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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
:
2
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8
8
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7
0
8
I
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t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
1
,
Feb
r
u
ar
y
20
2
6
:
4
6
3
-
476
464
ir
r
ad
ian
ce
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ev
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s
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[
4
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[
1
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ical
f
ac
to
r
s
an
d
PV
o
u
t
p
u
ts
.
I
n
r
esp
o
n
s
e,
ar
tific
ial
in
tellig
en
ce
(
AI
)
an
d
m
ac
h
in
e
lear
n
in
g
(
ML
)
m
eth
o
d
s
,
in
clu
d
in
g
ar
tific
ial
n
eu
r
al
n
etwo
r
k
s
(
ANN)
,
s
u
p
p
o
r
t
v
ec
to
r
r
eg
r
ess
io
n
(
SVR
)
,
an
d
d
ee
p
lear
n
in
g
(
DL
)
m
o
d
els,
h
a
v
e
em
er
g
ed
as m
o
r
e
p
o
wer
f
u
l a
lter
n
ativ
es
[
1
5
]
,
[
1
6
]
.
A
n
o
tab
le
ad
v
a
n
ce
m
en
t
is
t
h
e
ad
o
p
tio
n
o
f
h
y
b
r
id
lea
r
n
in
g
s
tr
ateg
ies,
p
ar
ticu
la
r
ly
c
o
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
s
co
m
b
in
ed
w
ith
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
n
etwo
r
k
s
(
C
NN
–
L
STM
)
[
1
7
]
,
[
1
8
]
.
C
NN
lay
er
s
ex
tr
ac
t
s
p
atial
f
ea
tu
r
es
(
e.
g
.
,
c
lo
u
d
c
o
v
er
an
d
ir
r
ad
ian
ce
d
is
tr
ib
u
tio
n
)
,
wh
ile
L
STM
lay
er
s
ca
p
tu
r
e
s
h
o
r
t
-
ter
m
an
d
lo
n
g
-
ter
m
tem
p
o
r
al
d
y
n
a
m
ics.
R
ec
en
t
s
tu
d
ies
h
av
e
d
e
m
o
n
s
tr
ated
th
e
ef
f
ec
tiv
e
n
ess
o
f
h
y
b
r
id
m
o
d
els
f
o
r
PV
f
o
r
ec
asti
n
g
[
1
9
]
,
[
2
0
]
,
co
n
s
is
ten
tly
s
h
o
win
g
th
at
C
NN
–
L
STM
o
u
tp
er
f
o
r
m
s
s
tan
d
alo
n
e
AI
/ML
m
eth
o
d
s
in
p
r
ed
ictiv
e
ac
cu
r
ac
y
.
T
h
is
p
o
s
itio
n
s
C
NN
–
L
STM
as th
e
s
tate
o
f
th
e
ar
t i
n
AI
-
d
r
iv
en
PV f
o
r
e
ca
s
tin
g
.
Nev
er
th
eless
,
s
ev
er
al
ch
allen
g
es
h
in
d
er
wid
er
d
ep
l
o
y
m
en
t
.
A
m
ajo
r
lim
itatio
n
is
th
e
s
ca
r
city
o
f
h
ig
h
-
r
eso
lu
tio
n
d
atasets
co
m
b
in
in
g
m
eteo
r
o
lo
g
ical
an
d
P
V
g
en
er
atio
n
v
ar
iab
les,
wh
ic
h
ar
e
ess
en
tial
f
o
r
m
o
d
el
tr
ain
in
g
a
n
d
v
ali
d
atio
n
[
2
1
]
,
[
2
2
]
.
Fu
r
th
er
m
o
r
e,
m
a
n
y
AI
f
r
am
ew
o
r
k
s
s
tr
u
g
g
le
t
o
g
en
er
alize
ac
r
o
s
s
d
iv
er
s
e
clim
atic
co
n
d
itio
n
s
,
s
u
f
f
er
f
r
o
m
o
v
er
f
itti
n
g
wh
en
d
at
a
is
lim
ited
,
an
d
lack
m
ec
h
an
i
s
m
s
f
o
r
ad
a
p
tin
g
to
d
y
n
am
ic
r
ea
l
-
wo
r
ld
en
v
i
r
o
n
m
en
ts
[
2
3
]
–
[
2
5
]
.
An
o
th
er
p
e
r
s
is
ten
t
g
ap
is
th
e
lim
ited
in
teg
r
atio
n
o
f
co
n
s
u
m
e
r
b
eh
av
io
r
an
d
d
em
an
d
-
s
id
e
f
a
cto
r
s
in
to
f
o
r
ec
asti
n
g
m
o
d
els,
an
im
p
o
r
tan
t
asp
ec
t
f
o
r
en
e
r
g
y
m
a
n
ag
em
e
n
t
b
u
t
lar
g
ely
o
v
er
l
o
o
k
e
d
in
cu
r
r
en
t
C
NN
–
L
STM
s
tu
d
ies
[
1
9
]
.
T
h
ese
g
ap
s
lead
to
th
e
ce
n
tr
al
r
esear
ch
q
u
esti
o
n
s
o
f
th
is
r
ev
iew:
i)
h
o
w
ef
f
ec
tiv
e
ar
e
h
y
b
r
id
C
NN
–
L
STM
m
o
d
els
in
f
o
r
ec
asti
n
g
PV
p
r
o
d
u
ct
io
n
ef
f
icien
c
y
,
a
n
d
ii)
to
wh
at
ex
ten
t h
as c
o
n
s
u
m
e
r
b
eh
av
i
o
r
b
ee
n
in
teg
r
ate
d
in
to
f
o
r
ec
asti
n
g
f
r
am
ewo
r
k
s
?
I
n
lig
h
t
o
f
t
h
ese
ch
allen
g
e
s
,
th
is
s
tu
d
y
co
n
d
u
cts
a
s
y
s
tem
atic
r
ev
iew
o
f
6
9
p
e
er
-
r
ev
iewe
d
p
u
b
licatio
n
s
f
r
o
m
2
0
2
0
–
2
0
2
4
to
ass
ess
r
ec
en
t
ad
v
a
n
ce
m
en
ts
in
AI
-
d
r
iv
e
n
PV
f
o
r
ec
asti
n
g
[
2
5
]
,
with
a
s
p
ec
if
ic
em
p
h
asis
o
n
h
y
b
r
id
C
NN
–
L
STM
m
o
d
els
an
d
th
e
em
er
g
in
g
d
im
e
n
s
io
n
o
f
c
o
n
s
u
m
p
ti
o
n
in
teg
r
atio
n
.
T
h
e
r
ev
iew
ex
am
in
es
f
o
r
ec
asti
n
g
m
eth
o
d
s
,
d
ata
p
r
o
ce
s
s
in
g
tech
n
iq
u
es,
m
o
d
el
ar
ch
itectu
r
es,
p
er
f
o
r
m
a
n
ce
m
etr
ics,
an
d
ap
p
licatio
n
c
o
n
tex
ts
.
T
h
e
m
ain
c
o
n
tr
ib
u
tio
n
s
o
f
th
is
s
tu
d
y
a
r
e
th
r
ee
f
o
ld
:
i)
p
r
o
v
id
in
g
a
s
tr
u
ctu
r
ed
s
y
n
th
esis
o
f
h
y
b
r
id
C
NN
–
L
STM
m
o
d
els
ap
p
licatio
n
s
f
o
r
PV
f
o
r
ec
asti
n
g
,
ii)
id
en
tif
y
in
g
r
esear
c
h
g
a
p
s
,
p
ar
ticu
lar
ly
r
eg
ar
d
in
g
th
e
in
co
r
p
o
r
atio
n
o
f
co
n
s
u
m
p
tio
n
d
ata,
an
d
iii)
o
u
tlin
in
g
f
u
t
u
r
e
d
ir
ec
tio
n
s
f
o
r
d
ev
elo
p
in
g
h
y
b
r
id
m
o
d
els th
at
ar
e
m
o
r
e
ac
cu
r
ate,
g
en
er
aliza
b
le,
an
d
p
r
ac
tical
f
o
r
r
ea
l
-
wo
r
l
d
en
er
g
y
s
y
s
tem
s
.
T
h
e
n
o
v
elty
o
f
t
h
is
r
ev
iew
lies
in
its
d
u
al
co
n
tr
ib
u
tio
n
:
f
i
r
s
t,
it
co
n
s
o
lid
ates
r
ec
en
t
AI
-
b
ased
PV
f
o
r
ec
asti
n
g
r
esear
ch
th
r
o
u
g
h
a
tax
o
n
o
m
y
o
f
6
9
s
tu
d
ies
p
u
b
lis
h
ed
b
etwe
en
2
0
2
0
an
d
2
0
2
4
;
s
ec
o
n
d
,
it
h
ig
h
lig
h
ts
th
e
u
n
d
er
ex
p
lo
r
ed
b
u
t
cr
u
cial
in
teg
r
atio
n
o
f
co
n
s
u
m
er
b
eh
av
io
r
in
to
f
o
r
ec
asti
n
g
m
o
d
els,
wh
ich
is
v
ital f
o
r
alig
n
i
n
g
p
r
o
d
u
ctio
n
w
ith
d
em
an
d
an
d
e
n
s
u
r
in
g
s
u
s
tain
ab
le
en
er
g
y
m
an
a
g
em
en
t.
2.
M
E
T
H
O
D
T
h
i
s
r
e
v
i
e
w
a
p
p
l
i
e
d
a
s
t
r
u
c
t
u
r
e
d
a
p
p
r
o
a
c
h
t
o
a
s
s
es
s
A
I
m
e
t
h
o
d
s
i
n
f
o
r
e
c
a
s
t
i
n
g
s
o
l
a
r
P
V
p
er
f
o
r
m
a
n
c
e
.
