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i
f
ica
n
t
[
4
]
.
On
e
o
f
t
h
e
r
e
n
e
w
ab
le
e
n
er
g
y
g
en
er
atio
n
ca
n
b
e
r
ea
lized
t
h
o
r
u
g
h
t
h
e
u
til
izatio
n
o
f
s
o
lar
en
er
g
y
u
s
in
g
p
h
o
to
v
o
ltaic
(
P
V)
ce
lls
,
w
h
ic
h
co
n
v
er
t
t
h
e
s
o
lar
t
h
er
m
al
en
er
g
y
i
n
to
elec
tr
ical
e
n
er
g
y
.
T
h
e
in
j
ec
tio
n
o
f
s
o
m
e
r
en
e
w
ab
le
en
er
g
y
-
b
ased
p
o
w
e
r
g
e
n
er
atio
n
s
li
k
e
t
h
e
P
V,
w
i
n
d
tu
r
b
in
e
s
,
b
io
m
a
s
s
,
m
icr
o
h
y
d
r
o
,
an
d
o
th
er
p
la
n
ts
in
to
th
e
a
v
ailab
le
g
r
id
i
n
a
p
o
w
er
d
is
tr
ib
u
tio
n
s
y
s
te
m
,
b
ei
n
g
k
n
o
w
n
as
d
is
p
e
r
s
ed
g
e
n
er
ato
r
s
(
DG)
,
g
r
ea
tl
y
in
f
lu
e
n
ce
th
e
d
is
tr
ib
u
tio
n
s
y
s
te
m
[
5
]
.
U
n
d
er
s
tead
y
-
s
tate
co
n
d
itio
n
,
it
m
a
y
af
f
ec
t
t
h
e
v
o
ltag
e
p
r
o
f
ile
a
n
d
p
o
w
er
lo
s
s
e
s
[
6
,
7
]
,
th
e
n
u
m
b
er
an
d
d
ir
ec
tio
n
o
f
p
o
w
er
f
l
o
w
[
8
,
9
]
,
MV
A
f
a
u
lt
le
v
el
s
[
1
0
,
1
1
]
,
r
eliab
ilit
y
s
y
s
te
m
[
1
2
-
1
4
]
,
an
d
p
o
w
er
q
u
alit
y
[
1
5
,
1
6
]
.
Un
d
er
th
e
d
y
n
a
m
ic
-
s
tate
co
n
d
itio
n
,
a
m
o
n
g
th
e
i
m
p
ac
t
s
o
f
t
h
e
PV
-
b
ased
r
en
e
w
ab
le
en
er
g
y
i
n
j
ec
tio
n
in
to
g
r
id
s
y
s
t
e
m
ar
e
t
h
e
s
tab
ilit
y
o
f
t
h
e
f
r
eq
u
en
c
y
a
n
d
v
o
ltag
e
[
5
,
1
7
-
2
1
]
an
d
th
e
s
m
a
ll si
g
n
al
s
tab
ilit
y
[
2
2
]
o
f
th
e
el
ec
tr
ical
s
y
s
te
m
.
T
h
e
en
er
g
y
g
e
n
er
atio
n
ca
p
ac
ity
o
f
a
P
V
-
b
ased
p
o
w
er
s
y
s
te
m
d
ep
en
d
s
o
n
t
h
e
s
o
lar
r
ad
iatio
n
an
d
th
e
w
ea
t
h
er
a
n
d
cli
m
ate
co
n
d
itio
n
s
o
f
t
h
e
lo
ca
tio
n
w
h
er
e
it
i
s
i
n
s
tal
led
.
T
h
e
w
ea
th
er
an
d
cli
m
ate
co
n
d
it
io
n
s
d
ep
en
d
o
n
th
e
g
eo
g
r
ap
h
ical
an
d
atm
o
s
p
h
er
ic
f
e
at
u
r
es.
T
h
e
g
eo
g
r
ap
h
ical
f
ea
t
u
r
es
i
n
clu
d
e
t
h
e
latit
u
d
e,
altitu
d
e,
s
ea
s
o
n
s
,
w
h
e
th
er
i
t
is
ter
r
ain
,
an
d
ev
e
n
w
h
e
n
d
u
r
i
n
g
th
e
d
a
y
ti
m
e
o
f
t
h
e
lo
ca
tio
n
.
I
n
d
o
n
esia
f
o
r
ex
a
m
p
le,
g
eo
g
r
ap
h
ical
l
y
it
is
lo
ca
ted
alo
n
g
t
h
e
eq
u
ato
r
w
it
h
th
e
a
v
ailab
le
m
o
n
t
h
l
y
s
o
lar
r
ad
i
atio
n
o
f
b
et
w
ee
n
4
.
6
k
W
h
/
m
2
an
d
7
.
2
k
W
h
/
m
2
g
i
v
in
g
an
a
v
er
ag
e
o
f
5
.
1
2
k
W
h
/
m
2
th
r
o
u
g
h
o
u
t
th
e
y
ea
r
[
2
4
]
.
T
h
e
atm
o
s
p
h
er
ic
f
ea
t
u
r
es
lead
to
th
e
v
ar
iatio
n
in
s
o
lar
r
ad
iatio
n
,
in
clu
d
i
n
g
p
r
ess
u
r
e,
h
u
m
id
it
y
,
te
m
p
er
at
u
r
e,
d
u
s
t
p
ar
ticles
co
n
ten
t,
c
lo
u
d
s
,
ae
r
o
s
o
ls
,
a
n
d
s
n
o
w
co
v
er
in
g
[
2
3
]
.
