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Evaluation Warning : The document was created with Spire.PDF for Python.
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i
o
n
t
r
e
e
c
l
a
s
s
if
i
e
r
(
E
DT
C
)
m
o
de
l
i
s
m
o
r
e
a
c
c
ur
a
t
e
a
n
d
pr
e
c
i
s
e
a
n
d
h
a
s
l
o
w
l
o
s
s
.
T
h
e
a
c
c
ur
a
c
y
o
f
t
h
e
m
o
de
l
wa
s
99.
07%
whi
c
h
i
s
hi
g
h
e
r
t
h
a
n
t
h
e
ot
h
e
r
a
l
go
r
i
t
hm
s
[
8]
.
T
h
e
p
o
s
s
i
bl
e
b
e
n
e
f
i
t
s
o
f
us
i
n
g
ML
t
e
c
hni
qu
e
s
f
o
r
S
G
a
n
a
l
y
s
i
s
,
i
nc
l
ud
i
ng
i
m
pr
o
vi
n
g
en
e
r
g
y
e
f
f
i
c
i
e
n
c
y
,
r
e
duc
i
n
g
e
n
e
r
g
y
c
o
n
s
u
m
pt
i
o
n
,
a
n
d
o
p
t
i
mi
z
i
ng
e
n
e
r
g
y
m
a
n
a
g
e
m
e
n
t
a
r
e
de
m
o
n
s
t
r
a
t
e
d.
T
h
e
hi
g
hli
g
h
t
s
c
o
n
s
i
s
t
o
f
t
h
e
c
h
a
ll
e
n
ge
s
a
s
s
o
c
i
a
t
e
d
w
i
t
h
i
m
p
l
e
m
e
n
t
i
n
g
M
L
t
e
c
hni
qu
e
s
f
o
r
S
G
a
n
a
l
y
s
i
s
a
nd
pr
o
vi
de
s
r
e
c
o
m
m
e
n
da
t
i
o
n
s
f
o
r
o
v
e
r
c
o
m
i
ng
t
h
e
s
e
c
h
a
ll
e
n
g
e
s
[
9]
.
T
h
e
f
o
c
us
i
s
o
n
t
h
e
e
n
e
r
g
y
u
s
a
ge
r
e
duc
t
i
o
n
i
n
t
h
e
r
e
s
i
d
e
n
t
i
a
l
a
r
e
a
by
c
o
m
pa
r
i
s
o
n
o
f
s
e
v
e
r
a
l
hi
g
hly
a
c
c
ur
a
t
e
f
o
r
e
c
a
s
t
a
l
go
r
i
t
hm
s
.
T
h
e
r
e
s
u
l
t
s
h
o
ws
a
c
o
m
pa
r
i
s
o
n
b
e
t
we
e
n
g
e
n
e
r
a
l
r
e
gr
e
s
s
i
o
n
ne
ur
a
l
n
e
t
wo
r
k
(
GR
NN
)
a
n
d
e
di
t
e
d
n
e
a
r
e
s
t
n
e
i
g
hb
o
ur
(
E
NN
)
m
o
de
l
s
a
n
d
a
l
s
o
s
h
o
ws
t
h
e
e
l
e
c
t
r
i
c
i
t
y
c
o
s
t
pr
e
di
c
t
i
o
n
[
10]
.
T
hi
s
s
t
ud
y
i
n
t
r
o
duc
e
s
a
n
o
v
e
l
m
u
l
t
i
d
i
r
e
c
t
i
o
n
a
l
l
o
n
g
s
h
o
r
t
-
t
e
r
m
m
e
m
o
r
y
(
M
L
S
T
M
)
m
o
de
l
f
o
r
pr
e
d
i
c
t
i
n
g
t
h
e
s
t
a
bi
li
t
y
o
f
SG
,
s
ur
pa
s
s
i
ng
t
r
a
di
t
i
o
na
l
m
o
de
l
s
l
i
ke
ga
t
e
d
r
e
c
ur
r
e
n
t
uni
t
(
GR
U)
,
l
o
n
g
s
ho
r
t
-
t
e
r
m
m
e
m
o
r
y
(
L
S
T
M
)
,
a
n
d
r
e
c
ur
r
e
n
t
n
e
ur
a
l
ne
t
wor
k
(
R
NN
)
i
n
a
c
c
ur
a
c
y
,
pr
e
c
i
s
i
o
n
,
a
n
d
l
o
s
s
m
e
t
r
i
c
s
.