T
h
e
p
r
o
c
e
d
u
r
e
w
a
s
a
l
i
g
n
e
d
w
i
t
h
P
R
I
S
M
A
2
0
2
0
t
o
e
n
s
u
r
e
t
r
a
n
s
p
a
r
e
n
c
y
a
n
d
r
e
p
r
o
d
u
c
i
b
i
l
i
t
y
,
w
h
i
l
e
a
d
d
i
t
i
o
n
al
m
e
a
s
u
r
e
s
w
e
r
e
i
n
t
r
o
d
u
c
e
d
t
o
r
e
d
u
c
e
b
i
a
s
a
n
d
s
t
r
e
n
g
t
h
e
n
t
h
e
r
o
b
u
s
t
n
e
s
s
o
f
t
h
e
s
y
n
t
h
e
s
is
.
T
h
e
f
o
l
l
o
w
i
n
g
s
u
b
s
e
ct
i
o
n
s
o
u
t
li
n
e
t
h
e
r
e
v
i
ew
d
e
s
i
g
n
,
s
o
u
r
c
e
s
e
l
e
ct
i
o
n
,
a
n
d
ap
p
r
a
i
s
a
l
p
r
o
c
e
d
u
r
e
s
i
n
m
o
r
e
d
e
t
a
i
l
.
2
.
1
.
Rev
iew
des
ig
n
Stan
d
ar
d
ap
p
r
o
ac
h
.
W
e
co
n
d
u
cted
a
s
y
s
tem
atic
r
ev
iew
in
ac
co
r
d
an
ce
with
PR
I
SMA
2
0
2
0
to
en
s
u
r
e
tr
an
s
p
ar
en
t
id
e
n
tific
atio
n
,
s
cr
ee
n
in
g
,
elig
ib
ilit
y
ass
ess
m
en
t,
an
d
in
clu
s
io
n
r
ep
o
r
tin
g
[
2
6
]
.
PR
I
SMA
is
th
e
d
e
fa
cto
s
tan
d
ar
d
f
o
r
r
e
p
r
o
d
u
cib
le
ev
id
e
n
ce
s
y
n
th
esis
an
d
m
in
im
izes
s
elec
tio
n
/r
ep
o
r
tin
g
b
ias
th
r
o
u
g
h
s
tr
u
ctu
r
ed
f
lo
w
r
ep
o
r
ti
n
g
an
d
a
p
r
io
r
i c
r
iter
ia.
No
v
el
ad
d
itio
n
s
tailo
r
e
d
to
th
i
s
to
p
ic.
a.
C
r
o
s
s
-
d
o
m
a
i
n
s
o
u
r
c
e
b
a
la
n
c
i
n
g
.
E
a
r
l
y
r
e
t
r
i
e
v
a
ls
w
e
r
e
s
k
e
w
ed
t
o
w
a
r
d
S
c
i
e
n
ce
D
i
r
e
ct
(
~
6
2
%
o
f
h
i
ts
)
,
r
is
k
i
n
g
d
o
m
a
i
n
b
i
a
s
t
o
wa
r
d
e
n
e
r
g
y
j
o
u
r
n
a
l
s
.
W
e
b
r
o
a
d
e
n
e
d
s
o
u
r
c
e
s
t
o
i
n
c
l
u
d
e
W
e
b
o
f
Sc
i
e
n
c
e
,
G
o
o
g
l
e
S
c
h
o
l
a
r
,
a
n
d
A
C
M
D
i
g
it
a
l
L
i
b
r
a
r
y
t
o
c
a
p
tu
r
e
h
i
g
h
-
i
m
p
a
c
t
i
n
t
e
r
d
is
c
i
p
l
i
n
a
r
y
o
u
t
l
e
t
s
a
n
d
A
I
/
M
L
v
e
n
u
e
s
.
T
h
is
i
m
p
r
o
v
es
c
o
v
e
r
a
g
e
o
f
m
e
a
s
u
r
e
m
e
n
t
/
c
o
n
tr
o
l
a
n
d
c
o
m
p
u
t
i
n
g
c
o
m
m
u
n
i
t
i
es
,
s
t
r
e
n
g
t
h
e
n
i
n
g
e
x
t
e
r
n
a
l
v
a
li
d
i
ty
[
2
7
]
–
[
3
6
]
.
b.
Op
er
atio
n
al
f
o
c
u
s
o
n
h
y
b
r
id
C
NN
–
L
STM
m
o
d
els
an
d
co
n
s
u
m
p
tio
n
in
teg
r
atio
n
.
B
ey
o
n
d
g
en
er
ic
AI
/ML
,
we
o
p
er
atio
n
ally
d
ef
in
e
d
wh
at
co
u
n
ts
as
h
y
b
r
id
C
NN
–
L
STM
m
o
d
els
an
d
as
u
s
er
co
n
s
u
m
p
tio
n
in
teg
r
atio
n
(
s
u
b
s
ec
tio
n
2
.
7
)
s
o
th
at
s
tu
d
ie
s
ar
e
class
if
ied
co
n
s
is
ten
tly
.
T
h
is
r
esp
o
n
d
s
to
g
ap
s
n
o
te
d
in
p
r
io
r
wo
r
k
an
d
en
ab
les d
ir
ec
t c
o
m
p
ar
ab
ilit
y
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
S
ystema
tic
r
ev
iew
o
f a
r
tifi
c
ia
l in
tellig
en
ce
a
p
p
lica
tio
n
s
in
p
r
ed
ictin
g
…
(
M.
R
iz
ki
I
kh
s
a
n
)
465
c.
B
ias
m
i
tig
atio
n
an
d
r
eliab
ilit
y
ch
ec
k
s
.
Du
al
in
d
ep
en
d
e
n
t
s
cr
ee
n
in
g
with
co
n
s
en
s
u
s
ad
ju
d
icatio
n
;
we
r
ec
o
r
d
e
d
r
ea
s
o
n
s
f
o
r
e
x
clu
s
io
n
at
f
u
ll
-
tex
t,
an
d
c
o
m
p
u
ted
i
n
ter
-
r
ater
ag
r
ee
m
en
t
(
C
o
h
e
n
’
s
κ)
to
d
o
cu
m
en
t
s
cr
ee
n
in
g
r
eliab
ilit
y
[
3
4
]
,
[
3
6
]
.
d.
Qu
ality
an
d
r
is
k
-
of
-
b
ias
ap
p
r
aisal.
W
e
ad
ap
ted
item
s
f
r
o
m
T
R
I
POD
(
r
ep
o
r
tin
g
o
f
p
r
e
d
ictiv
e
m
o
d
elin
g
s
tu
d
ies)
an
d
PR
OB
A
ST
(
r
is
k
-
of
-
b
ias
d
o
m
ain
s
)
to
th
e
P
V
-
f
o
r
ec
asti
n
g
c
o
n
tex
t
(
tim
e
-
a
war
e
v
alid
atio
n
,
leak
ag
e
ch
ec
k
s
,
b
aselin
e
co
m
p
ar
ato
r
s
)
.
T
h
is
u
n
d
er
p
i
n
s
th
e
v
a
lid
ity
o
f
th
e
q
u
alitativ
e
s
y
n
th
e
s
is
.
J
u
s
tific
atio
n
:
T
h
ese
ad
d
itio
n
s
ad
d
r
ess
k
n
o
wn
th
r
ea
ts
to
v
alid
ity
in
AI
r
ev
iews
—
s
o
u
r
c
e
b
ias,
in
co
n
s
is
ten
t
m
o
d
el
lab
elin
g
,
o
p
tim
is
tic
v
alid
atio
n
,
an
d
r
ev
iewe
r
s
u
b
jec
tiv
ity
—
th
er
eb
y
s
tr
en
g
th
en
i
n
g
co
n
s
tr
u
ct
v
alid
ity
,
in
ter
n
al
v
alid
ity
,
a
n
d
r
e
p
r
o
d
u
c
ib
ilit
y
.
2
.
2
.
I
nfo
r
m
a
t
io
n so
urce
s
W
e
q
u
er
ie
d
s
i
x
d
ata
b
as
es
wit
h
co
m
p
r
e
h
e
n
s
i
v
e
a
n
d
c
o
m
p
le
m
e
n
ta
r
y
c
o
v
e
r
a
g
e
:
I
E
E
E
X
p
l
o
r
e
,
Scie
n
c
eDi
r
e
ct
,
S
co
p
u
s
,
W
e
b
o
f
Sc
ie
n
c
e,
G
o
o
g
le
Sc
h
o
la
r
,
a
n
d
t
h
e
AC
M
D
ig
ita
l
L
i
b
r
a
r
y
[
2
6
]
–
[
3
8
]
.
T
h
is
s
ele
cti
o
n
e
n
s
u
r
es
b
r
o
ad
c
o
v
er
a
g
e
ac
r
o
s
s
el
ec
tr
ica
l
a
n
d
p
o
we
r
en
g
i
n
ee
r
i
n
g
,
r
e
n
e
wa
b
l
e
e
n
e
r
g
y
s
y
s
te
m
s
,
a
r
ti
f
i
cia
l
in
t
elli
g
en
ce
a
n
d
m
ac
h
i
n
e
l
ea
r
n
in
g
(
A
I
/
ML
)
,
a
n
d
i
n
t
er
d
is
c
ip
l
in
ar
y
r
esea
r
ch
d
o
m
ai
n
s
.
T
h
e
u
s
e
o
f
m
u
l
ti
p
le
d
at
a
b
ases
a
ls
o
r
e
d
u
ce
s
s
e
le
cti
o
n
b
i
as
a
n
d
i
n
c
r
e
ases
t
h
e
r
o
b
u
s
t
n
ess
a
n
d
r
e
p
r
o
d
u
c
ib
ilit
y
o
f
t
h
e
lite
r
a
tu
r
e
r
e
v
i
ew.
2
.
3
.