T
h
er
ef
o
r
e,
th
e
p
r
ed
ictio
n
o
f
s
o
lar
r
ad
iatio
n
co
n
d
it
io
n
s
is
v
er
y
i
m
p
o
r
tan
t to
h
ar
v
est t
h
e
s
o
lar
en
er
g
y
as e
f
f
ec
ti
v
e
as p
o
s
s
ib
le.
Var
io
u
s
s
t
u
d
ies
h
a
v
e
b
ee
n
u
n
d
er
tak
en
to
p
r
ed
ict
th
e
in
ten
s
i
t
y
o
f
s
o
lar
r
ad
iatio
n
i
n
a
p
ar
ticu
lar
p
lace
.
Ma
n
y
m
et
h
o
d
s
ca
n
b
e
u
s
ed
to
p
er
f
o
r
m
th
e
p
r
ed
ictio
n
.
E
ac
h
tech
n
iq
u
e
h
a
s
its
o
w
n
p
r
o
p
er
ties
an
d
p
r
ec
is
io
n
.
T
h
e
in
cu
r
r
ed
co
s
t
is
ev
en
al
s
o
to
co
n
s
id
er
in
ch
o
o
s
in
g
a
p
ar
ticu
lar
m
et
h
o
d
[
2
5
]
.
So
m
e
k
n
o
w
n
p
r
ed
ictio
n
m
et
h
o
d
s
ca
n
b
e
ca
teg
o
r
ized
in
to
co
n
v
e
n
tio
n
al
m
eth
o
d
s
,
w
h
eth
er
s
o
m
e
o
t
h
er
s
ar
e
u
s
in
g
ar
tif
icia
l
in
te
llig
e
n
ce
m
et
h
o
d
s
.
B
ein
g
co
m
p
ar
ed
to
th
e
co
n
v
e
n
tio
n
al
m
et
h
o
d
s
,
th
e
ar
tif
icial
i
n
telli
g
e
n
ce
-
b
a
s
ed
m
eth
o
d
s
o
f
f
er
s
e
v
er
al
ad
v
an
ta
g
es,
s
u
c
h
as r
elati
v
el
y
ea
s
y
u
p
d
ates a
n
d
m
ai
n
te
n
a
n
ce
,
in
co
m
p
lete
i
n
p
u
t
s
,
an
d
r
ea
s
o
n
in
g
s
k
il
ls
[
2
6
]
.
Sev
er
al
p
r
ed
ictio
n
m
et
h
o
d
s
o
f
s
o
lar
r
ad
iatio
n
ar
e
b
a
s
ed
o
n
t
h
e
ar
ti
f
icial
n
e
u
r
a
l
n
et
w
o
r
k
m
et
h
o
d
s
[
2
4
]
,
lin
ea
r
r
e
g
r
ess
io
n
[
2
7
]
,
p
r
o
b
ab
ilis
tic
m
et
h
o
d
s
[
2
8
]
,
n
et
w
o
r
k
m
o
n
ito
r
i
n
g
d
ata
[
2
9
]
,
f
u
zz
y
m
eth
o
d
ap
p
r
o
ac
h
[
3
0
,
3
1
]
,
A
NFI
S,
M
u
ltip
le
L
i
n
ea
r
R
eg
r
es
s
io
n
s
(
M
L
R
)
m
e
th
o
d
[
3
2
]
,
an
d
s
o
m
e
o
th
er
m
et
h
o
d
s
[
3
3
]
.
T
h
e
co
n
v
en
tio
n
al
m
et
h
o
d
co
m
m
o
n
l
y
u
s
ed
f
o
r
p
r
ed
ictio
n
is
th
e
m
u
ltip
le
r
eg
r
es
s
io
n
m
eth
o
d
.
T
h
is
m
e
th
o
d
ca
n
b
e
an
al
y
ze
d
b
y
u
s
i
n
g
s
e
v
er
a
l
in
d
ep
en
d
en
t
v
ar
iab
les
s
o
th
at
th
e
o
b
tain
ed
r
esu
lts
ar
e
m
o
r
e
a
cc
u
r
ate
[
2
5
]
.
An
o
th
er
ar
ti
f
icial
m
et
h
o
d
wh
ich
ca
n
b
e
u
tili
ze
d
f
o
r
p
r
ed
ictio
n
is
t
h
e
e
x
tr
e
m
e
lear
n
i
n
g
m
ac
h
i
n
e
(
E
L
M)
m
et
h
o
d
,
w
h
ic
h
is
b
ased
o
n
t
h
e
o
f
ar
ti
f
icial
i
n
telli
g
e
n
ce
t
h
eo
r
y
.
T
h
is
m
eth
o
d
h
as
ad
v
a
n
tag
e
s
in
ter
m
s
o
f
ac
cu
r
ac
y
,
g
o
o
d
g
e
n
er
aliza
tio
n
p
er
f
o
r
m
a
n
ce
,
an
d
f
ast lea
r
n
in
g
s
p
ee
d
[
3
4
]
.
T
h
is
p
ap
er
p
r
esen
ts
a
co
m
p
ar
is
o
n
o
f
m
et
h
o
d
s
to
o
b
tain
th
e
b
est
p
r
ed
ictio
n
o
f
s
o
lar
r
ad
iatio
n
in
te
n
s
it
y
.
T
h
e
p
r
ed
ictio
n
al
g
o
r
ith
m
b
ased
o
n
t
h
e
E
L
M
m
et
h
o
d
is
to
b
e
co
m
p
ar
ed
to
th
at
b
ased
o
n
th
e
M
L
R
m
et
h
o
d
.