T
e
s
t
e
d
o
n
t
h
e
M
L
r
e
po
s
i
t
o
r
y
’
s
S
G
da
t
a
s
e
t,
t
h
e
M
L
S
T
M
a
c
hi
e
v
e
d
s
i
g
ni
f
i
c
a
n
t
l
y
hi
g
h
e
r
pe
r
f
o
r
m
a
nc
e
,
s
ugge
s
t
i
n
g
f
ut
ur
e
e
x
p
l
o
r
a
t
i
o
n
i
n
c
o
n
t
e
x
t
-
a
wa
r
e
m
o
de
l
s
f
o
r
dy
n
a
mi
c
p
o
we
r
m
a
na
ge
m
e
n
t
[
11]
.
E
s
t
i
m
a
t
i
o
n
o
f
t
h
e
e
l
e
c
t
r
i
c
i
t
y
ge
n
e
r
a
t
i
o
n
i
n
C
y
pr
u
s
i
s
do
n
e
by
i
m
p
l
e
m
e
n
t
i
n
g
f
o
u
r
m
o
de
l
s
’
a
r
t
i
f
i
c
i
a
l
n
e
ur
a
l
ne
t
wor
k
(
A
NN
)
,
a
da
p
t
i
v
e
n
e
ur
o
n
e
ut
r
o
s
o
phi
c
i
nf
e
r
e
nc
e
s
y
s
t
e
m
(
AN
NI
S
)
,
s
upp
o
r
t
v
e
c
t
or
m
a
c
hi
ne
(
S
VM
)
,
a
n
d
m
u
l
t
i
p
l
e
l
i
ne
a
r
r
e
gr
e
s
s
i
o
n
(
M
L
R
)
a
s
l
o
n
g
-
t
e
r
m
a
n
d
s
h
o
r
t
-
t
e
r
m
a
n
a
ly
s
i
s
.
T
hi
s
r
e
s
e
a
r
c
h
a
i
m
s
a
t
b
e
tt
e
r
l
o
a
d
pr
e
d
i
c
t
i
o
n
f
o
r
e
l
e
c
t
r
i
c
i
t
y
l
o
a
d.
I
n
t
h
e
e
v
a
l
ua
t
i
o
n
,
S
VM
s
t
a
n
ds
o
u
t
o
f
a
l
l
t
he
m
o
de
l
s
us
e
d
f
o
r
t
h
e
l
o
n
g
-
t
e
r
m
whe
r
e
a
s
A
NN
i
s
b
e
t
ter
i
n
t
h
e
s
h
o
r
t
-
t
e
r
m
a
na
ly
s
i
s
[
12]
.
F
o
r
e
s
t
i
m
a
t
i
n
g
t
h
e
l
o
a
d
f
o
r
e
c
a
s
t
i
n
g
o
f
s
h
o
r
t
-
t
e
r
m
e
l
e
c
t
r
i
c
a
l
l
o
a
ds
a
n
d
m
a
i
n
ly
i
n
l
o
a
d
pr
o
f
i
l
e
s
o
f
da
y
-
a
h
e
a
d
f
o
r
e
c
a
s
t
i
n
g
whi
c
h
a
r
e
o
f
s
c
h
o
o
l
s
,
i
n
du
s
t
r
i
e
s
,
s
upe
r
m
a
r
ke
t
s
,
a
n
d
r
e
s
i
de
n
t
i
a
l
da
t
a
t
h
e
m
e
t
h
o
ds
us
e
d
a
r
e
s
uppo
r
t
v
e
c
t
or
r
e
g
r
e
s
s
i
o
n
(
S
V
R
)
,
l
i
ne
a
r
r
e
gr
e
s
s
i
o
n
(
L
R
)
,
m
u
l
t
i
l
a
y
e
r
pe
r
c
e
pt
i
o
n
(
M
L
P
)
,
L
S
T
M
,
r
a
n
do
m
f
o
r
e
s
t
(
R
F
)
,
a
u
tor
e
gr
e
s
s
i
ve
i
n
t
e
gr
a
t
e
d
m
o
vi
ng
a
ve
r
a
ge
(
A
R
I
M
A
)
,
a
n
d
K
-
ne
a
r
e
s
t
n
e
i
g
hb
o
ur
(
K
NN
)
.