Sea
rc
h str
a
t
eg
y
W
e
co
m
b
in
ed
r
elev
an
t
k
ey
w
o
r
d
s
u
s
in
g
B
o
o
lean
o
p
er
ato
r
s
t
o
s
y
s
tem
atica
lly
r
etr
iev
e
s
tu
d
i
es
r
elate
d
to
AI
-
b
ased
p
h
o
to
v
o
ltaic
s
y
s
tem
s
.
T
h
e
b
ase
s
ea
r
ch
ex
p
r
ess
io
n
,
s
u
m
m
ar
ized
in
T
ab
le
1
,
was
d
esig
n
ed
to
ca
p
tu
r
e
v
a
r
iatio
n
s
in
ter
m
in
o
lo
g
y
r
elate
d
to
e
f
f
ec
tiv
en
es
s
,
p
er
f
o
r
m
an
ce
,
p
h
o
to
v
o
ltaic
tech
n
o
lo
g
ies,
an
d
ar
tific
ial
in
tellig
en
ce
o
r
m
ac
h
in
e
lear
n
in
g
ap
p
r
o
ac
h
es
ac
r
o
s
s
m
u
ltip
le
d
atab
ases
.
A
p
u
b
licatio
n
y
ea
r
f
ilter
(
2
0
2
0
–
2
0
2
4
)
was
ap
p
lied
at
t
h
e
q
u
er
y
s
tag
e
to
en
s
u
r
e
a
f
o
c
u
s
o
n
r
ec
en
t
d
ev
elo
p
m
en
ts
,
r
e
s
u
ltin
g
in
a
n
in
itial
p
o
o
l o
f
9
,
0
1
3
r
ec
o
r
d
s
co
llected
f
r
o
m
all
s
elec
ted
s
o
u
r
ce
s
.
T
ab
le
1
.
Sear
ch
ex
p
r
ess
io
n
u
tili
ze
d
in
th
e
s
y
s
tem
atic
r
ev
iew
D
a
t
a
b
a
s
e
Q
u
e
r
y
Y
e
a
r
o
f
p
u
b
l
i
c
a
t
i
o
n
S
c
o
p
u
s
,
S
c
i
e
n
c
e
D
i
r
e
c
t
,
I
EEE
X
p
l
o
r
e
,
W
e
b
o
f
S
c
i
e
n
c
e
,
G
o
o
g
l
e
S
c
h
o
l
a
r
,
A
C
M
D
i
g
i
t
a
l
L
i
b
r
a
r
y
“
(
Ef
f
e
c
t
i
v
e
n
e
ss)
”
O
R
“
(
P
e
r
f
o
r
ma
n
c
e
)
”
A
N
D
“
(
s
o
l
a
r
p
a
n
e
l
s)
”
O
R
“
(
p
h
o
t
o
v
o
l
t
a
i
c
)
”
A
N
D
“
(
M
a
c
h
i
n
e
Le
a
r
n
i
n
g
)
”
O
R
“
(
A
r
t
i
f
i
c
i
a
l
I
n
t
e
l
l
i
g
e
n
c
e
)
”
2
0
2
0
-
2
0
2
4
2
.
4
.
Study
s
elec
t
io
n
T
h
e
in
itial
d
atab
ase
s
ea
r
ch
y
ield
ed
a
to
tal
o
f
9
,
0
1
3
r
ec
o
r
d
s
ac
r
o
s
s
Sco
p
u
s
,
Scien
ce
Dir
ec
t,
I
E
E
E
Xp
lo
r
e,
W
eb
o
f
Scien
ce
,
G
o
o
g
le
Sch
o
lar
,
an
d
AC
M
Dig
ital
L
ib
r
ar
y
.
Af
ter
r
em
o
v
i
n
g
3
0
0
d
u
p
licates,
8
,
7
1
3
r
ec
o
r
d
s
r
em
ain
ed
f
o
r
t
itle
an
d
a
b
s
tr
ac
t
s
cr
ee
n
in
g
.
B
ased
o
n
th
e
p
r
ed
e
f
in
ed
in
clu
s
io
n
an
d
ex
clu
s
io
n
cr
iter
ia,
8
,
1
4
8
r
ec
o
r
d
s
wer
e
ex
clu
d
ed
,
leav
in
g
5
6
5
s
tu
d
ie
s
f
o
r
f
u
ll
-
tex
t
ass
ess
m
en
t.
Fo
llo
win
g
elig
ib
ilit
y
ch
ec
k
s
,
1
9
6
s
tu
d
ies
wer
e
ex
cl
u
d
ed
d
u
e
to
in
s
u
f
f
icien
t m
eth
o
d
o
lo
g
ical
r
i
g
o
r
,
ir
r
elev
an
ce
to
th
e
r
esear
ch
s
co
p
e
,
o
r
lack
o
f
ac
ce
s
s
ib
le
f
u
ll
-
tex
t,
r
esu
ltin
g
in
6
9
s
tu
d
ies th
at
wer
e
f
in
ally
in
clu
d
ed
in
th
e
s
y
s
tem
atic
r
ev
iew.
2
.
5
.
Q
ua
lit
y
a
pp
ra
is
a
l a
nd
risk
-
of
-
bia
s
T
h
e
m
eth
o
d
o
lo
g
ical
q
u
ality
o
f
ea
ch
s
tu
d
y
was
ass
es
s
ed
u
s
i
n
g
an
ad
ap
te
d
T
R
I
POD
an
d
PR
O
B
AST
f
r
am
ewo
r
k
,
f
o
cu
s
in
g
o
n
r
is
k
s
s
u
ch
as
d
ata
leak
ag
e,
lim
ited
t
em
p
o
r
al
v
alid
atio
n
,
lack
o
f
b
a
s
elin
e
co
m
p
ar
ato
r
s
,
o
v
er
f
itti
n
g
in
s
m
all
d
atasets
,
an
d
in
ad
e
q
u
ate
h
an
d
lin
g
o
f
m
i
s
s
in
g
v
alu
es.
Stu
d
ies
wer
e
r
ated
as
lo
w,
u
n
clea
r
,
o
r
h
ig
h
r
is
k
o
f
b
ias,
with
s
en
s
i
tiv
ity
ch
ec
k
s
ap
p
lied
to
d
o
wn
-
weig
h
t
o
r
e
x
clu
d
e
h
ig
h
-
r
is
k
ca
s
es.
B
y
co
m
b
in
in
g
PR
I
SMA
g
u
id
elin
es
with
T
R
I
POD/
PR
O
B
AST
ap
p
r
aisal,
th
is
r
ev
iew
en
s
u
r
es
tr
an
s
p
ar
en
cy
,
r
ig
o
r
,
an
d
r
ep
r
o
d
u
cib
ilit
y
,
th
er
e
b
y
s
tr
en
g
th
en
in
g
c
o
n
f
id
e
n
ce
in
th
e
s
y
n
t
h
esized
ev
id
en
ce
.
2
.
6
.
O
pera
t
io
na
l def
ini
t
io
ns
T
o
en
s
u
r
e
co
n
s
is
ten
t
co
d
in
g
an
d
co
m
p
a
r
ab
ilit
y
ac
r
o
s
s
s
tu
d
ies,
we
o
p
er
atio
n
alize
d
k
e
y
ter
m
s
as
f
o
llo
ws.
F
o
r
ec
a
s
ted
o
u
tco
me
r
ef
er
s
to
a
n
y
s
u
p
er
v
is
ed
tar
g
et
r
elate
d
to
PV
p
er
f
o
r
m
a
n
ce
,
i
n
clu
d
in
g
PV
p
o
we
r
(
W
,
k
W
,
MW),
en
er
g
y
(
W
h
,
k
W
h
)
,
o
r
m
o
d
u
le/p
lan
t
ef
f
icien
cy
(
%).
W
h
en
s
tu
d
ies
u
s
ed
d
if
f
er
en
t
u
n
its
,
v
alu
es
wer
e
n
o
r
m
alize
d
to
co
m
m
o
n
s
ca
les
d
u
r
in
g
ex
tr
ac
tio
n
to
all
o
w
s
id
e
-
by
-
s
id
e
in
te
r
p
r
etatio
n
[
3
6
]
–
[
3
9
]
F
o
r
ec
a
s
t
h
o
r
iz
o
n
s
wer
e
ca
teg
o
r
ized
a
s
n
o
wca
s
tin
g
(
≤
1
h
)
,
in
t
r
ad
ay
(
1
–
2
4
h
)
,
d
a
y
-
ah
ea
d
(
2
4
–
4
8
h
)
,
an
d
m
u
lti
-
d
ay
/wee
k
-
ah
ea
d
(
>4
8
h
)
,
a
n
d
temp
o
r
a
l
r
eso
lu
tio
n
ca
p
tu
r
ed
s
am
p
lin
g
r
ates
(
e.
g
.
,
1
–
5
m
in
,
1
0
–
1
5
m
in
,
h
o
u
r
l
y
,
d
aily
)
.