T
h
e
co
m
p
ar
is
o
n
p
er
f
o
r
m
a
n
ce
p
ar
a
m
eter
s
to
b
e
co
n
s
id
er
ed
ar
e
th
e
r
o
o
t
m
ea
n
s
q
u
ar
e
er
r
o
r
(
R
MSE
)
an
d
th
e
m
ea
n
ab
s
o
lu
te
er
r
o
r
(
MA
E
)
o
b
tain
ed
o
n
ea
ch
test
p
er
f
o
r
m
ed
.
I
t
is
to
b
e
em
p
h
a
s
ized
th
at
a
p
r
o
p
e
r
m
o
d
eli
n
g
is
r
eq
u
ir
ed
in
i
m
p
le
m
en
tin
g
t
h
e
E
L
M
m
et
h
o
d
to
r
esu
lt
i
n
th
e
g
o
o
d
p
r
ed
ictio
n
r
esu
lt
s
.
I
n
o
r
d
er
to
o
b
tain
th
e
o
p
ti
m
al
r
es
u
lts
,
s
e
v
er
al
t
y
p
es
o
f
m
o
d
ell
in
g
v
ar
i
atio
n
s
ar
e
co
n
s
id
er
ed
,
in
clu
d
i
n
g
t
h
e
co
m
p
o
s
itio
n
v
ar
iatio
n
s
o
f
tr
ain
i
n
g
d
ata
an
d
test
in
g
d
ata,
v
ar
iatio
n
s
in
t
h
e
n
u
m
b
er
o
f
h
id
d
en
n
e
u
r
o
n
s
,
a
n
d
t
h
e
u
s
e
o
f
m
o
r
e
n
u
m
b
er
o
f
v
ar
iab
les a
n
d
lo
n
g
e
r
d
ata
r
an
g
es.
T
h
e
test
d
ata
u
s
ed
ar
e
th
e
B
as
el
r
eg
io
n
w
ea
th
er
d
ata
o
f
th
e
S
w
is
s
co
u
n
tr
y
,
b
ei
n
g
o
b
tai
n
ed
f
r
o
m
t
h
e
Me
teo
b
lu
e
w
eb
s
ite
[
3
5
]
w
h
ic
h
p
r
o
v
id
es
h
i
g
h
q
u
ali
t
y
lo
ca
l
w
ea
t
h
er
i
n
f
o
r
m
atio
n
w
o
r
ld
w
i
d
e
f
o
r
ev
er
y
th
in
g
o
n
lan
d
o
r
s
ea
in
th
e
w
o
r
ld
.
T
h
e
p
ar
a
m
eter
s
u
s
ed
ar
e
te
m
p
er
atu
r
e,
d
u
r
atio
n
o
f
d
a
y
ti
m
e
an
d
s
o
l
ar
r
ad
iatio
n
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
T
h
e
g
en
er
al
p
r
o
ce
s
s
o
f
th
e
r
esear
ch
in
th
i
s
p
ap
er
is
p
r
esen
ted
in
Fig
u
r
e
1
.
T
h
e
s
tep
s
to
u
n
d
er
tak
e
th
e
r
esear
ch
o
n
th
e
p
r
ed
ictio
n
o
f
s
o
lar
r
ad
iatio
n
in
ten
s
i
t
y
u
s
i
n
g
E
L
M
m
e
th
o
d
an
d
ML
R
m
eth
o
d
ca
n
b
e
elab
o
r
ated
as f
o
llo
w
s
:
1)
P
r
ep
ar
in
g
th
e
p
ar
a
m
eter
s
d
at
a
s
u
c
h
as
te
m
p
er
atu
r
e
,
d
u
r
ati
o
n
o
f
s
u
n
ex
p
o
s
u
r
e
,
an
d
th
e
s
o
lar
r
ad
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n
w
it
h
t
h
e
s
p
ec
i
f
ied
ti
m
e
d
u
r
atio
n
in
ac
co
r
d
an
ce
w
it
h
t
h
e
ex
i
s
ti
n
g
d
ata.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
P
r
ed
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o
f S
o
l
a
r
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a
d
ia
tio
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n
ten
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xtreme
Lea
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in
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Ma
ch
in
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Ha
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yo
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693
2)
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o
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in
g
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a
v
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d
ata
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n
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t
w
o
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ar
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ai
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as g
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6
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ased
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d
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et
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s
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t
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m
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d
test
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s
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w
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o
le
d
ata
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ai
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d
ata
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d
test
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g
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h
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w
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0
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th
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Fig
u
r
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1
.
T
h
e
r
esear
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m
e
th
o
d
in
p
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ed
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t
h
e
s
o
lar
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ad
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in
te
n
s
it
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s
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m
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u
r
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2
.
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h
e
tr
ain
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g
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th
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m
p
le
m
en
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et
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d
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h
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f
u
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ce
s
s
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ev
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lo
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th
e
m
o
d
e
l
o
f
th
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L
M
m
et
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o
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i
m
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m
en
tatio
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.
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t
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u
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e
to
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eter
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i
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m
p
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m
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n
tatio
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s
y
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te
m
.
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h
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lt
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e
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h
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ain
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ar
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n
i
m
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m
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ted
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p
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ed
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th
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s
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lar
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te
n
s
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ased
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test
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s
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tes
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L
M
m
eth
o
d
is
s
h
o
w
n
in
F
ig
u
r
e
3
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2502
-
4752
I
n
d
o
n
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n
J
E
lec
E
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&
C
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m
p
Sci,
Vo
l
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1
2
,
No
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2
,
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b
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201
8
:
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9
1
–
6
9
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694
Fig
u
r
e
3
.