Am
o
n
g
a
l
l
t
he
m
e
t
h
o
ds
,
K
NN
wa
s
f
o
un
d
to
b
e
t
h
e
m
o
s
t
s
u
i
t
a
bl
e
whi
c
h
i
s
f
o
l
l
o
we
d
by
S
V
R
,
L
R
,
a
n
d
A
R
I
M
A
[
13]
.
T
w
o
m
o
de
l
s
we
r
e
a
pp
l
i
e
d
a
n
d
t
e
s
t
e
d
f
o
r
t
h
e
da
t
a
o
f
e
l
e
c
t
r
i
c
l
o
a
d
t
a
ke
n
f
r
o
m
a
gr
o
c
e
r
y
s
to
r
e
a
n
d
li
b
r
a
r
y
,
t
h
e
n
c
o
m
pa
r
e
d
w
i
t
h
t
h
e
e
xi
s
t
i
n
g
f
o
r
e
c
a
s
t
i
n
g
m
o
de
l
s
.
T
he
l
o
g
i
s
t
i
c
m
i
x
t
ur
e
v
e
c
t
o
r
a
u
to
r
e
gr
e
s
s
i
ve
m
o
de
l
(
L
M
V
A
R
)
o
ut
pe
r
f
o
r
m
s
a
ll
t
h
e
m
o
de
l
s
[
14]
.
A
hy
b
r
i
d
m
o
de
l
,
c
o
m
bi
n
i
ng
v
a
r
i
a
t
i
o
n
a
l
m
o
de
de
c
o
m
po
s
i
t
i
o
n
,
S
VR
,
s
e
l
f
-
r
e
c
ur
r
e
n
t
m
e
c
ha
ni
s
m
s
,
c
h
a
o
t
i
c
m
a
pp
i
ng,
a
n
d
C
uc
ko
o
s
e
a
r
c
h
a
l
go
r
i
t
hm
im
pr
o
v
e
m
e
n
t
s
,
o
u
t
pe
r
f
o
r
m
s
ot
h
e
r
f
o
r
e
c
a
s
t
i
n
g
m
o
de
l
s
.
T
hi
s
s
h
o
ws
e
f
f
i
c
a
c
y
i
n
f
i
e
l
d
s
l
i
ke
s
t
o
c
k
p
r
i
c
e
f
o
r
e
c
a
s
t
i
n
g,
o
f
f
e
r
i
ng
a
dva
n
c
e
d
da
t
a
a
n
a
l
y
s
i
s
,
e
nha
n
c
e
d
a
c
c
ur
a
c
y
,
a
n
d
e
f
f
e
c
t
i
ve
b
o
un
da
r
y
h
a
n
d
li
ng
.
F
u
t
ur
e
wo
r
k
a
i
m
s
to
i
n
t
e
gr
a
t
e
t
h
e
s
e
t
e
c
h
ni
que
s
w
i
t
h
ot
h
e
r
a
l
go
r
i
t
hm
s
f
o
r
b
r
o
a
de
r
a
pp
l
i
c
a
t
i
o
n
s
[
15]
.