Meteo
r
o
lo
g
ica
l
in
p
u
ts
en
co
m
p
ass
ed
ir
r
ad
ian
ce
ter
m
s
—
g
lo
b
al
h
o
r
izo
n
tal
ir
r
ad
i
an
ce
(
GHI
)
,
d
ir
ec
t
n
o
r
m
al
ir
r
ad
ian
ce
(
DNI
)
,
a
n
d
d
if
f
u
s
e
h
o
r
izo
n
tal
ir
r
a
d
ian
ce
(
DI
F)
—
an
d
am
b
ien
t
v
ar
i
ab
les
(
tem
p
er
atu
r
e
,
h
u
m
id
ity
,
win
d
s
p
ee
d
/d
ir
ec
tio
n
,
an
d
r
ai
n
f
all/p
r
ec
ip
itatio
n
)
,
with
o
p
tio
n
al
ex
o
g
en
o
u
s
s
ig
n
als
s
u
ch
as
clea
r
n
ess
in
d
ex
o
r
s
k
y
im
a
g
er
y
/s
atellite
f
ea
tu
r
es wh
en
p
r
o
v
id
ed
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
1
,
Feb
r
u
ar
y
20
2
6
:
4
6
3
-
476
466
A
s
tu
d
y
was
lab
ele
d
h
y
b
r
i
d
C
NN
–
L
STM
wh
en
c
o
n
v
o
lu
tio
n
al
lay
er
s
wer
e
em
p
l
o
y
ed
to
e
x
tr
ac
t
s
p
atial/f
ea
tu
r
e
m
ap
s
(
f
r
o
m
g
r
i
d
d
ed
wea
th
er
,
s
k
y
im
ag
es,
o
r
en
g
in
ee
r
ed
tem
p
o
r
al
p
atch
es)
th
at
f
ed
a
r
ec
u
r
r
en
t
m
o
d
u
le
(
L
STM
/v
ar
ian
ts
)
f
o
r
tem
p
o
r
al
d
y
n
a
m
ics,
tr
ain
ed
en
d
-
to
-
en
d
o
r
in
a
s
tag
ed
f
ea
tu
r
e
→
s
eq
u
en
ce
p
ip
elin
e
[
1
6
]
–
[
1
8
]
.
Ar
ch
itectu
r
es
u
s
in
g
C
NN
with
o
u
t
r
ec
u
r
r
en
t
u
n
its
,
o
r
L
STM
/GR
U
wit
h
o
u
t
co
n
v
o
l
u
tio
n
al
f
ea
tu
r
e
ex
tr
ac
tio
n
,
wer
e
n
o
t
co
d
ed
as
C
NN
–
L
STM
h
y
b
r
id
s
.
C
o
n
s
u
m
p
tio
n
in
teg
r
atio
n
d
e
n
o
ted
ex
p
licit
u
s
e
o
f
u
s
er
lo
ad
o
r
d
e
m
an
d
p
atter
n
s
:
i)
as
ex
o
g
en
o
u
s
p
r
e
d
icto
r
s
f
o
r
PV
f
o
r
ec
asti
n
g
,
ii)
as
jo
i
n
t/co
-
tar
g
ets
in
m
u
lti
-
task
s
ettin
g
s
(
PV
an
d
lo
ad
p
r
e
d
icted
s
im
u
ltan
eo
u
s
ly
)
,
o
r
iii)
as
co
u
p
led
m
o
d
els
wh
er
e
co
n
s
u
m
p
tio
n
m
o
d
if
ies
PV f
o
r
ec
asts
v
ia
co
n
tr
o
l/d
is
p
a
tch
co
n
s
tr
ain
ts
.
B
aselin
es
in
clu
d
ed
p
er
s
is
ten
ce
/n
aïv
e
m
o
d
els,
au
to
r
eg
r
ess
iv
e
in
teg
r
ated
m
o
v
in
g
av
e
r
ag
e
(
A
R
I
MA
)
o
r
o
th
er
class
ical
s
tatis
tical
m
et
h
o
d
s
,
a
n
d
s
tan
d
a
r
d
ML
/DL
c
o
m
p
ar
ato
r
s
(
e.
g
.
,
SVR
,
R
F,
s
tan
d
alo
n
e
L
STM
)
.
Valid
atio
n
s
ch
em
es
wer
e
c
o
d
ed
as
tim
e
-
awa
r
e
h
o
ld
o
u
t
(
tr
ain
/v
alid
atio
n
/tes
t
in
ch
r
o
n
o
lo
g
ical
o
r
d
er
)
,
r
o
llin
g
/r
ec
u
r
s
iv
e
(
walk
-
f
o
r
war
d
)
ev
alu
atio
n
,
k
-
f
o
l
d
b
lo
ck
e
d
cr
o
s
s
v
alid
atio
n
,
an
d
cr
o
s
s
-
s
ite
o
r
cr
o
s
s
s
ea
s
o
n
g
en
er
aliza
tio
n
.
A
s
tu
d
y
was
f
lag
g
ed
f
o
r
p
o
ten
tial
leak
ag
e
i
f
an
y
s
tep
u
s
ed
f
u
tu
r
e
in
f
o
r
m
atio
n
in
tr
ain
in
g
o
r
s
ca
lin
g
(
e.
g
.
,
g
l
o
b
al
n
o
r
m
aliz
atio
n
o
n
th
e
f
u
ll
s
er
ies,
s
h
u
f
f
lin
g
tim
e
s
er
ies
with
o
u
t
p
r
eser
v
atio
n
o
f
o
r
d
er
)
.
Per
f
o
r
m
an
ce
m
etr
ics
f
o
llo
wed
au
th
o
r
r
ep
o
r
ts
b
u
t
wer
e
h
ar
m
o
n
ized
to
r
o
o
t
m
ea
n
s
q
u
ar
ed
e
r
r
o
r
(
R
MSE
)
,
m
ea
n
ab
s
o
lu
te
er
r
o
r
(
MA
E
)
,
m
ea
n
ab
s
o
lu
te
p
er
ce
n
tag
e
er
r
o
r
(
MA
PE)
an
d
R
²
wh
er
e
av
ailab
le
;
m
etr
ic
d
ef
in
itio
n
s
wer
e
r
etain
ed
as
s
tated
b
y
th
e
o
r
ig
in
al
s
tu
d
ies,
an
d
an
y
u
n
it
co
n
v
er
s
io
n
s
wer
e
lo
g
g
ed
in
t
h
e
ex
tr
ac
tio
n
s
h
ee
t
[
3
7
]
,
[
3
8
]
.
2
.
7
.
Ste
p
-
by
-
s
t
ep
pro
ce
du
re
T
h
e
liter
atu
r
e
s
ea
r
ch
was
co
n
d
u
cted
ac
r
o
s
s
I
E
E
E
Xp
lo
r
e
,
Scien
ce
Dir
ec
t,
Sco
p
u
s
,
W
eb
o
f
Scien
ce
,
Go
o
g
le
Sch
o
lar
,
a
n
d
th
e
AC
M
Dig
ital
L
ib
r
ar
y
f
r
o
m
2
8
F
eb
r
u
ar
y
2
0
2
2
,
with
an
u
p
d
ate
in
Dec
em
b
er
2
0
2
4
,
u
s
in
g
th
e
p
r
ed
ef
i
n
ed
B
o
o
lean
s
tr
in
g
d
escr
ib
ed
in
Sectio
n
2
.
3
(
with
f
ield
tag
s
ad
ju
s
ted
p
er
d
atab
ase)
an
d
a
p
u
b
licatio
n
y
ea
r
f
ilter
o
f
2
0
2
0
–
2
0
2
4
.
Sear
ch
r
esu
lts
(
titl
e,
ab
s
tr
ac
t,
k
ey
wo
r
d
s
,
an
d
DOI
)
w
er
e
ex
p
o
r
ted
i
n
R
I
S
an
d
C
SV
f
o
r
m
ats,
m
er
g
ed
,
a
n
d
d
e
-
d
u
p
licated
u
s
in
g
DOI
m
atch
in
g
,
ex
ac
t
-
titl
e
m
atch
i
n
g
,
an
d
f
u
zz
y
-
titl
e
s
im
ilar
ity
(
th
r
esh
o
ld
≈
0
.
9
0
)
,
f
o
llo
wed
b
y
m
an
u
al
v
er
if
ica
tio
n
,
r
esu
ltin
g
in
th
e
r
em
o
v
a
l
o
f
3
0
0
d
u
p
licate
r
ec
o
r
d
s
.
T
itle a
n
d
ab
s
tr
ac
t scr
ee
n
in
g
wer
e
p
er
f
o
r
m
ed
b
ased
o
n
th
e
p
r
ed
ef
in
e
d
in
clu
s
io
n
an
d
ex
clu
s
io
n
cr
iter
ia,
y
ield
in
g
5
6
5
r
ec
o
r
d
s
f
o
r
f
u
ll
-
tex
t
ass
ess
m
en
t;
s
u
b
s
eq
u
en
tly
,
1
9
6
s
tu
d
ies
wer
e
e
x
clu
d
e
d
with
d
o
c
u
m
en
ted
r
ea
s
o
n
s
,
in
clu
d
in
g
o
u
t
-
of
-
s
co
p
e
co
n
te
n
t,
in
s
u
f
f
icien
t
m
eth
o
d
o
lo
g
ical
r
e
p
o
r
tin
g
,
o
r
lim
ite
d
ac
ce
s
s
ib
ilit
y
.
T
h
e
r
em
ain
in
g
6
9
s
tu
d
ies
p
r
o
ce
ed
ed
to
d
ata
ex
tr
ac
ti
o
n
u
s
in
g
a
s
tan
d
ar
d
ized
f
o
r
m
(
s
ec
tio
n
2
.
5
)
,
i
n
d
ep
e
n
d
en
tl
y
co
n
d
u
cte
d
b
y
two
r
e
v
iewe
r
s
,
with
d
is
ag
r
ee
m
en
ts
r
eso
lv
ed
th
r
o
u
g
h
co
n
s
en
s
u
s
o
r
c
o
n
s
u
l
tatio
n
with
a
th
ir
d
r
ev
iewe
r
wh
en
n
ec
ess
ar
y
.
E
ac
h
in
clu
d
e
d
s
tu
d
y
u
n
d
er
wen
t
q
u
ality
ass
ess
m
en
t
an
d
r
is
k
-
of
-
b
ias
ap
p
r
aisal
u
s
in
g
a
tax
o
n
o
m
y
-
o
r
ien
ted
ev
al
u
atio
n
f
r
am
ew
o
r
k
c
o
m
m
o
n
ly
ad
o
p
ted
in
AI
-
b
ased
s
y
s
tem
atic
r
ev
iews,
f
o
cu
s
in
g
o
n
tim
e
-
awa
r
e
v
alid
atio
n
,
d
ata
l
ea
k
ag
e
p
r
e
v
en
tio
n
,
b
aselin
e
co
m
p
ar
ato
r
s
,
o
v
er
f
itti
n
g
r
is
k
s
,
an
d
m
is
s
in
g
d
ata
h
an
d
lin
g
[
3
4
]
.