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h
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test
in
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s
s
d
u
r
in
g
th
e
i
m
p
le
m
en
tatio
n
o
f
E
L
M
m
eth
o
d
3.
RE
SU
L
T
S
A
ND
AN
AL
Y
SI
S
3
.
1
.
M
et
eo
blu
e
Cli
m
a
t
o
lo
g
y
Da
t
a
(
NO
AA)
T
h
e
d
ata
u
s
ed
in
th
is
r
esear
c
h
h
av
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b
ee
n
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b
tain
ed
f
r
o
m
th
e
NO
AA
Me
teo
b
u
e
C
li
m
ato
lo
g
y
w
eb
s
ite,
n
a
m
e
l
y
t
h
e
d
ata
o
f
t
h
e
B
asel
C
it
y
,
S
w
itzer
la
n
d
[
3
5
]
.
T
h
e
s
e
d
ata
o
f
f
er
s
lo
n
g
er
ti
m
e
s
p
a
n
a
n
d
lar
g
er
n
u
m
b
er
o
f
in
d
ep
en
d
en
t
v
ar
iab
les
,
s
o
t
h
at
th
e
p
er
f
o
r
m
a
n
ce
o
f
t
h
e
E
L
M
m
et
h
o
d
u
n
d
er
co
n
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id
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ca
n
b
e
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al
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d
m
o
r
e
co
m
p
r
e
h
en
s
iv
e
l
y
.
T
h
e
d
ata
co
n
tai
n
t
h
e
h
o
u
r
l
y
d
ata
o
f
B
asel
cit
y
d
u
r
in
g
t
h
e
p
er
io
d
o
f
J
an
u
ar
y
2
0
1
2
to
Ma
r
ch
2018
,
w
it
h
a
to
tal
o
f
4
3
8
0
0
d
ata.
T
h
e
d
ata
in
cl
u
d
e
t
h
e
p
ar
a
m
eter
s
s
u
c
h
a
s
t
h
e
d
u
r
atio
n
o
f
s
u
n
r
ad
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n
,
av
er
ag
e
te
m
p
er
at
u
r
e,
h
u
m
id
i
t
y
,
r
ai
n
f
a
ll,
a
n
d
t
h
e
i
n
te
n
s
i
t
y
o
f
s
o
lar
r
ad
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n
.
Six
v
ar
iatio
n
s
o
f
d
ata
co
m
p
o
s
i
tio
n
h
av
e
b
ee
n
co
n
s
id
er
ed
d
u
r
in
g
t
h
e
r
esear
ch
,
as
s
h
o
w
n
in
T
ab
le
1
.
T
h
e
d
ata
co
m
p
o
s
itio
n
v
ar
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n
s
ca
n
b
e
f
o
r
m
ed
b
y
t
h
e
co
m
p
o
s
i
tio
n
o
f
X%
o
f
th
e
to
tal
d
ata
as
th
e
tr
ain
i
n
g
d
ata
an
d
(
1
0
0
-
X)
%
o
f
to
tal
d
ata
a
s
th
e
test
in
g
d
ata
.
T
h
e
u
s
e
o
f
M
atL
ab
p
r
o
g
r
a
m
m
i
n
g
h
as b
ee
n
co
n
s
id
er
ed
in
th
e
E
L
M
m
et
h
o
d
im
p
le
m
e
n
tatio
n
.
3.
2
.
S
t
ud
y
Ca
s
e
#
1
:
Da
t
a
Co
m
po
s
it
io
n
70
%
-
30
%
T
h
is
ex
p
er
i
m
e
n
t
a
i
m
s
to
co
m
p
ar
e
th
e
p
r
ed
ictio
n
r
es
u
lts
o
b
tain
ed
u
s
i
n
g
th
e
E
L
M
m
e
th
o
d
an
d
t
h
o
s
e
u
s
i
n
g
th
e
M
L
R
m
et
h
o
d
.
I
t
ai
m
s
to
f
i
n
d
th
e
s
m
alle
s
t
er
r
o
r
v
alu
e
b
et
w
ee
n
t
h
e
t
w
o
m
eth
o
d
s
.
I
n
th
e
St
u
d
y
C
a
s
e
#
1
,
th
e
d
ata
c
o
m
p
o
s
itio
n
is
f
o
r
m
ed
b
y
7
0
%
(
3
0
6
6
0
d
ata)
o
f
tr
ain
i
n
g
d
ata,
an
d
3
0
%
(
1
3
1
4
0
d
ata)
o
f
test
in
g
d
ata.
Fig
u
r
e
4
s
h
o
w
s
th
e
co
m
p
ar
is
o
n
o
f
t
h
e
p
r
ed
ictio
n
r
esu
lts
u
s
in
g
t
h
e
E
L
M
an
d
M
L
R
m
et
h
o
d
s
to
t
h
e
ac
tu
al
d
ata
f
r
o
m
N
O
AA
.
T
h
e
R
MSE
an
d
M
A
E
v
alu
e
s
g
e
n
e
r
ated
f
r
o
m
t
h
e
E
L
M
m
e
th
o
d
i
m
p
le
m
e
n
tat
io
n
ar
e
1
3
2
.
2
3
9
W
/m
2
an
d
9
1
.
5
6
9
W
/
m
2
,
w
h
er
ea
s
u
s
i
n
g
th
e
M
L
R
m
et
h
o
d
th
e
y
ar
e
1
5
0
.
5
4
7
W
/m
2
an
d
1
1
3
.
4
0
5
W
/m
2
r
esp
ec
tiv
el
y
.
