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s
[
1
]
.
I
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last
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th
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a
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f
ac
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f
ic
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as
b
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i
m
p
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o
v
ed
[
2
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3
]
.
T
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o
f
t
h
e
m
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clea
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[
4
]
.
I
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s
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[
6
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.
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Evaluation Warning : The document was created with Spire.PDF for Python.
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I
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9
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.
4
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2211
2206
to
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6
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4
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ea
l
w
it
h
th
e
h
i
g
h
l
y
n
o
n
li
n
ea
r
ch
a
n
g
e
in
d
y
n
a
m
ics
a
n
d
o
p
er
atio
n
al
ch
ar
ac
ter
is
tics
o
f
t
h
e
P
V
s
y
s
te
m
[
1
]
.
I
n
cr
ea
s
ed
ca
p
ac
it
y
o
f
r
e
n
e
wab
le
p
o
w
er
s
o
u
r
ce
s
i
s
a
m
ai
n
d
r
iv
i
n
g
f
ac
to
r
f
o
r
s
u
s
tai
n
a
b
le
p
o
w
er
g
en
er
atio
n
[
7
]
.
I
n
r
ec
en
t
y
ea
r
s
,
p
en
etr
atio
n
o
f
s
u
ch
r
e
n
e
w
a
b
le
s
o
u
r
ce
s
,
alo
n
g
w
it
h
t
h
eir
i
n
ter
m
itte
n
ce
p
o
w
er
g
en
er
atio
n
,
h
as
p
o
s
ed
ch
allen
g
es
to
p
o
w
er
en
g
i
n
ee
r
s
[
8
]
.
T
h
e
r
ap
id
g
r
o
w
t
h
o
f
s
u
ch
r
eso
u
r
ce
s
h
as
r
esu
lted
i
n
s
o
m
e
s
tr
u
ct
u
r
al
ch
a
n
g
es
i
n
p
o
w
er
g
r
id
s
[
9
]
.
T
h
e
r
eli
ab
le
o
p
er
atio
n
an
d
ec
o
n
o
m
ic
d
i
s
p
atch
(
d
is
tr
ib
u
tio
n
)
o
f
p
o
w
er
g
en
er
atio
n
ar
e
u
s
u
all
y
p
lan
n
ed
r
ea
l
-
ti
m
e,
d
ail
y
,
w
ee
k
l
y
,
m
o
n
t
h
l
y
,
an
d
y
ea
r
l
y
.
He
n
c
e,
it
is
im
p
o
r
tan
t
to
p
r
ed
ict
th
e
lo
ad
p
r
o
f
ile
o
f
p
o
w
er
s
y
s
te
m
s
a
n
d
t
h
eir
g
e
n
er
at
io
n
o
u
tp
u
ts
i
n
cl
u
d
in
g
t
h
e
co
n
v
en
t
io
n
al
ce
n
tr
alize
d
p
o
w
er
p
lan
ts
a
n
d
r
en
e
w
ab
le
p
o
w
er
s
tatio
n
s
s
u
c
h
as p
h
o
to
v
o
l
taic
(
P
V)
an
d
w
in
d
[
1
0
]
.
P
o
w
er
o
u
tp
u
t
f
o
r
ec
asti
n
g
o
f
R
E
S
is
i
m
p
o
r
tan
t
f
o
r
g
r
id
o
p
er
ato
r
s
.
I
t
h
elp
s
th
e
g
r
id
o
p
er
ato
r
s
to
p
r
ed
ict
th
e
s
h
o
r
tag
e
o
r
s
u
r
p
lu
s
ti
m
e
o
f
R
E
S
a
n
d
m
a
k
e
n
ec
e
s
s
ar
y
d
ec
is
io
n
s
.
I
n
ad
d
iti
o
n
,
it
is
u
s
e
f
u
l
f
o
r
s
ch
ed
u
lin
g
,
e
n
er
g
y
s
to
r
ag
e,
m
an
ag
e
m
e
n
t
a
n
d
m
ai
n
te
n
an
ce
o
f
t
h
e
p
o
w
er
s
y
s
te
m
s
[
3
]
.
T
h
e
p
r
ed
ictio
n
er
r
o
r
o
f
s
h
o
r
t
-
ter
m
P
V
p
o
w
er
o
u
tp
u
t
is
in
th
e
r
an
g
e
o
f
∼
10
-
2
0
%.
T
h
e
p
r
ed
ictio
n
ac
cu
r
ac
y
is
t
y
p
icall
y
lo
w
er
in
th
e
m
o
r
n
i
n
g
s
,
e
v
en
in
g
s
,
o
r
u
n
d
er
r
ain
y
w
ea
t
h
er
co
n
d
itio
n
s
,
an
d
u
n
d
er
s
o
m
e
co
n
d
itio
n
s
t
h
e
r
elati
v
e
m
ea
n
s
q
u
ar
e
er
r
o
r
(
R
MSE
)
ca
n
b
e
e
v
en
h
i
g
h
er
t
h
an
5
0
% [
4
]
.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
T
h
e
f
o
r
ec
asti
n
g
ap
p
r
o
ac
h
es
o
f
P
V
g
en
er
atio
n
ca
n
b
e
class
i
f
ied
in
to
t
h
r
ee
ca
teg
o
r
ie
s
in
clu
d
i
n
g
n
u
m
er
ical
w
ea
t
h
er
f
o
r
ec
asti
n
g
(
NW
P
)
-
b
ased
ap
p
r
o
ac
h
,
d
ata
-
d
r
iv
en
s
tat
is
tica
l
ap
p
r
o
ac
h
an
d
h
y
b
r
id
ap
p
r
o
ac
h
[
1
1
]
.