T
h
e
o
v
er
all
r
ev
iew
p
r
o
ce
s
s
f
o
llo
wed
PR
I
SMA
b
ased
r
ep
o
r
tin
g
p
r
ac
tices
to
en
s
u
r
e
tr
an
s
p
ar
en
cy
an
d
r
e
p
r
o
d
u
cib
i
lity
[
3
6
]
.
All
s
ea
r
c
h
q
u
er
ies,
tim
estam
p
s
,
PR
I
SMA
f
lo
w
co
u
n
ts
,
ex
tr
ac
tio
n
s
h
ee
ts
,
an
d
ap
p
r
aisal
f
o
r
m
s
wer
e
ar
ch
iv
ed
as
s
u
p
p
lem
en
t
ar
y
m
ater
ials
to
s
u
p
p
o
r
t
f
u
ll
r
ep
licatio
n
o
f
th
e
r
ev
iew.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
s
y
s
tem
atic
r
ev
iew
an
aly
ze
d
6
9
ar
ticles,
wh
ich
w
er
e
s
elec
ted
f
r
o
m
a
n
in
itial
p
o
o
l
o
f
9
,
0
1
3
r
ec
o
r
d
s
r
etr
iev
e
d
f
r
o
m
Scien
ce
Dir
ec
t
(
4
0
%),
I
E
E
E
Xp
lo
r
e
(
2
0
%),
Sco
p
u
s
(
1
5
%),
W
eb
o
f
Scien
ce
(
1
0
%),
Go
o
g
le
Sch
o
lar
(
1
0
%)
,
an
d
th
e
AC
M
Dig
ital
L
ib
r
a
r
y
(
5
%),
af
te
r
s
cr
ee
n
i
n
g
f
o
r
d
u
p
licates,
r
elev
an
ce
,
an
d
ac
ce
s
s
ib
ilit
y
,
s
ee
Fig
u
r
e
s
1
to
4
.
T
h
e
s
elec
ted
ar
ticles,
p
u
b
lis
h
ed
b
etwe
en
2
0
2
0
an
d
2
0
2
4
,
r
e
p
r
esen
t g
lo
b
al
r
esear
ch
ef
f
o
r
ts
s
p
an
n
in
g
3
0
co
u
n
t
r
ies,
with
C
h
in
a
co
n
tr
ib
u
tin
g
t
h
e
lar
g
est
n
u
m
b
er
o
f
p
u
b
licatio
n
s
(
1
9
ar
ticles),
f
o
llo
wed
b
y
th
e
Un
ited
States
(
4
)
,
an
d
I
r
an
,
Mo
r
o
cc
o
,
So
u
th
Ko
r
ea
,
Au
s
tr
alia,
an
d
th
e
Un
ited
Kin
g
d
o
m
(
3
ar
ticles
ea
ch
)
.
T
h
e
r
esu
lts
in
d
icate
s
u
b
s
t
an
tial
p
r
o
g
r
ess
in
AI
-
d
r
iv
e
n
s
o
lar
p
h
o
to
v
o
ltaic
f
o
r
ec
asti
n
g
,
p
ar
ticu
la
r
ly
th
r
o
u
g
h
h
y
b
r
id
m
o
d
elin
g
ap
p
r
o
ac
h
es,
wh
ich
co
n
s
is
ten
tly
o
u
tp
er
f
o
r
m
s
tan
d
alo
n
e
ML
an
d
DL
m
eth
o
d
s
,
in
lin
e
with
r
ec
en
t
s
tu
d
ies
r
e
p
o
r
te
d
in
2
0
2
3
an
d
2
0
2
4
.
A
s
tr
u
ctu
r
e
d
a
n
aly
s
is
b
ased
o
n
s
o
u
r
ce
in
d
ex
,
a
u
th
o
r
s
’
n
atio
n
ality
,
a
n
d
p
r
im
ar
y
m
eth
o
d
o
lo
g
ical
c
ateg
o
r
ies
is
p
r
esen
ted
an
d
d
i
s
cu
s
s
ed
in
d
etail
in
s
u
b
s
ec
tio
n
3
.
1
,
with
t
h
e
r
esu
lt
s
s
u
m
m
ar
ized
in
th
e
c
o
r
r
esp
o
n
d
in
g
f
ig
u
r
es
.
3
.
1
.
Resul
t
s
by
s
o
urce
ind
ex
es,
na
t
io
na
lity
,
a
nd
m
e
t
ho
do
lo
g
ica
l c
a
t
e
g
o
ries
T
h
e
s
tu
d
y
s
elec
tio
n
was
co
n
d
u
cte
d
in
ac
co
r
d
an
ce
wit
h
th
e
PR
I
SMA
f
r
am
ewo
r
k
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
S
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r
ev
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f a
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tifi
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R
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kh
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th
s
o
f
ML
an
d
DL
,
wer
e
id
en
tifie
d
in
s
ix
a
r
ticles an
d
co
n
s
is
ten
tly
o
u
tp
er
f
o
r
m
ed
s
tan
d
alo
n
e
a
p
p
r
o
ac
h
es.
Fo
r
ex
am
p
le,
s
tu
d
ies
[
1
9
]
an
d
[
2
0
]
r
ep
o
r
ted
th
at
C
NN
–
L
STM
m
o
d
els
ac
h
iev
ed
u
p
to
1
5
%
lo
wer
R
MSE
in
PV
p
o
wer
p
r
ed
ictio
n
co
m
p
ar
ed
with
in
d
iv
id
u
al
L
S
T
M
o
r
C
NN
m
o
d
els b
y
j
o
in
tly
lear
n
in
g
s
p
atial
an
d
tem
p
o
r
al
ch
ar
ac
ter
is
tics
.
Fig
u
r
e
5
.
T
a
x
o
n
o
m
y
liter
atu
r
e
r
ev
iew
o
f
r
esear
ch
PV p
r
ed
ict
io
n
B
ey
o
n
d
s
tr
u
ctu
r
al
class
if
icati
o
n
,
th
e
d
ev
elo
p
ed
tax
o
n
o
m
y
also
h
ig
h
lig
h
ts
th
e
tem
p
o
r
al
ev
o
lu
tio
n
o
f
f
o
r
ec
asti
n
g
m
eth
o
d
s
.
E
ar
l
y
s
tu
d
ies
(
2
0
2
0
–
2
0
2
1
)
p
r
im
ar
ily
r
elied
o
n
co
n
v
en
tio
n
a
l
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
es,
d
u
e
t
o
th
eir
lo
wer
co
m
p
u
tatio
n
al
d
em
an
d
s
an
d
s
tab
le
p
er
f
o
r
m
a
n
ce
o
n
lim
ited
d
ataset
s
[
4
3
]
–
[
4
5
]
.
Fro
m
2
0
2
2
to
2
0
2
4
,
r
esear
ch
h
as
in
cr
ea
s
in
g
ly
s
h
if
ted
to
war
d
d
ee
p
lear
n
in
g
an
d
h
y
b
r
id
ar
ch
itectu
r
es,
d
r
iv
en
b
y
en
h
a
n
ce
d
co
m
p
u
tatio
n
al
ca
p
ab
ilit
ies
an
d
th
e
av
ailab
ilit
y
o
f
h
ig
h
e
r
-
r
eso
lu
tio
n
m
ete
o
r
o
lo
g
ical
an
d
PV
d
atasets
.
Hy
b
r
id
m
o
d
els,
s
u
ch
as
C
N
N
–
L
STM
an
d
NARX
-
b
ased
ap
p
r
o
ac
h
es,
h
av
e
b
ee
n
wid
ely
ad
o
p
ted
to
o
v
er
co
m
e
th
e
lim
itatio
n
s
o
f
s
tan
d
alo
n
e
m
o
d
els
in
ca
p
tu
r
i
n
g
n
o
n
lin
ea
r
d
y
n
am
ics
an
d
co
m
p
lex
tem
p
o
r
al
–
s
p
atial
d
ep
en
d
en
cies
[
1
9
]
,
[
2
0
]
,
[
4
6
]
,
[
4
7
]
.
T
h
is
tr
e
n
d
alig
n
s
with
b
r
o
ad
e
r
d
e
v
elo
p
m
e
n
ts
in
AI
-
d
r
iv
e
n
PV
f
o
r
ec
asti
n
g
,
wh
er
e
m
o
d
el
in
teg
r
atio
n
h
as
b
ec
o
m
e
a
k
e
y
s
tr
ateg
y
f
o
r
im
p
r
o
v
in
g
b
o
t
h
r
o
b
u
s
tn
ess
an
d
g
en
er
aliza
tio
n
p
er
f
o
r
m
an
ce
[
4
8
]
–
[
5
4
]
.
I
n
o
t
h
er
wo
r
d
s
,
co
m
b
in
in
g
m
u
ltip
le
a
p
p
r
o
a
ch
es
is
in
cr
ea
s
in
g
ly
s
ee
n
as
m
o
r
e
ef
f
ec
tiv
e
th
an
r
ely
in
g
o
n
a
s
in
g
le
m
o
d
el.
3
.
3
.
Dis
cus
s
io
n o
f
k
ey
f
ind
ing
s
Ou
r
f
in
d
in
g
s
in
d
icate
th
at
h
y
b
r
id
m
o
d
els,
p
ar
ticu
lar
ly
C
NN
–
L
STM
ar
ch
itectu
r
es,
co
n
s
is
ten
tly
en
h
an
ce
PV
f
o
r
ec
asti
n
g
ac
cu
r
ac
y
b
y
ef
f
ec
tiv
el
y
ca
p
t
u
r
in
g
b
o
th
s
p
atial
an
d
tem
p
o
r
al
c
h
a
r
ac
ter
is
tics
o
f
s
o
lar
p
o
wer
d
ata.