T
h
e
pr
o
p
o
s
e
d
s
t
a
t
i
s
t
i
c
a
l
l
o
a
d
f
o
r
e
c
a
s
t
i
n
g
(
S
L
F
)
a
s
s
e
s
s
e
s
r
i
s
k
s
i
n
l
o
a
d
de
m
a
n
d
pr
o
f
il
e
s
v
e
r
i
f
i
e
d
w
i
t
h
i
n
t
e
r
n
a
t
i
o
n
a
l
o
r
ga
ni
z
a
t
i
o
n
f
o
r
s
t
a
n
da
r
d
i
z
a
t
i
o
n
(
I
S
O)
-
Ne
w
E
n
g
l
a
n
d
da
t
a
,
t
hi
s
o
u
t
pe
r
f
o
r
m
s
b
e
n
c
hm
a
r
ks
by
pr
o
vi
d
i
n
g
pr
e
c
i
s
e
pr
e
d
i
c
t
i
o
n
i
n
t
e
r
v
a
l
s
a
n
d
r
i
s
k
e
v
a
l
u
a
t
i
o
ns
f
o
r
s
m
a
r
t
e
r
gr
i
d
o
pe
r
a
t
i
o
n
s
[
16]
.
T
h
e
e
m
p
i
r
i
c
a
l
m
o
de
de
c
o
m
po
s
i
t
i
o
n
-
s
uppo
r
t
v
e
c
t
or
r
e
gr
e
s
s
i
o
n
-
b
a
c
kpr
o
p
a
ga
t
i
o
n
i
n
n
e
ur
a
l
n
e
t
wo
r
k
(
E
M
D
-
S
VR
-
B
P
NN
)
m
o
de
l
e
nh
a
nc
e
s
l
o
a
d
f
o
r
e
c
a
s
t
i
n
g
a
c
c
ur
a
c
y
a
n
d
f
i
t
t
i
n
g,
e
f
f
e
c
t
i
v
e
ly
a
ddr
e
s
s
i
ng
da
t
a
v
o
l
a
t
i
l
i
t
y
a
n
d
t
r
e
n
d
i
s
s
ue
s
f
o
r
po
we
r
s
y
s
t
e
m
s
t
a
bi
li
t
y
[
17]
.
A
s
h
o
r
t
-
t
e
r
m
l
o
a
d
f
o
r
e
c
a
s
t
i
n
g
m
e
t
h
o
d
wa
s
c
o
i
n
e
d
f
o
r
M
e
m
o
r
i
a
l
U
ni
ve
r
s
i
t
y
o
f
Ne
w
f
o
un
d
l
a
nd
us
i
n
g
19
r
e
gr
e
s
s
i
o
n
m
o
de
l
s
,
w
i
t
h
ga
us
s
i
a
n
pr
o
c
e
s
s
r
e
gr
e
s
s
i
o
n
(
GPR
)
m
o
de
l
s
i
de
n
t
i
f
i
e
d
a
s
t
h
e
m
o
s
t
e
f
f
e
c
t
i
ve
due
to
t
h
e
i
r
n
o
n
pa
r
a
m
e
t
r
i
c
,
ke
r
n
e
l
-
b
a
s
e
d
a
ppr
o
a
c
h
.
GPR
e
x
c
e
l
s
i
n
pa
tt
e
r
n
r
e
c
o
gni
t
i
o
n
a
n
d
e
x
t
r
a
po
l
a
t
i
o
n
f
r
o
m
s
m
a
ll
da
t
a
s
e
t
s
,
m
a
k
i
ng
ra
t
i
o
n
a
l
qua
dr
a
t
i
c
a
n
d
e
x
po
ne
n
t
i
a
l
GPR
a
l
go
r
i
t
hm
s
i
de
a
l
f
o
r
f
o
r
e
c
a
s
t
i
n
g
[
18]
.
A
r
e
l
e
v
a
nc
e
v
e
c
t
o
r
m
a
c
hi
ne
(
R
VM
)
b
a
s
e
d
t
e
c
h
ni
que
f
o
r
s
ho
r
t
-
t
e
r
m
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l
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c
t
r
i
c
i
t
y
l
o
a
d
f
o
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c
a
s
t
i
n
g,
i
n
t
e
gr
a
t
i
n
g
wa
v
e
l
e
t
t
r
a
n
s
f
o
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m
a
n
d
f
e
a
t
ur
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s
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l
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c
t
i
o
n
t
hi
s
o
ut
pe
r
f
o
r
m
s
t
r
a
d
i
t
i
o
n
a
l
m
e
t
h
o
ds
by
e
f
f
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c
t
i
ve
ly
ha
n
d
l
i
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n
o
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s
y
da
t
a
a
n
d
pr
o
vi
d
i
n
g
pr
o
b
a
bil
i
s
t
i
c
pr
e
d
i
c
t
i
o
ns
.