T
h
e
p
r
ed
ictio
n
r
e
s
u
lt
s
u
s
i
n
g
t
h
e
E
L
M
m
et
h
o
d
a
r
e
m
u
ch
clo
s
er
to
t
h
e
ac
t
u
al
d
a
ta
b
ein
g
co
m
p
ar
ed
to
th
e
r
esu
lt
s
o
f
M
L
R
p
r
ed
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n
m
et
h
o
d
.
3.
3
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ased
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ase
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ase
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Evaluation Warning : The document was created with Spire.PDF for Python.
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2502
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4752
P
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ten
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S
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c
re
taria
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n
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ra
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f
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a
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En
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.
[2
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P
L
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2
0
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A
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Re
p
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P
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li
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F
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ti
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Ja
k
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2
0
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5
[3
]
S
h
a
h
z
a
d
BK,
Yo
u
sa
f
M
.
Co
a
l
F
ired
P
o
w
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P
lan
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Em
issio
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ro
b
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s
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Co
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tr
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h
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.
2
0
1
7
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(7
):
1
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9.
[4
]
Jin
k
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s
N
,
A
ll
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R,
Co
ss
le
y
P
,
Kirsc
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In
stit
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f
El
e
c
tri
c
a
l
En
g
in
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rs.
2
0
0
0
.
[5
]
S
u
y
o
n
o
H,
Ha
sa
n
a
h
RN,
M
u
d
ji
ra
h
a
rd
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P
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F
E.
S
tea
d
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-
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ta
te
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n
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n
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p
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rid
Em
b
e
d
d
e
d
Ge
n
e
ra
ti
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in
Distrib
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ti
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S
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In
tern
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ti
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a
l
S
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m
in
a
r
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In
tel
li
g
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T
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c
h
n
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n
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Its
A
p
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s
(I
S
IT
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)
a
n
d
Re
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l
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n
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tri
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E)
2
0
1
7
(I
S
IT
IA
–
RCEE
E
2
0
1
7
).
2
0
1
7
:
142
-
1
4
7
.
[6
]
Ka
ti
ra
e
i
F
,
M
a
u
c
h
K,
Dig
n
a
rd
-
B
a
il
e
y
L
.
In
teg
ra
ti
o
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p
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v
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taic
p
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s
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p
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tra
ti
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n
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l
u
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b
u
ti
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n
n
e
tw
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rk
s an
d
m
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rid
s.
In
t.
J
.
Distri
b
.
E
n
e
rg
y
Res
.
2
0
0
7
;
3
(3
)
:
2
0
7
–
2
2
3
.
[7
]
S
u
y
o
n
o
H,
Ha
sa
n
a
h
RN.
A
n
a
l
y
si
s
o
f
P
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w
e
r
L
o
ss
e
s
d
u
e
to
Distrib
u
ted
G
e
n
e
ra
ti
o
n
In
c
re
a
se
o
n
Distr
ib
u
ti
o
n
S
y
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m
.
J
u
rn
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l
T
e
k
n
o
l
o
g
i
.
2
0
1
6
;
7
8
(
6
-
3
)
:
2
3
-
28.
[8
]
T
h
o
m
so
n
M
,
In
f
ield
DG
.
Ne
t
wo
rk
p
o
w
e
r
-
f
lo
w
a
n
a
l
y
sis
f
o
r
a
h
ig
h
p
e
n
e
tratio
n
o
f
d
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b
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ted
g
e
n
e
ra
ti
o
n
.
IEE
E
T
ra
n
sa
c
ti
o
n
o
n
P
o
we
r S
y
ste
m
.
2
0
0
7
;
2
2
(
3
):
1
1
5
7
–
1
1
6
2
.
[9
]
W
id
é
n
J,
S
h
e
p
e
ro
M
,
M
u
n
k
h
a
m
m
a
r
J.
P
ro
b
a
b
il
isti
c
L
o
a
d
F
l
o
w
fo
r
P
o
w
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r
G
rid
s
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it
h
Hig
h
P
V
P
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tratio
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s
Us
in
g
Co
p
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la
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Ba
se
d
M
o
d
e
li
n
g
o
f
S
p
a
ti
a
ll
y
Co
rre
late
d
S
o
lar Irrad
ian
c
e
.
IEE
E
J
o
u
rn
a
l
o
f
Ph
o
t
o
v
o
lt
a
ics
.
2
0
1
7
;
7
(
6
):
1
7
4
0
–
1
7
4
5
.
[1
0
]
M
u
rd
o
c
h
N,
Be
rry
J,
Ka
z
e
ro
o
n
i
A
.
Distrib
u
te
d
g
e
n
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ra
ti
o
n
c
o
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n
e
c
ti
o
n
s
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f
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lt
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lev
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e
t
w
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rk
m
a
n
a
g
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m
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t
sc
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m
e
.
CIRE
D
-
Op
e
n
Acc
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ss
Pro
c
e
e
d
in
g
s
J
o
u
rn
a
l
IET
J
o
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rn
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ls
&
M
a
g
a
zin
e
s
.
2
0
1
7
;
2
0
1
7
(
1
):
1
7
0
7
–
1
7
1
0
.
[1
1
]
Bo
lj
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v
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,
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ti
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o
f
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istrib
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ted
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ra
ti
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.
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Co
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n
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s
2
0
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6
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h
In
tern
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ti
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ro
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t.
2
0
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9
:
1
–
6.
[1
2
]
L
iu
N,
W
u
T
,
X
u
T
,
M
a
Y.
Re
li
a
b
il
it
y
e
v
a
lu
a
ti
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n
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o
d
f
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n
e
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rk
.
IET
J
o
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rn
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ls
&
M
a
g
a
zin
e
s,
T
h
e
J
o
u
rn
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l
o
f
E
n
g
i
n
e
e
rin
g
.