T
h
is
o
b
s
er
v
ati
o
n
alig
n
s
with
th
e
g
e
n
er
ali
za
tio
n
an
aly
s
is
r
ep
o
r
ted
b
y
C
o
s
ta
[
5
5
]
,
wh
o
d
em
o
n
s
tr
ated
t
h
e
r
o
b
u
s
tn
ess
o
f
co
n
v
o
lu
tio
n
al
–
L
STM
n
etwo
r
k
s
f
o
r
h
o
u
s
eh
o
ld
PV
f
o
r
ec
ast
in
g
,
as
well
as
with
r
ec
en
t
d
ee
p
lear
n
i
n
g
–
b
ased
s
p
atio
tem
p
o
r
al
ap
p
r
o
ac
h
es
in
teg
r
atin
g
f
r
eq
u
en
cy
tim
e
r
e
p
r
esen
tatio
n
s
an
d
n
eu
r
a
l
n
etwo
r
k
s
[
4
0
]
.
Hy
b
r
id
ar
ch
it
ec
tu
r
es
h
elp
ad
d
r
ess
th
e
lim
itatio
n
s
o
f
tr
ad
itio
n
al
s
tatis
tic
al
m
o
d
els
s
u
ch
as
AR
I
MA
,
wh
ich
s
tr
u
g
g
le
with
n
o
n
lin
ea
r
d
y
n
am
ics,
an
d
s
tan
d
alo
n
e
ar
tific
ial
in
tellig
en
c
e
m
eth
o
d
s
s
u
ch
as
ANN,
wh
ich
m
ay
e
x
h
ib
it
o
v
e
r
f
itti
n
g
wh
en
ap
p
lied
to
c
o
m
p
lex
an
d
h
ig
h
-
d
im
en
s
io
n
al
PV
d
atasets
[
1
4
]
,
[
1
8
]
,
[
2
3
]
,
[
5
2
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
1
,
Feb
r
u
ar
y
20
2
6
:
4
6
3
-
476
470
T
h
e
ef
f
ec
tiv
en
ess
o
f
C
NN
–
L
STM
m
o
d
els
p
r
im
ar
ily
s
ys
tem
s
f
r
o
m
th
eir
ab
ilit
y
to
j
o
in
tly
ex
tr
ac
t
s
p
atial
f
ea
tu
r
es
s
u
ch
as
clo
u
d
m
o
v
em
en
t
p
atter
n
s
an
d
ir
r
ad
ian
ce
d
is
tr
ib
u
tio
n
s
th
r
o
u
g
h
co
n
v
o
lu
tio
n
al
lay
er
s
,
wh
ile
s
im
u
ltan
eo
u
s
ly
m
o
d
eli
n
g
tem
p
o
r
al
d
ep
en
d
en
cies
in
PV
o
u
tp
u
t
u
s
in
g
L
STM
u
n
i
ts
.
Acr
o
s
s
m
u
ltip
l
e
s
tu
d
ies
p
u
b
lis
h
ed
b
etwe
en
2
0
2
1
a
n
d
2
0
2
4
,
t
h
is
s
p
atio
tem
p
o
r
al
lear
n
in
g
ca
p
ab
ilit
y
r
e
s
u
lted
in
co
n
s
is
ten
t
p
er
f
o
r
m
an
ce
g
ain
s
,
with
r
e
p
o
r
ted
r
e
d
u
ctio
n
s
o
f
a
p
p
r
o
x
im
ately
1
5
%
in
R
MSE
co
m
p
ar
ed
to
c
o
n
v
e
n
tio
n
al
m
ac
h
in
e
lear
n
in
g
b
aselin
es
[
1
9
]
,
[
2
0
]
,
[
2
4
]
,
[
4
7
]
,
[
4
9
]
,
[
5
4
]
–
[
5
6
]
.
Fo
r
ex
am
p
le,
So
u
h
aila
an
d
Mo
h
am
ed
[
2
]
r
ep
o
r
ted
a
n
R
²
v
alu
e
o
f
ap
p
r
o
x
im
ately
9
7
%
u
s
in
g
e
n
s
em
b
le
an
d
h
y
b
r
id
lea
r
n
in
g
s
tr
ateg
ies,
u
n
d
er
s
co
r
in
g
th
e
p
o
ten
tial o
f
s
u
c
h
m
o
d
els f
o
r
a
cc
u
r
ate
en
er
g
y
p
la
n
n
in
g
an
d
g
r
id
-
lev
el
d
ec
is
io
n
s
u
p
p
o
r
t.
T
h
e
r
o
b
u
s
tn
ess
o
f
th
ese
f
in
d
in
g
s
is
f
u
r
th
er
s
u
p
p
o
r
ted
b
y
th
e
d
iv
er
s
ity
o
f
d
ata
b
ases
co
n
s
id
er
ed
in
th
is
r
ev
iew.
B
y
in
c
o
r
p
o
r
atin
g
I
E
E
E
Xp
lo
r
e
an
d
th
e
AC
M
Dig
ita
l
L
ib
r
ar
y
f
o
r
AI
-
f
o
cu
s
ed
s
tu
d
i
es,
W
eb
o
f
Scien
ce
f
o
r
in
ter
d
is
cip
lin
ar
y
r
esear
ch
,
an
d
Go
o
g
le
Sch
o
lar
f
o
r
b
r
o
a
d
er
co
v
er
a
g
e
o
f
o
p
e
n
-
ac
ce
s
s
p
u
b
licatio
n
s
,
a
wid
e
r
an
g
e
o
f
m
eth
o
d
o
lo
g
ical
p
er
s
p
ec
tiv
es
was
ca
p
tu
r
ed
.
Nev
er
th
eless
,
p
o
ten
tial
s
elec
t
io
n
b
ias
r
em
ain
s
d
u
e
to
th
e
em
p
h
asis
o
n
E
n
g
lis
h
-
lan
g
u
ag
e,
p
ee
r
-
r
e
v
iewe
d
liter
atu
r
e,
w
h
ich
m
ay
lim
it
th
e
in
clu
s
io
n
o
f
r
elev
an
t
s
tu
d
ies
f
r
o
m
u
n
d
er
r
ep
r
esen
ted
r
eg
io
n
s
s
u
ch
as L
atin
Am
er
ica,
Af
r
ica
,
an
d
th
e
Mid
d
le
E
ast.
T
o
co
n
s
o
lid
ate
th
ese
o
b
s
er
v
atio
n
s
,
T
ab
le
2
p
r
o
v
id
es
a
c
o
m
p
ar
ativ
e
s
u
m
m
a
r
y
o
f
r
ep
r
esen
tativ
e
s
tu
d
ies,
o
u
tlin
in
g
th
e
ap
p
lie
d
m
eth
o
d
s
an
d
th
e
b
est
-
p
e
r
f
o
r
m
in
g
alg
o
r
ith
m
s
r
ep
o
r
ted
in
ea
ch
ca
s
e.
T
h
e
r
ev
iewe
d
wo
r
k
s
en
c
o
m
p
ass
co
n
v
en
tio
n
al
m
ac
h
in
e
lear
n
in
g
,
d
ee
p
lear
n
in
g
,
an
d
h
y
b
r
id
m
o
d
elin
g
s
tr
ateg
ies,
ev
alu
ated
u
s
in
g
s
tan
d
a
r
d
p
e
r
f
o
r
m
a
n
ce
m
et
r
ics
in
clu
d
in
g
R
MSE
,
MA
E
,
MA
PE,
a
n
d
th
e
c
o
ef
f
icien
t
o
f
d
eter
m
in
atio
n
(
R
²)
.
As
s
u
m
m
ar
ized
in
T
ab
le
2
,
h
y
b
r
i
d
m
o
d
els
p
ar
ticu
lar
ly
th
o
s
e
in
teg
r
atin
g
co
n
v
o
l
u
tio
n
al
n
eu
r
al
n
etwo
r
k
s
(
C
NN)
with
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
L
S
T
M)
ar
e
m
o
s
t
f
r
eq
u
en
tly
id
e
n
tifie
d
as
ac
h
iev
in
g
s
u
p
er
io
r
f
o
r
ec
asti
n
g
p
er
f
o
r
m
an
ce
[
1
9
]
,
[
2
0
]
,
[
4
1
]
,
[
4
7
]
,
[
5
5
]
,
[
5
6
]
.
I
n
ad
d
itio
n
,
s
ev
er
al
s
tu
d
ies
r
ep
o
r
t
co
m
p
etitiv
e
r
esu
lts
u
s
in
g
a
d
v
an
ce
d
en
s
em
b
le
an
d
g
r
a
d
ien
t
-
b
o
o
s
tin
g
f
r
am
ewo
r
k
s
,
s
u
ch
as
co
m
b
in
ed
L
ig
h
tGB
M
–
XGBo
o
s
t
m
o
d
els
[
4
1
]
,
as
well
as
g
r
ap
h
-
e
n
h
an
ce
d
L
STM
ap
p
r
o
ac
h
es
in
co
r
p
o
r
atin
g
m
u
lti
-
m
eteo
r
o
lo
g
ical
d
ep
en
d
en
cies
[
3
8
]
C
o
llectiv
ely
,
th
ese
f
i
n
d
in
g
s
r
ein
f
o
r
ce
t
h
e
g
r
o
win
g
ad
o
p
tio
n
o
f
h
y
b
r
id
an
d
en
h
an
ce
d
d
ee
p
lear
n
in
g
m
o
d
el
s
in
p
h
o
to
v
o
ltaic
p
o
wer
p
r
ed
ic
tio
n
.
T
ab
le
2
.
Su
m
m
a
r
izes r
ep
r
esen
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.