W
h
e
n
t
e
s
t
e
d
w
i
t
h
Ne
w
Yo
r
k
i
n
de
pe
n
de
n
t
s
y
s
t
e
m
o
pe
r
a
to
r
(
NY
I
S
O)
a
n
d
I
S
O
Ne
w
E
n
g
l
a
n
d
da
t
a
,
t
hi
s
s
h
o
ws
pot
e
n
t
i
a
l
f
o
r
pr
a
c
t
i
c
a
l
a
pp
li
c
a
t
i
o
n
a
n
d
f
ut
ur
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pr
i
c
i
ng
s
t
r
a
t
e
gy
o
pt
i
mi
z
a
t
i
o
n
[
19]
.
De
m
o
n
s
t
r
a
t
e
d
e
f
f
e
c
t
i
v
e
ne
s
s
o
n
b
e
n
c
hm
a
r
ks
a
n
d
r
e
a
l
-
wo
r
l
d
da
t
a
s
h
o
ws
s
i
g
ni
f
i
c
a
n
t
f
o
r
e
c
a
s
t
i
n
g
im
pr
o
v
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m
e
n
t
,
hi
g
hl
i
g
h
t
i
n
g
i
t
s
i
m
po
r
t
a
n
c
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f
o
r
s
us
t
a
i
na
bl
e
de
v
e
l
o
p
m
e
n
t
[
20]
.
T
h
e
R
F
-
m
o
m
e
n
t
ge
n
e
r
a
t
i
n
g
f
u
n
c
t
i
o
n
(
M
GF
)
r
e
s
po
n
s
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ur
f
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c
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m
e
t
h
o
do
l
o
gy
(
R
S
M
)
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b
r
i
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m
o
d
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l
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m
bi
ne
s
R
F
a
n
d
m
e
a
n
ge
n
e
r
a
t
i
n
g
f
u
n
c
t
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o
n
f
o
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s
h
o
r
t
-
t
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r
m
l
o
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d
f
o
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c
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s
t
i
n
g,
s
i
g
ni
f
i
c
a
nt
l
y
m
a
xim
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z
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a
c
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ur
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y
by
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pt
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m
i
z
i
ng
i
nput
v
a
r
i
a
bl
e
s
a
n
d
us
i
n
g
r
e
s
po
ns
e
s
ur
f
a
c
e
m
e
t
h
o
do
l
o
g
y
,
e
s
pe
c
i
a
ll
y
in
f
l
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t
ua
t
i
n
g
da
t
a
pe
a
ks
a
n
d
va
l
l
e
y
s
[
21]
.
T
h
e
de
e
p
f
o
r
e
s
t
r
e
gr
e
s
s
i
o
n
,
de
s
i
g
n
e
d
f
o
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s
h
o
r
t
-
t
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r
m
po
we
r
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y
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t
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m
l
o
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d
f
o
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c
a
s
t
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n
g,
o
u
t
pe
r
f
o
r
m
s
t
r
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di
t
i
o
n
a
l
a
l
go
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i
t
hm
s
w
i
t
h
i
t
s
dua
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-
pr
o
c
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dur
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s
t
r
uc
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ur
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,
m
i
n
im
i
z
i
ng
m
e
a
n
a
b
s
o
l
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t
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pe
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t
a
ge
e
r
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o
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.
I
t
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m
p
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t
h
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o
ugh
i
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pr
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v
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d
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t
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r
a
t
i
o
ns
[
22]
.
T
h
e
C
#
o
pe
n
s
o
ur
c
e
m
a
n
a
g
e
d
o
pe
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a
t
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n
g
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y
s
t
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m
(
C
OSM
OS)
s
c
he
m
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c
o
m
bi
ne
s
de
e
p
ne
ur
a
l
n
e
t
wo
r
k
(
DN
N)
m
o
de
l
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us
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a
s
t
a
c
k
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n
g
a
ppr
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v
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m
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a
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c
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y
m
a
n
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ge
m
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n
t
s
y
s
t
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m
s
[
23]
-
[
25]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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by
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6
]
.