2
0
1
7
;
2
0
1
7
(
1
3
):
1
7
7
1
–
1
7
7
6
.
[1
3
]
A
r
g
ü
e
l
lo
,
L
a
ra
JD
,
Ro
jas
JD
,
V
a
lv
e
rd
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.
I
m
p
a
c
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to
p
P
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In
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ra
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Distrib
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ti
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m
s
Co
n
sid
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g
S
o
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io
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c
o
n
o
m
ic F
a
c
to
rs.
IEE
E
S
y
ste
ms
J
o
u
rn
a
l
.
2
0
1
7
;
p
p
(9
9
):
1
–
1
2
.
[1
4
]
S
u
y
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n
o
H,
W
ij
o
n
o
,
Ha
sa
n
a
h
RN,
Dh
u
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a
S
.
Po
we
r
d
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b
u
t
io
n
sy
ste
m
re
li
a
b
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it
y
imp
ro
v
e
me
n
t
d
u
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to
in
jec
ti
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o
f
d
istrib
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ted
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.
2
0
1
7
IE
EE
1
0
t
h
In
tern
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ti
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n
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l
Co
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f
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El
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c
tri
c
a
l
a
n
d
El
e
c
tro
n
ics
En
g
in
e
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rin
g
(EL
ECO).
2
0
1
7
:
1
4
8
5
–
1
4
9
0
.
[1
5
]
Esp
a
rz
a
M
,
S
e
g
u
n
d
o
J,
N
ú
ñ
e
z
C,
W
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n
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Blaa
b
jerg
F
.
A
Co
m
p
re
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siv
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De
sig
n
A
p
p
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a
c
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f
P
o
w
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r
El
e
c
tro
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-
Ba
se
d
Distrib
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ted
G
e
n
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ra
ti
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n
Un
it
s
F
o
c
u
se
d
o
n
P
o
w
e
r
-
Qu
a
li
ty
I
m
p
ro
v
e
m
e
n
t.
IEE
E
T
ra
n
sa
c
t
io
n
s
o
n
Po
we
r
De
li
v
e
ry
.
2
0
1
7
;
3
2
(2
):
9
4
2
–
9
5
0
.
[1
6
]
Zen
g
Z,
Ya
n
g
H,
G
u
e
rre
ro
J
M
,
Zh
a
o
R.
M
u
lt
i
-
f
u
n
c
ti
o
n
a
l
d
istri
b
u
ted
g
e
n
e
ra
ti
o
n
u
n
i
t
f
o
r
p
o
w
e
r
q
u
a
li
ty
e
n
h
a
n
c
e
m
e
n
t.
IET
Po
we
r E
lec
tro
n
ics
.
2
0
1
5
;
8
(3
):
4
6
7
–
4
7
6
.
[1
7
]
Isla
m
M
,
M
it
h
u
lan
a
n
th
a
n
N,
H
o
ss
a
in
M
J.
Dy
n
a
m
i
c
v
o
lt
a
g
e
su
p
p
o
rt
b
y
TL
-
P
V
sy
ste
m
s
to
m
it
ig
a
te
sh
o
rt
-
term
v
o
lt
a
g
e
in
sta
b
il
it
y
in
re
sid
e
n
ti
a
l
D
N
.
IEE
E
T
r
a
n
s
a
c
ti
o
n
s o
n
P
o
we
r S
y
ste
ms
.
2
0
1
7
;
p
p
(9
9
):
1
–
1.
[1
8
]
G
lo
v
e
r
E,
Ch
a
n
g
C
-
C,
G
o
rin
e
v
s
k
y
D,
L
a
ll
S
.
Fre
q
u
e
n
c
y
sta
b
il
it
y
fo
r
d
istrib
u
ted
g
e
n
e
ra
ti
o
n
c
o
n
n
e
c
ted
th
ro
u
g
h
g
rid
-
ti
e
i
n
v
e
rte
r
.
2
0
1
2
IEE
E
I
n
ter
n
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
P
o
w
e
r
S
y
ste
m
Tec
h
n
o
lo
g
y
(P
OW
ERCON).
2
0
1
2
:
1
–
6.
[1
9
]
Do
n
g
D,
L
i
J,
Bo
ro
y
e
v
ich
D,
M
a
t
tav
e
ll
i,
Cv
e
t
k
o
v
ic
I,
X
u
e
Y.
Fre
q
u
e
n
c
y
b
e
h
a
v
i
o
r
a
n
d
it
s
sta
b
il
it
y
o
f
g
rid
-
i
n
ter
fa
c
e
c
o
n
v
e
rte
r
in
d
istrib
u
ted
g
e
n
e
ra
t
io
n
sy
ste
ms
.
2
0
1
2
T
w
e
n
t
y
-
S
e
v
e
n
th
A
n
n
u
a
l
IE
EE
A
p
p
li
e
d
P
o
we
r
El
e
c
tro
n
ics
Co
n
f
e
re
n
c
e
a
n
d
Ex
p
o
sit
io
n
(A
P
E
C).
2
0
1
2
:
1
8
8
7
–
1
8
9
3
.
[2
0
]
A
n
g
e
li
m
JH
,
Aff
o
n
so
CM
.
Im
p
a
c
t
o
f
d
istri
b
u
ted
g
e
n
e
ra
t
io
n
tec
h
n
o
lo
g
y
a
n
d
lo
c
a
ti
o
n
o
n
p
o
w
e
r
s
y
ste
m
v
o
lt
a
g
e
sta
b
il
it
y
.
IEE
E
L
a
ti
n
Ame
ric
a
T
ra
n
sa
c
ti
o
n
s
.