[
1
9
]
(
2
0
2
2
)
C
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[
3
8
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(
2
0
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1
)
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[
2
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(
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M
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4
1
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4
7
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2
0
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4
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r
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elate
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o
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6
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ly
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m
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atter
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im
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ily
th
r
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g
h
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r
am
ewo
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k
s
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s
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ch
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t
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C
NN
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STM
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ased
ap
p
r
o
ac
h
r
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ted
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y
Ag
g
a
et
a
l.
[
1
9
]
.
T
h
is
lim
itatio
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is
s
ig
n
if
ican
t,
as
u
s
er
co
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eh
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l
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PV
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y
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tem
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icien
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g
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en
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s
tu
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tr
ate
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r
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cu
r
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;
f
o
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x
am
p
le,
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k
in
ci
[
5
6
]
r
ep
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r
ted
a
n
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r
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ed
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ctio
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f
ap
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ately
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ld
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wer
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ated
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L
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ased
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o
r
ec
asti
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g
m
o
d
els.
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v
id
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ce
f
r
o
m
r
e
s
id
en
tial
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d
m
icr
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g
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y
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tu
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ies
f
u
r
th
er
s
u
p
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ts
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e
im
p
o
r
ta
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ce
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f
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eh
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m
o
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elin
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as
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er
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lo
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im
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ac
tf
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l
co
m
p
o
n
e
n
t
o
f
PV
p
r
ed
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n
r
esear
ch
[
7
]
,
[
8
]
.
T
h
e
g
e
o
g
r
a
p
h
ical
d
is
tr
ib
u
tio
n
o
f
PV
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o
r
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asti
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g
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tu
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ies
r
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ls
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r
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n
o
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ce
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r
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is
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ar
ities
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m
in
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tly
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ce
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tr
ated
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ev
elo
p
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g
io
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s
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t
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tio
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em
ai
n
lim
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u
e
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ch
allen
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elate
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ailab
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co
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p
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tatio
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in
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r
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g
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ali
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f
o
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m
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ce
[
3
7
]
,
[
4
6
]
,
[
5
2
]
.
Alth
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g
h
b
r
o
ad
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atab
ase
co
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r
ag
e
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r
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m
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d
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p
r
esen
ted
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io
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,
th
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f
in
d
in
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s
h
ig
h
lig
h
t
th
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n
ee
d
f
o
r
im
p
r
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v
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in
ter
n
atio
n
al
co
lla
b
o
r
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n
an
d
d
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s
h
ar
in
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in
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to
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s
tn
ess
ac
r
o
s
s
d
iv
er
s
e
clim
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d
itio
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s
.
Desp
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th
eir
s
u
p
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io
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p
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d
ictiv
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ac
cu
r
ac
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y
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d
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ly
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esen
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p
r
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ch
allen
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with
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p
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ex
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Sev
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s
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g
g
est
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N:
2088
-
8
7
0
8
S
ystema
tic
r
ev
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o
f a
r
tifi
c
ia
l in
tellig
en
ce
a
p
p
lica
tio
n
s
in
p
r
ed
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g
…
(
M.
R
iz
ki
I
kh
s
a
n
)
471
co
m
b
in
in
g
d
ee
p
lea
r
n
in
g
with
p
h
y
s
ical
m
o
d
elin
g
o
r
o
p
tim
izatio
n
tech
n
iq
u
es
to
im
p
r
o
v
e
d
ep
lo
y
ab
ilit
y
in
r
ea
l
-
wo
r
ld
en
e
r
g
y
s
y
s
tem
s
[
4
5
]
,
[
4
9
]
.
C
o
n
s
eq
u
e
n
tly
,
f
u
tu
r
e
r
esear
ch
s
h
o
u
ld
p
r
io
r
itize
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h
e
d
ev
elo
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m
en
t
o
f
lig
h
tweig
h
t
h
y
b
r
id
ar
c
h
itectu
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es,
m
o
d
el
co
m
p
r
ess
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s
tr
a
teg
ies,
an
d
d
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am
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o
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p
ab
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o
f
s
u
p
p
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g
r
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.
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er
all,
th
e
f
in
d
in
g
s
co
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f
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m
th
at
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y
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r
id
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lear
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o
d
els
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s
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s
tan
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m
en
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elate
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to
d
em
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s
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d
ata
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r
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r
eg
io
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g
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aliza
b
ilit
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,
a
n
d
c
o
m
p
u
tatio
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al
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f
icien
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m
e
r
g
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n
g
p
h
y
s
ics
-
in
f
o
r
m
e
d
lear
n
i
n
g
[
4
5
]
,
e
n
s
em
b
le
h
y
b
r
id
izatio
n
[
4
8
]
,
a
n
d
f
ed
e
r
ated
lear
n
in
g
f
r
am
ewo
r
k
s
[
2
1
]
,
o
f
f
er
p
r
o
m
is
in
g
d
ir
ec
tio
n
s
f
o
r
ad
v
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cin
g
ac
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ate,
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ca
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le,
an
d
p
r
ac
tical
PV f
o
r
ec
asti
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g
s
o
lu
tio
n
s
.
3
.
4
.
L
im
it
a
t
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ns
o
f
t
he
re
v
iew
W
h
ile
th
is
r
ev
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p
r
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v
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esis
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f
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t
a
d
v
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ce
s
in
h
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r
id
d
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n
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n
g
m
o
d
els
f
o
r
s
o
lar
PV
g
en
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d
r
esid
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tial
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c
o
n
s
u
m
p
tio
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f
o
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lim
itatio
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s
h
o
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ld
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e
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k
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wled
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d
.
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t
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r
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E
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n
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4
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ical
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th
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g
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ay
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x
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ed
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tially
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ci
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p
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licatio
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d
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g
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ag
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b
ias.
Seco
n
d
,
s
u
b
s
tan
tial
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eter
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eity
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m
eth
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o
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g
ical
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esig
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al
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r
ac
tices
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r
o
s
s
th
e
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ies
lim
ited
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e
a
b
ilit
y
to
p
er
f
o
r
m
d
ir
ec
t
co
m
p
a
r
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n
s
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Per
f
o
r
m
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ce
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r
ep
o
r
ted
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s
in
g
d
i
f
f
er
en
t
m
etr
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in
clu
d
in
g
R
MSE
,
MA
E
,
MA
PE,
an
d
R
²,
wh
ile
v
ar
iatio
n
s
in
tem
p
o
r
al
r
eso
lu
tio
n
(
e.
g
.
,
h
o
u
r
ly
,
d
aily
,
an
d
m
o
n
th
ly
f
o
r
ec
asti
n
g
h
o
r
i
zo
n
s
)
an
d
g
eo
g
r
ap
h
ical
s
ettin
g
s
f
u
r
th
er
c
o
n
s
tr
ain
ed
th
e
g
e
n
er
aliza
b
ilit
y
o
f
th
e
f
in
d
in
g
s
ac
r
o
s
s
d
iv
er
s
e
clim
atic
an
d
s
o
cio
-
ec
o
n
o
m
ic
co
n
tex
t
s
.
T
h
ir
d
,
th
is
r
ev
iew
m
ay
b
e
af
f
e
cted
b
y
p
u
b
licatio
n
b
ias,
as st
u
d
ies r
ep
o
r
tin
g
s
tatis
tically
s
ig
n
if
ican
t o
r
f
av
o
r
a
b
le
p
r
e
d
ictiv
e
o
u
tc
o
m
e
s
ar
e
m
o
r
e
lik
ely
to
ap
p
ea
r
in
p
ee
r
-
r
ev
iewe
d
jo
u
r
n
als,
w
h
er
ea
s
n
eg
ativ
e
o
r
in
co
n
clu
s
iv
e
r
esu
lts
ten
d
to
b
e
u
n
d
er
r
e
p
o
r
ted
.
T
h
is
im
b
alan
ce
m
ay
co
n
tr
ib
u
te
to
a
n
o
p
tim
is
tic
r
ep
r
esen
tatio
n
o
f
th
e
p
e
r
f
o
r
m
an
ce
o
f
h
y
b
r
id
d
ee
p
lear
n
in
g
m
o
d
els in
PV f
o
r
ec
asti
n
g
ap
p
licatio
n
s
.
Fin
ally
,
a
f
o
r
m
al
m
eta
-
an
aly
s
i
s
was
n
o
t
co
n
d
u
cted
d
u
e
to
t
h
e
lack
o
f
s
tatis
tical
h
o
m
o
g
en
e
ity
am
o
n
g
th
e
in
clu
d
e
d
s
tu
d
ies.
I
n
s
tead
,
th
e
r
esu
lts
wer
e
s
y
n
t
h
esized
u
s
in
g
a
n
a
r
r
ativ
e
a
n
d
th
em
atic
ap
p
r
o
ac
h
,
wh
ic
h
is
s
u
itab
le
f
o
r
i
d
en
tify
in
g
tr
en
d
s
,
m
eth
o
d
o
l
o
g
ical
p
atter
n
s
,
a
n
d
r
esear
ch
g
a
p
s
,
b
u
t
d
o
es
n
o
t
allo
w
f
o
r
p
r
ec
is
e
q
u
an
titativ
e
esti
m
atio
n
o
f
ef
f
ec
t
s
izes.
Fu
tu
r
e
s
y
s
tem
atic
r
ev
iews
co
u
ld
a
d
d
r
ess
th
is
lim
itatio
n
b
y
ad
o
p
tin
g
s
tan
d
ar
d
ized
e
v
alu
atio
n
p
r
o
t
o
co
ls
an
d
h
ar
m
o
n
ized
d
atasets
,
th
er
eb
y
en
ab
lin
g
m
o
r
e
r
i
g
o
r
o
u
s
q
u
a
n
titativ
e
s
y
n
th
esis
an
d
co
m
p
ar
ativ
e
ass
ess
m
en
t.
3
.
5
.