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7
]
.
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[
28]
.
A
v
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u
tor
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gr
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s
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v
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(
V
A
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)
[
29]
m
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us
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to
pr
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[
30
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3.
RE
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3.
1.
Anal
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T
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po
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m
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hi
s
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xt
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ha
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s
c
u
s
s
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d.
3.
2.
1.
Rand
om
f
or
e
s
t
r
e
g
r
e
s
s
o
r
r
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s
u
l
t
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hi
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c
t
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r
a
l
ke
y
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c
s
r
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l
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t
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to
h
o
us
e
h
o
l
d
po
we
r
c
o
n
s
u
m
pt
i
o
n,
i
nc
l
ud
i
ng
g
l
o
b
a
l
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c
t
i
ve
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r
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gl
o
b
a
l
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a
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r
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v
o
l
t
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ge
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gl
o
b
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l
_
i
n
t
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n
s
i
t
y
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ub
_
m
e
t
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r
i
n
g_1,
s
ub
_
m
e
t
e
r
i
n
g_2,
a
n
d
s
u
b
_
m
e
t
e
r
i
n
g_3.
T
h
e
pe
r
f
o
r
m
a
n
c
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t
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s
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m
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:
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y.
T
a
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e
1.
C
o
m
pa
r
i
s
o
n
o
f
DF
,
RF
,
a
n
d
VA
R
m
o
de
l
s
V
a
r
ia
bl
e
M
o
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M
S
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R
M
S
E
M
A
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R
2
s
c
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r
e
G
l
o
ba
l
a
c
t
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v
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w
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r
DF
0.000585
0.024198
0.014
0.999324
G
l
o
ba
l
a
c
t
i
v
e
p
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w
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RF
0.000574
0.023973
0.014
0.999337
G
l
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ba
l
a
c
t
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e
p
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w
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r
VAR
1.092
1.045010
0.805
-
G
l
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ba
l
r
e
a
c
ti
v
e
p
o
w
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r
DF
0.003824
0.061796
0.039
0.112670
G
l
o
ba
l
r
e
a
c
ti
v
e
p
o
w
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r
RF
0.003815
0.061752
0.039
0.114825
G
l
o
ba
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r
e
a
c
ti
v
e
p
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w
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r
VAR
0.014
0.117615
0.089
-
V
o
lt
a
g
e
DF
6.359017
2.514952
1.766
0.352550
V
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a
g
e
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6.488995
2.546218
1.789
0.339316
V
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a
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VAR
12.162
3.487420
2.763
-
G
l
o
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in
t
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ns
it
y
DF
0.011127
0.105594
0.068
0.999267
G
l
o
ba
l
in
t
e
ns
it
y
RF
0.011338
0.106485
0.068
0.999253
G
l
o
ba
l
in
t
e
ns
it
y
VAR
19.115
4.372051
3.331
-
S
ub
me
t
e
r
in
g 1
DF
10682.528
103.362360
27.006
0.800698
S
ub me
t
e
r
in
g 1
RF
11203.703
105.904540
27.538
0.790975
S
ub me
t
e
r
in
g 1
VAR
39.654
6.297160
2.127
-
S
ub me
t
e
r
in
g 2
DF
12800.620
113.143162
35.526
0.802282
S
ub me
t
e
r
in
g 2
RF
11460.213
107.084791
34.536
0.822986
S
ub me
t
e
r
in
g 2
VAR
30.836
5.552991
1.895
-
S
ub me
t
e
r
in
g 3
DF
9596.198
97.960208
34.015
0.951974
S
ub me
t
e
r
in
g 3
RF
8745.824
93.554123
32.926
0.956230
S
ub me
t
e
r
in
g 3
VAR
84.554
9.195301
8.336
-
s
ub me
te
r
in
g 4
DF
13011.987
114.034235
41.016
0.949388
s
ub me
te
r
in
g 4
RF
11876.662
108.974627
39.570
0.953804
s
ub me
te
r
in
g 4
VAR
76.739
8.760105
6.455
-
T
a
bl
e
2
.