2
0
1
6
;
1
4
(4
):
1
7
5
8
–
1
7
6
5
.
[2
1
]
Ka
rlsso
n
P
,
Bj
o
rn
ste
d
t
J,
S
tro
m
M
.
S
t
a
b
i
li
ty
o
f
v
o
lt
a
g
e
a
n
d
fre
q
u
e
n
c
y
c
o
n
tro
l
i
n
d
istri
b
u
te
d
g
e
n
e
r
a
ti
o
n
b
a
se
d
o
n
p
a
ra
l
lel
-
c
o
n
n
e
c
ted
c
o
n
v
e
rte
rs
fee
d
in
g
c
o
n
sta
n
t
p
o
we
r
lo
a
d
s
.
2
0
0
5
Eu
ro
p
e
a
n
Co
n
f
e
re
n
c
e
o
n
P
o
w
e
r
El
e
c
tro
n
ics
a
n
d
A
p
p
li
c
a
ti
o
n
s.
2
0
0
5
.
[2
2
]
Kris
m
a
n
to
A
U,
M
it
h
u
lan
a
n
th
a
n
N.
Id
e
n
ti
f
ica
ti
o
n
o
f
m
o
d
a
l
in
tera
c
ti
o
n
a
n
d
sm
a
ll
sig
n
a
l
sta
b
il
it
y
i
n
a
u
to
n
o
m
o
u
s
m
icro
g
rid
o
p
e
ra
ti
o
n
.
IET
Ge
n
e
r
a
t
io
n
,
T
ra
n
sm
issio
n
&
Distrib
u
ti
o
n
.
2
0
1
8
;
1
2
(1
):
2
4
7
–
2
5
7
.
[2
3
]
Ra
th
o
d
A
P
S
,
M
i
tt
a
l
P
,
Ku
m
a
r
B
.
An
a
lys
is
o
f
fa
c
to
rs
a
ff
e
c
ti
n
g
th
e
so
la
r
ra
d
ia
ti
o
n
re
c
e
ive
d
b
y
a
n
y
re
g
io
n
.
2
0
1
6
In
tern
a
ti
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
Em
e
r
g
in
g
T
re
n
d
s in
Co
m
m
u
n
ica
ti
o
n
T
e
c
h
n
o
l
o
g
ies
(ET
CT
).
2
0
1
6
:
1
–
4.
[2
4
]
Ru
m
b
a
y
a
n
M
,
A
b
u
d
u
re
y
i
m
u
A
,
Na
g
a
s
a
k
a
K.
M
a
p
p
i
n
g
o
f
so
lar
e
n
e
rg
y
p
o
ten
ti
a
l
i
n
I
n
d
o
n
e
sia
u
si
n
g
a
rti
fi
c
ial
n
e
u
ra
l
n
e
tw
o
rk
a
n
d
g
e
o
g
ra
p
h
ica
l
in
f
o
rm
a
ti
o
n
sy
ste
m
.
Ren
e
wa
b
le a
n
d
S
u
st
a
in
a
b
le E
n
e
rg
y
Rev
iews
.
2
0
1
2
;
1
6
:
1
4
3
7
–
1
4
4
9
.
[2
5
]
M
a
k
rid
a
k
is
S
,
W
h
e
e
l
w
rig
h
t
S
C,
M
c
Ge
e
V
E.
F
o
re
c
a
stin
g
,
2
n
d
Ed
it
i
o
n
,
V
o
l.
I.
P
rin
ted
V.
T
ra
n
sla
ti
o
n
Ja
k
a
rta:
Erl
a
n
g
g
a
.
1
9
9
5
[2
6
]
Ku
su
m
a
d
e
w
i
S
.
A
rti
f
icia
l
In
telli
g
e
n
c
e
(
T
e
c
h
n
iq
u
e
a
n
d
Its
A
p
p
li
c
a
ti
o
n
)
(I
n
d
o
n
e
sia
n
V
e
rsio
n
).
Y
o
g
y
a
k
a
rta:
G
ra
h
a
Ilm
u
.
2
0
0
3
.
[2
7
]
S
u
laim
a
n
S
I,
A
b
d
u
l
Ra
h
m
a
n
TK,
M
u
sirin
I,
S
h
a
a
ri
S
.
Arti
fi
c
i
a
l
n
e
u
ra
l
n
e
two
rk
v
e
rs
u
s
li
n
e
a
r
re
g
re
ss
io
n
fo
r
p
re
d
ictin
g
Gr
id
-
C
o
n
n
e
c
ted
P
h
o
t
o
v
o
lt
a
ic
sy
ste
m
o
u
tp
u
t
.
2
0
1
2
IE
EE
In
tern
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
Cy
b
e
r
T
e
c
h
n
o
lo
g
y
in
A
u
to
m
a
ti
o
n
,
C
o
n
tr
o
l,
a
n
d
I
n
tel
li
g
e
n
t
S
y
ste
m
s (
CYBER).
2
0
1
2
:
1
7
0
-
1
7
4
.
[2
8
]
Kh
a
ll
a
t
M
A
,
Ra
h
m
a
n
S
.
A
P
ro
b
a
b
il
isti
c
A
p
p
ro
a
c
h
t
o
P
h
o
t
o
v
o
lt
a
ic G
e
n
e
r
a
to
r
P
e
rf
o
rm
a
n
c
e
P
re
d
ictio
n
.
IEE
E
Po
we
r
En
g
i
n
e
e
rin
g
Rev
iew
.
1
9
8
6
;
P
ER
-
6
(9
):
2
4
–
2
4
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2502
-
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I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l
.
