IEEE Computational Intelligence Magazine - August 2021 - 62
Elman neural network (ENN) [59]; a multilayer perceptron
(MLP); a decision tree (DT) model; support vector regression
with a linear kernel
(SVR-l), a polynomial kernel
(SVR-p), a radial basis function (RBF) kernel (SVR-r), and
a sigmoid kernel (SVR-s) [60]; a long short-term memory
(LSTM) network; and several variants of the DNM,
specifically, the original DNM, the DNM trained with the
L-SHADE algorithm (EDNM), and a DNR model trained
with the BP algorithm. The parameter settings for all algorithms
are listed in Table III. To ensure fair comparisons,
the experiments with all algorithms were conducted 30
times for each time series.
Since the size of the training dataset can impact the fore0.1
0.2
0.3
0.4
100
200
300
Learning
Epoch
FIGURE 4 Convergence curves of the DNR model for the experimental
wind speed time series.
400
500
One-Day 1:1
One-Day 4:1
One-Day 9:1
Ten-Min 1:1
Ten-Min 4:1
Ten-Min 9:1
casting accuracy, the prediction results of DNR-SMS were
evaluated on wind speed data with different partition ratios
between the training and test datasets, specifically, 1:1, 4:1
and 9:1. The average and standard deviation for each
approach are summarized in Tables IV and V. Note that
MSE and RMSE were calculated using the normalized data,
whereas MAE and R were calculated using the raw data.
Tables IV and V show that for most algorithms, the prediction
results for both the long-term and short-term wind
speed time series with a partition ratio of 4:1 were superior to
those for a ratio of 1:1; moreover, the prediction results for a
partition ratio of 9:1 were inferior to those for 4:1. These
findings suggest that merely increasing the number of
20
15
10
5
20
15
10
5
20
15
10
5
Training and Prediction (One-Day 1:1)
Actual Value
Predictive Value
Training
Prediction
2468 10 12 14 16
Data
Fit
Y = T
10
12
14
16
200
400
600
(a)
Actual Value
Predictive Value
800
Training and Prediction (One-Day 4:1)
Training
1,000
1,200
2
4
6
8
Target
2468 10 12 14 16
Prediction
Data
Fit
Y = T
10
12
14
16
200
400
600
(b)
Actual Value
Predictive Value
Training
800
Training and Prediction (One-Day 9:1)
Prediction
1,000
1,200
2
4
6
8
Target
24 68 10 12 1416
Data
Fit
Y = T
10
12
14
16
200
400
600
(c)
800
1,000
1,200
2
4
6
8
Target
FIGURE 5 Training and prediction results and predicted correlation coefficients of DNR-SMS for the wind speed time series with an interval of
one day and partition ratios of (a) 1:1, (b) 4:1, and (c) 9:1.
62 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | AUGUST 2021
The Values of InFlow (M/S)
The Values of InFlow (M/S)
The Values of InFlow (M/S)
MSE
Output ~= 0.27 ∗ Target + 5
Output ~= 0.29 ∗ Target + 4.8
Output ~= 0.34 ∗ Target + 4.6
IEEE Computational Intelligence Magazine - August 2021
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