F
uture
re
s
ea
rc
h a
nd
pra
ct
ica
l im
pli
ca
t
io
ns
Fu
tu
r
e
r
esear
ch
in
PV
f
o
r
ec
as
tin
g
s
h
o
u
ld
p
r
io
r
itize
th
e
s
y
s
tem
atic
in
teg
r
atio
n
o
f
co
n
s
u
m
e
r
b
eh
av
i
o
r
in
to
AI
-
b
ased
p
r
ed
ictio
n
m
o
d
els,
as
m
o
s
t
ex
is
tin
g
s
tu
d
ies
f
o
cu
s
p
r
ed
o
m
i
n
an
tly
o
n
p
r
o
d
u
ctio
n
-
s
id
e
v
ar
iab
les
[
5
7
]
.
E
v
i
d
en
ce
f
r
o
m
r
ec
e
n
t
wo
r
k
s
i
n
d
icate
s
th
at
i
n
co
r
p
o
r
atin
g
d
em
a
n
d
-
s
id
e
in
f
o
r
m
atio
n
im
p
r
o
v
es
th
e
alig
n
m
en
t b
etwe
en
PV g
en
er
a
tio
n
an
d
ac
tu
al
elec
tr
icity
u
s
ag
e,
p
ar
ticu
lar
ly
u
n
d
er
h
ig
h
clim
atic
v
ar
iab
ilit
y
an
d
d
ec
en
tr
alize
d
e
n
er
g
y
s
y
s
tem
s
[
5
6
]
.
Hig
h
-
r
eso
lu
tio
n
s
m
ar
t
m
eter
d
ata
o
f
f
e
r
a
p
r
ac
tical
f
o
u
n
d
atio
n
f
o
r
t
h
is
in
teg
r
atio
n
b
y
ca
p
tu
r
in
g
h
o
u
s
e
h
o
ld
co
n
s
u
m
p
tio
n
p
atter
n
s
at
d
aily
,
wee
k
ly
,
a
n
d
s
ea
s
o
n
al
s
ca
les.
T
h
ese
p
atter
n
s
ca
n
b
e
tr
a
n
s
f
o
r
m
e
d
in
to
s
tr
u
ct
u
r
ed
f
ea
t
u
r
es
s
u
ch
as
p
ea
k
d
e
m
an
d
p
e
r
io
d
s
,
lo
a
d
p
r
o
f
iles
,
a
n
d
lo
n
g
-
ter
m
u
s
ag
e
tr
en
d
s
,
wh
ich
ca
n
b
e
in
co
r
p
o
r
ated
in
to
h
y
b
r
id
f
o
r
ec
asti
n
g
m
o
d
els.
I
n
r
e
g
io
n
s
with
lim
ited
d
ata
av
ailab
ilit
y
,
ag
en
t
-
b
ased
o
r
s
y
n
th
etic
co
n
s
u
m
p
tio
n
m
o
d
elin
g
p
r
o
v
id
es
a
n
alter
n
ativ
e
f
o
r
en
h
a
n
cin
g
m
o
d
el
r
o
b
u
s
tn
ess
an
d
tr
an
s
f
er
ab
ilit
y
[
2
1
]
.
Mu
lti
-
in
p
u
t
d
ee
p
lear
n
in
g
ar
ch
itectu
r
es,
esp
ec
ially
co
n
v
o
l
u
tio
n
al
n
eu
r
al
n
etwo
r
k
-
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
C
NN
-
L
STM
)
f
r
am
ewo
r
k
s
,
ar
e
well
s
u
ited
to
j
o
in
tly
m
o
d
el
PV
p
r
o
d
u
ctio
n
an
d
co
n
s
u
m
p
tio
n
d
y
n
am
ics.
Prio
r
s
tu
d
ies
d
em
o
n
s
tr
ate
th
at
en
r
ich
in
g
L
STM
-
b
ased
PV
f
o
r
ec
asti
n
g
with
u
s
er
-
lev
el
co
n
s
u
m
p
tio
n
d
ata
ca
n
r
ed
u
ce
p
r
ed
ictio
n
e
r
r
o
r
s
b
y
a
p
p
r
o
x
im
ately
8
%
c
o
m
p
ar
ed
to
p
r
o
d
u
ctio
n
-
o
n
ly
m
o
d
els
[
4
4
]
,
[
5
6
]
.
Gr
ap
h
-
en
h
an
ce
d
r
ec
u
r
r
en
t
m
o
d
els
f
u
r
th
e
r
ex
te
n
d
th
is
ca
p
ab
ilit
y
b
y
ca
p
tu
r
in
g
i
n
ter
ac
tio
n
s
am
o
n
g
d
is
tr
ib
u
ted
PV
s
y
s
tem
s
an
d
r
esid
en
tial
lo
ad
s
in
n
etwo
r
k
e
d
g
r
id
en
v
ir
o
n
m
en
ts
[
3
8
]
.
T
o
a
d
d
r
ess
co
m
p
u
tatio
n
al
co
n
s
tr
ain
ts
,
f
u
tu
r
e
w
o
r
k
s
h
o
u
ld
em
p
h
asize
f
ea
tu
r
e
s
elec
tio
n
,
tr
an
s
f
er
lear
n
in
g
,
a
n
d
ef
f
icien
cy
-
o
r
ien
te
d
m
o
d
el
o
p
tim
izatio
n
s
tr
ateg
ies
[
4
5
]
,
[
4
9
]
.
Fro
m
a
p
r
ac
tical
an
d
p
o
licy
p
er
s
p
ec
tiv
e,
h
y
b
r
i
d
AI
-
b
ase
d
f
o
r
ec
asti
n
g
m
o
d
els
ca
n
s
u
p
p
o
r
t
g
r
i
d
s
tab
ilit
y
,
d
em
an
d
-
s
id
e
m
a
n
a
g
em
en
t,
a
n
d
lar
g
e
-
s
ca
le
PV
in
teg
r
atio
n
.
I
n
I
n
d
o
n
esia,
ac
h
iev
in
g
n
atio
n
al
r
en
ewa
b
le
e
n
er
g
y
tar
g
ets
a
n
d
th
e
Net
Z
er
o
E
m
is
s
io
n
2
0
6
0
p
ath
way
r
eq
u
ir
es
p
r
ed
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e
t
o
o
ls
th
at
ac
co
u
n
t
f
o
r
b
o
th
g
e
n
er
atio
n
a
n
d
c
o
n
s
u
m
p
tio
n
d
y
n
am
ics,
in
lin
e
with
n
atio
n
al
en
er
g
y
p
lan
n
in
g
f
r
a
m
ewo
r
k
s
an
d
s
o
lar
ca
p
ac
ity
tar
g
ets
[
5
8
]
,
[
5
9
]
.
Ov
er
all,
in
teg
r
atin
g
co
n
s
u
m
er
b
eh
av
io
r
r
ep
r
esen
ts
a
cr
itical
s
tep
to
war
d
m
o
r
e
ac
cu
r
ate,
g
en
e
r
aliza
b
le,
an
d
o
p
er
atio
n
ally
r
ele
v
an
t PV
f
o
r
ec
asti
n
g
s
y
s
tem
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
1
,
Feb
r
u
ar
y
20
2
6
:
4
6
3
-
476
472
4.
CO
NCLU
SI
O
N
T
h
is
s
y
s
tem
atic
r
ev
iew
ex
am
in
ed
6
9
s
tu
d
ies
p
u
b
lis
h
ed
b
etwe
en
2
0
2
0
an
d
2
0
2
4
to
ass
es
s
th
e
ap
p
licatio
n
o
f
a
r
tific
ial
in
tellig
en
ce
an
d
m
ac
h
in
e
lear
n
in
g
in
s
o
lar
p
h
o
to
v
o
ltaic
f
o
r
ec
a
s
tin
g
.
T
h
e
f
in
d
in
g
s
co
n
f
ir
m
th
at
h
y
b
r
id
a
p
p
r
o
ac
h
es,
p
ar
ticu
lar
ly
C
NN
–
L
STM
ar
ch
itectu
r
es,
co
n
s
is
ten
tly
o
u
t
p
er
f
o
r
m
tr
a
d
itio
n
al
s
tatis
t
ical
m
o
d
els
an
d
s
tan
d
al
o
n
e
AI
m
eth
o
d
s
b
y
ef
f
ec
tiv
el
y
ca
p
tu
r
in
g
n
o
n
lin
ea
r
s
p
atio
te
m
p
o
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al
p
atter
n
s
in
m
eteo
r
o
lo
g
ical
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d
PV
g
e
n
e
r
atio
n
d
ata
.
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h
ese
im
p
r
o
v
em
en
ts
tr
an
s
late
in
to
n
o
tab
le
er
r
o
r
r
ed
u
ctio
n
s
an
d
en
h
an
ce
d
f
o
r
ec
asti
n
g
r
eliab
ilit
y
,
wh
ich
ar
e
ess
en
tial f
o
r
ef
f
ic
ien
t e
n
er
g
y
m
an
ag
e
m
en
t a
n
d
g
r
id
s
tab
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.
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ite
th
ese
ad
v
an
ce
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,
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e
r
e
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iew
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en
tifie
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n
if
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t
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in
th
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c
o
r
p
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ati
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o
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co
n
s
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m
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io
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a
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m
an
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s
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f
o
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m
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in
to
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V
f
o
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g
m
o
d
els.
Mo
s
t e
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d
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em
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r
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r
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esp
o
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iv
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.
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alan
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ig
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s
tin
g
f
r
am
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th
at
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tly
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n
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d
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em
an
d
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y
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m
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Sev
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allen
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p
er
s
is
t,
in
clu
d
in
g
c
o
n
s
tr
ain
ed
d
ata
a
v
ailab
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in
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n
d
er
r
e
p
r
esen
ted
r
e
g
i
o
n
s
,
lim
ited
m
o
d
el
g
en
er
aliza
b
ilit
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ac
r
o
s
s
d
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s
e
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atic
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n
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itio
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s
,
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d
t
h
e
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ased
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