A (blue), for the Santo Antônio Jari HPP.
Figure 11
Percentage of improvement in HPP and NEI in terms of predictive distributions at each time step (a) and for moving averages (b), using MAPE as a metric.
Figure 12
Boxplots of the synthetic temporal autocorrelations of hyper-multimodel (red), PAR(p)-A (blue), and for historical values (green), fixing the first month of each of the simulations, and varying the second vector of t +2 until t+12, for Pimental HPP.
Figure 13
Boxplots of predictive distributions in t+6 performed with the Hyper-multimodel (red) and PAR(p)-A (blue), for the Itumbiara HPP.
Figure 14
Boxplots of predictive distributions of six months moving average performed with the Hyper-multimodel (red) and PAR(p)-A (blue), for the Porto Primavera HPP.
Figure 15
NCRPS of north region HPPs, for the predictive distributions at t + 1.
Figure 16
Boxplots of predictive distributions in t+1 performed with PARX (red) and PAR(p)-A (blue), Pimental HPP.
Figure 17
Boxplots of predictive distributions in t+1 performed with PARX (red) and PAR(p)-A (blue), Baixo Iguaçu HPP.
Figure 18
Boxplots of the synthetic temporal autocorrelations of the model proposed by the PARX (red), PAR(p)-A (blue), and for the historical values (green), fixing the first month of each of the simulations, and varying the second vector from t+2 to t+12, for Furnas HPP.
Figure 19
NCRPS Grande and Paranaíba basin HPPs, for the predictive distributions at t + 1.
Figure 20
Boxplots of predictive distributions in t+1 performed with GHCen (red) and PAR(p)-A (blue), Furnas HPP.
Figure 21
Boxplots of predictive distributions in t+1 performed with GHCen (red) and PAR(p)-A (blue), Pimental HPP.
Figure 5
NCRPS of south region HPPs, for the predictive distributions at t + 9.
Figure 6
Boxplots of predictive distributions of twelve-month moving averages performed with CARMA (red) and PAR(p)-A (blue), for Furnas HPP.
Figure 7
Percentage of improvement in HPP and NEI in terms of predictive distributions at each time step (a) and for moving averages (b), using NRMSE as a metric.
Figure 8
NRMSE of north region HPPs, for the predictive distributions at t + 1.
Figure 9
Boxplots of predictive distributions of six-month moving average performed with the MS-PAR(p) (red) and PAR(p)-A (blue), for the Itá HPP.
Figure 10
Boxplots of predictive distributions of six-month moving average performed with the MS-PAR(p) (red) and PAR(p)-A (blue), for the Santo Antônio Jari HPP.
Figure 11
Percentage of improvement in HPP and NEI in terms of predictive distributions at each time step (a) and for moving averages (b), using MAPE as a metric.
Figure 12
Boxplots of the synthetic temporal autocorrelations of hyper-multimodel (red), PAR(p)-A (blue), and for historical values (green), fixing the first month of each of the simulations, and varying the second vector of t +2 until t+12, for Pimental HPP.
Figure 13
Boxplots of predictive distributions in t+6 performed with the Hyper-multimodel (red) and PAR(p)-A (blue), for the Itumbiara HPP.
Figure 14
Boxplots of predictive distributions of six months moving average performed with the Hyper-multimodel (red) and PAR(p)-A (blue), for the Porto Primavera HPP.
Figure 15
NCRPS of north region HPPs, for the predictive distributions at t + 1.
Figure 16
Boxplots of predictive distributions in t+1 performed with PARX (red) and PAR(p)-A (blue), Pimental HPP.
Figure 17
Boxplots of predictive distributions in t+1 performed with PARX (red) and PAR(p)-A (blue), Baixo Iguaçu HPP.
Figure 18
Boxplots of the synthetic temporal autocorrelations of the model proposed by the PARX (red), PAR(p)-A (blue), and for the historical values (green), fixing the first month of each of the simulations, and varying the second vector from t+2 to t+12, for Furnas HPP.
Figure 19
NCRPS Grande and Paranaíba basin HPPs, for the predictive distributions at t + 1.
Figure 20
Boxplots of predictive distributions in t+1 performed with GHCen (red) and PAR(p)-A (blue), Furnas HPP.
Figure 21
Boxplots of predictive distributions in t+1 performed with GHCen (red) and PAR(p)-A (blue), Pimental HPP.
Table 1
Summary of evaluated methodologies and their main characteristics.
Table 2
HPP’s improvement percentage in comparison to the PAR(p)-A model, for each time step, using NCRPS as the metric. The highest-performing proposed model among the five is highlighted in red.
Table 3
Improvement percentage for NEI in comparison to the PAR(p)-A model, for each time step, using NCRPS as the metric.
Table 4
Improvement percentage for HPP in comparison to the PAR(p)-A model, for moving average, using NCRPS as the metric.
Table 5
Improvement percentage for NEI in comparison to the PAR(p)-A model, for moving average, using NCRPS as the metric.
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