C
o
m
pa
r
i
s
o
n
o
f
S
AR
I
M
A
X
m
o
de
l
V
a
r
ia
bl
e
M
S
E
R
M
S
E
R
2
s
c
o
r
e
G
l
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ba
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a
c
t
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v
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p
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w
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r
0.363479
0.602894
0.525965
G
l
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ba
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r
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a
c
ti
v
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p
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w
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r
0.011565
0.107556
-
V
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a
g
e
7.674620
2.771110
-
G
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t
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6.473471
2.544107
0.515130
S
ub me
t
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r
in
g 1
11.952510
3.457551
0.078765
4.
CONC
L
USI
ON
T
hi
s
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s
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hli
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e
l
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c
t
r
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c
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s
u
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a
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d
mi
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c
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s
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I
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c
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pr
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ns
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u
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da
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we
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ul
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e
m,”
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e
ur
o
Q
uant
ol
ogy
,
vo
l.
20,
n
o
.
6,
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7419,
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[
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.
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ts
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kl
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.
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,
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.
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or
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,
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nd
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.
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pe
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,”
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ne
r
gy
R
e
por
ts
, v
o
l.
8, pp. 1
–
6,
J
un. 2022, do
i:
10.1016/j
.
e
g
y
r
.2022.01.033.
[
11]
M
.
A
la
z
a
b,
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.
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ha
n,
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.
S
.
R
.
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n,
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.
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.
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.
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,
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nd
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.
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.
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a
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T
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E
E
E
A
c
c
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s
s
,
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ol
.
8,
pp.
85454
–
85463,
20
20,
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i:
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E
S
S
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[
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D
.
S
o
l
y
a
l
i,
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A
c
o
mpa
r
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ti
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na
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hi
ne
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in
C
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pr
us
,”
Sus
ta
in
abi
li
ty
(
Sw
it
z
e
r
la
nd)
,
vo
l.
12, n
o
. 9, p. 3612, Apr
. 2020,
do
i:
10.3390/
S
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12093612.
[
13]
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.
G
r
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ne
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I
nf
o
r
m
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s
, v
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l.
4, n
o
. S
3,
p. 13, S
e
p. 2021, d
o
i:
10.1186/s
42162
-
021
-
00172
-
6.
Evaluation Warning : The document was created with Spire.PDF for Python.
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.
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o
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ppl
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ne
r
gy
, vo
l.
282, p. 11
6249, J
a
n. 2021, do
i:
10.1016/j
.a
pe
n
e
r
g
y
.2020.116249.
[
15]
Z
.
Z
ha
ng,
W
.
C
.
H
o
ng,
a
nd
J
.
L
i,
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E
A
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s
s
,
vo
l.
8,
pp.
14642
–
14658,
2
020,
do
i:
10.1109/aC
C
E
S
S
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[
16]
H
.
A
pr
il
li
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,
H
.
T
.
Y
a
ng,
a
nd
C
.
M
.
H
ua
ng,
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ta
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E
T
r
ans
ac
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ons
on
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a
r
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G
r
id
,
v
o
l.
12,
no
.
2,
pp.
1467
–
1480,
M
a
r
.
20
21,
do
i:
10.1109/
T
S
G
.2020.3034194.
[
17]
G
.
F
.
F
a
n,
Y
.
H
.
G
u
o
,
J
.
M
.
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.
C
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o
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c
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s
ti
ng,”
J
our
nal
of
F
or
e
c
as
ti
ng
, v
o
l.
39, n
o
. 5, pp. 737
–
756, Aug. 2020,
do
i:
10.1002/
f
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[
18]
M
.
M
a
dhukuma
r
,
A
.
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e
ba
s
ti
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n,
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.
L
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ng,
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.
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.
N
.
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.
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E
E
E
A
c
c
e
s
s
, vo
l.
10,
pp. 8
891
–
8905, 2022, do
i:
10.1109/AC
C
E
S
S
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[
19]
J
.
D
in
g, M
. W
a
ng,
Z
.
P
in
g, D
.
F
u,
a
nd
V
.
S
.