1
2
,
No
.
2
,
No
v
e
m
b
er
201
8
:
6
9
1
–
6
9
8
698
[2
9
]
Zh
o
n
g
Z,
T
a
n
J,
Zh
a
n
g
T
,
Zh
u
L
.
P
V
p
o
w
e
r
sh
o
rt
-
term
f
o
re
c
a
stin
g
m
o
d
e
l
b
a
se
d
o
n
th
e
d
a
ta
g
a
th
e
re
d
f
ro
m
m
o
n
it
o
rin
g
n
e
tw
o
rk
.
IEE
E
J
o
u
rn
a
ls
&
M
a
g
a
zin
e
s Ch
in
a
Co
mm
u
n
i
c
a
ti
o
n
s
.
2
0
1
4
;
1
1
(
1
4
):
6
1
–
69.
[3
0
]
M
e
ll
it
A
,
A
r
a
b
A
H,
Kh
o
rissi
N,
S
a
lh
i
H.
An
ANF
IS
-
b
a
se
d
Fo
re
c
a
stin
g
f
o
r
S
o
l
a
r
Ra
d
i
a
ti
o
n
Da
t
a
fro
m
S
u
n
sh
i
n
e
Du
ra
ti
o
n
a
n
d
Amb
ien
t
T
e
mp
e
ra
tu
re
.
IEE
E
2
0
0
7
P
o
w
e
r
En
g
in
e
e
rin
g
S
o
c
iety
G
e
n
e
r
a
l
M
e
e
ti
n
g
.
2
0
0
7
:
1
–
6.
[3
1
]
M
e
ll
it
,
Ka
l
o
g
iro
u
S
A
.
Ne
u
ro
-
Fu
zz
y
Ba
se
d
M
o
d
e
li
n
g
fo
r
Ph
o
to
v
o
lt
a
ic
Po
we
r
S
u
p
p
ly
S
y
ste
m
.
2
0
0
6
IEE
E
In
tern
a
ti
o
n
a
l
P
o
w
e
r
a
n
d
En
e
rg
y
Co
n
f
e
re
n
c
e
.
2
0
0
6
:
8
8
–
9
3
.
[3
2
]
S
u
y
o
n
o
H,
Ha
sa
n
a
h
RN,
S
e
ty
a
wa
n
RA
,
M
u
d
ji
ra
h
a
r
d
jo
P
,
W
ij
o
y
o
A
,
M
u
sirin
I.
C
o
m
p
a
riso
n
o
f
S
o
lar
Ra
d
iati
o
n
In
ten
sity
F
o
re
c
a
stin
g
Us
in
g
A
N
F
IS
a
n
d
M
u
lt
i
p
le
L
in
e
a
r
Re
g
re
s
s
io
n
M
e
t
h
o
d
s.
Bu
ll
e
ti
n
o
f
El
e
c
trica
l
En
g
in
e
e
ri
n
g
a
n
d
In
f
o
rm
a
ti
c
s
.
2
0
1
8
;
7
(
2
).
[3
3
]
M
o
ri
H,
T
a
k
a
h
a
sh
i
M
.
A
p
re
d
ictio
n
me
th
o
d
fo
r
p
h
o
to
v
o
lt
a
ic
p
o
we
r
g
e
n
e
ra
ti
o
n
wit
h
a
d
v
a
n
c
e
d
Ra
d
i
a
l
Ba
sis
Fu
n
c
ti
o
n
Ne
tw
o
rk
.
IEE
E
P
ES
In
n
o
v
a
ti
v
e
S
m
a
rt
G
rid
T
e
c
h
n
o
lo
g
ies
.
2
0
1
2
:
1
–
6.
[3
4
]
Ch
e
n
g
G
,
Ca
i
L
,
P
a
n
H.
Co
m
p
a
r
iso
n
o
f
Extre
me
L
e
a
rn
i
n
g
M
a
c
h
i
n
e
wit
h
S
u
p
p
o
rt
Vec
to
r
Reg
re
ss
i
o
n
f
o
r
Res
e
rv
o
ir
Per
me
a
b
il
it
y
Pre
d
icti
o
n
.
I
EE
E
2
0
0
9
I
n
tern
a
ti
o
n
a
l
Co
n
f
e
re
n
c
e
o
n
Co
m
p
u
tatio
n
a
l
I
n
telli
g
e
n
c
e
a
n
d
S
e
c
u
rit
y
.
2
0
0
9
;
2
:
173
-
1
7
6
.
[3
5
]
M
e
teo
b
l
u
e
,
”
W
e
a
th
e
r
h
isto
ry
d
o
w
n
lo
a
d
Ba
se
l”,
c
a
n
b
e
a
c
c
e
ss
e
d
in
:
h
tt
p
s:/
/www
.
m
e
teo
b
lu
e
.
c
o
m
/en
/
we
a
th
e
r/arc
h
iv
e
/ex
p
o
rt/
b
a
se
l_
sw
it
z
e
rlan
d
_
2
6
6
1
6
0
4
[3
6
]
Ch
a
i
T
,
Dra
x
ler RR.
Ro
o
t
m
e
a
n
s
q
u
a
re
e
rro
r
(RM
S
E)
o
r
m
e
a
n
a
b
so
lu
te erro
r
(M
A
E)?
–
A
r
g
u
m
e
n
ts
a
g
a
in
st a
v
o
id
in
g
RM
S
E
i
n
t
h
e
li
tera
tu
re
.
Ge
o
sc
ien
t
if
ic M
o
d
e
l
De
v
e
lo
p
me
n
t
.
2
0
1
4
;
7
:
1
2
4
7
–
1
2
5
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