V
a
s
s
il
ia
di
s
,
“
A
n
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ur
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J
our
nal
o
f
O
pe
r
at
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R
e
s
e
ar
c
h
,
v
o
l.
287,
n
o
.
2,
pp.
497
–
510,
D
e
c
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2020,
do
i:
10.1016/j
.
e
j
or
.2020.04.007.
[
20]
Z
.
W
a
ng,
X
.
Z
ho
u,
J
.
T
ia
n,
a
nd
T
.
H
ua
ng,
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H
i
e
r
a
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hi
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a
l
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lo
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or
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c
a
s
ti
ng,”
Sus
ta
in
abl
e
C
it
ie
s
and Soc
ie
ty
, v
o
l.
71, p. 10293
7,
A
ug. 2021, do
i:
10.1016/j
.s
c
s
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[
21]
G
. F
.
F
a
n,
L
. Z
.
Z
ha
ng, M
. Y
u, W
. C
.
H
o
ng,
a
nd S
. Q
. D
o
ng,
“
A
ppl
ic
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ti
o
ns
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our
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c
al
P
o
w
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and
E
ne
r
gy
Sy
s
te
m
s
,
v
o
l.
139,
p.
108073,
J
ul
.
20
22,
do
i:
10.1016/j
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ij
e
p
e
s
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[
22]
L
.
Y
in
,
Z
.
S
un,
F
.
G
a
o
,
a
nd
H
.
L
iu
,
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or
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te
ms
,”
I
E
E
E
A
c
c
e
s
s
,
vo
l.
8, pp. 49090
–
49099, 2020, d
o
i:
10.1109
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C
E
S
S
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79686.
[
23]
J
.
M
o
o
n,
S
.
J
ung,
J
.
R
e
w
,
S
.
R
ho
,
a
nd
E
.
H
w
a
ng,
“
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na
ti
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ki
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ns
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a
ppr
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a
c
h,”
E
ne
r
gy
and B
ui
ld
in
gs
, vo
l.
216, p. 10992
1, J
un. 2020, do
i:
10.1016/j
.
e
nbui
ld
.2020.109921.
[
24]
S
.
R
.
S
a
lk
ut
i,
P
.
R
a
y
,
a
nd
S
.
P
a
gi
di
pa
la
,
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w
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x
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ti
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ma
r
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gr
id
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in
L
e
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tu
r
e
N
ot
e
s
in
E
le
c
tr
ic
al
E
ngi
ne
e
r
i
ng
,
vo
l.
824, 2022, pp. 1
–
28.
[
25]
J
.
V
.
K
uma
r
,
H
.
S
e
s
ha
m,
a
nd
S
.
R
.
S
a
lk
ut
i,
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S
ma
r
t
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n
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r
g
y
ma
na
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me
nt
m
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l
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o
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l
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tr
i
c
ve
hi
c
l
e
s
,”
in
G
r
e
e
n
E
ne
r
gy
and
T
e
c
hnol
ogy
, vo
l.
P
a
r
t
F
2899, 2024, pp. 193
–
209.
[
26]
A
.
A
.
A
hme
d,
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o
us
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o
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o
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umpt
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n,”
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aggl
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,
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ht
tp
s
:/
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ggl
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.
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o
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da
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a
d (
a
c
c
e
s
s
e
d N
ov
. 24, 20
22
).
[
27]
L
.
K
r
is
hna
n,
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.
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uppus
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m
y
,
a
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.
S
.
A
ka
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ni
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a
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a
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ne
r
gy
E
ff
ic
ie
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y
,
vo
l.
16,
n
o
.
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p.
77,
O
c
t.
20
23,
do
i:
10.1007/s
12053
-
023
-
10155
-
z.
[
28]
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.
S
.
Y
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.
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.
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ng,
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.
Y
.
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n,
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.
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r
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r
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f
a
l
l
f
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r
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c
a
s
ti
ng,”
J
our
nal
of
H
y
dr
ol
ogy
,
v
o
l.
552,
pp.
92
–
104,
S
e
p.
2
017,
do
i:
10.1016/j
.
jh
y
dr
o
l.
2017.06.020.
[
29]
J
.
M
.
B
.
H
a
s
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