NW
P
-
b
ased
f
o
r
ec
asti
n
g
ap
p
r
o
ac
h
h
as
b
ee
n
d
is
cu
s
s
ed
in
[
1
2
]
,
w
h
ic
h
u
s
es
t
h
e
f
ir
s
t
p
r
in
cip
les
o
f
s
o
lar
ir
r
ad
ian
ce
an
d
P
V
g
en
e
r
atio
n
p
r
ed
ictio
n
.
T
h
e
s
ec
o
n
d
ap
p
r
o
ac
h
in
clu
d
e
s
au
to
r
eg
r
ess
io
n
(
A
R
)
-
b
ased
m
o
d
el
s
[
1
3
]
an
d
co
m
p
u
tatio
n
al
i
n
telli
g
en
ce
to
o
ls
s
u
c
h
a
s
ar
tif
icia
l
n
e
u
r
al
n
et
w
o
r
k
s
(
A
NN
s
)
[
1
4
,
1
5
]
.
T
h
e
th
ir
d
ap
p
r
o
ac
h
is
a
co
m
b
in
atio
n
o
f
th
e
t
w
o
f
ir
s
t a
p
p
r
o
ac
h
es [
1
6
,
1
7
]
.
Ha
m
m
er
,
A
.
et
al
.
p
r
o
p
o
s
ed
a
P
V
o
u
tp
u
t
f
o
r
ec
ast
in
g
m
et
h
o
d
in
w
h
ic
h
t
h
e
i
n
f
o
r
m
atio
n
co
n
ce
r
n
i
n
g
clo
u
d
m
o
v
e
m
en
t
i
n
lo
ca
l
ar
ea
f
o
r
v
er
y
s
h
o
r
t
-
ter
m
h
o
r
izo
n
s
i
n
s
atellite
i
m
ag
e
s
ar
e
u
s
ed
f
o
r
p
r
ed
ictin
g
p
r
o
ce
s
s
.
T
h
is
ca
n
i
m
p
r
o
v
e
t
h
e
f
o
r
ec
ast
i
n
g
ac
c
u
r
ac
y
to
s
o
m
e
ex
te
n
t [
1
8
]
.
A
o
ne
-
d
a
y
-
a
h
ea
d
P
V
p
o
w
er
o
u
tp
u
t
f
o
r
ec
asti
n
g
m
o
d
el
is
p
r
o
p
o
s
ed
f
o
r
a
s
in
g
le
s
tat
io
n
b
y
J
ie
Sh
i
et
al.
,
w
h
ich
is
b
ased
o
n
w
ea
t
h
er
f
o
r
ec
asti
n
g
d
ata,
ac
tu
al
h
is
to
r
ical
d
ata
o
f
p
o
w
er
o
u
t
p
u
t,
an
d
th
e
SV
M
alg
o
r
ith
m
.
T
h
e
y
ap
p
lied
th
e
m
o
d
el
i
n
to
a
P
V
s
tatio
n
in
C
h
in
a
[
4
,
1
9
]
.
T
o
p
r
ed
ict
th
e
o
u
tp
u
t
ch
ar
ac
ter
is
tics
o
f
a
co
m
m
er
cial
P
V
m
o
d
u
le,
[
1
4
]
u
s
es
a
r
ad
ial
b
asis
f
u
n
ct
io
n
n
e
u
r
al
n
et
w
o
r
k
(
R
B
FNN
)
m
o
d
el.
Ho
w
ev
er
,
co
n
s
id
er
in
g
t
h
e
n
ee
d
f
o
r
lar
g
e
tr
ain
i
n
g
d
ata,
b
o
th
SVM
a
n
d
R
B
FNN
m
o
d
el
s
s
u
f
f
er
f
r
o
m
t
h
e
d
is
ad
v
a
n
ta
g
e
o
f
h
ig
h
-
co
m
p
u
tatio
n
al
co
m
p
lex
it
y
[
6
]
.
No
w
ad
a
y
s
,
th
a
n
k
s
to
t
h
e
ad
v
an
ce
s
i
n
Me
teo
r
o
lo
g
y
,
ac
c
u
r
ate
f
o
r
ec
asti
n
g
o
f
t
h
e
s
u
n
r
ad
iatio
n
in
te
n
s
it
y
a
n
d
th
e
a
m
b
ie
n
t
w
ea
th
er
te
m
p
er
at
u
r
e
h
a
s
b
ec
o
m
e
p
o
s
s
ib
le.
Usi
n
g
a
g
lo
b
al
d
atab
ase
o
f
t
h
e
g
en
er
ate
d
elec
tr
ic
en
er
g
y
a
n
d
t
h
ese
p
ar
a
m
eter
s
,
it
is
p
o
s
s
ib
le
to
p
r
o
v
id
e
a
p
r
ec
is
e
p
r
ed
ictio
n
o
f
t
h
e
g
en
er
ated
elec
tr
ic
en
er
g
y
i
n
v
ar
io
u
s
i
n
ter
v
al
s
o
f
th
e
d
a
y
a
n
d
b
y
s
u
m
m
i
n
g
t
h
e
m
an
esti
m
atio
n
o
f
t
h
e
d
ail
y
e
le
ctr
ic
en
er
g
y
ca
n
b
e
o
b
tain
ed
.
T
o
y
ield
a
h
i
g
h
d
eg
r
ee
o
f
ac
cu
r
ac
y
,
it
i
s
n
ec
e
s
s
ar
y
to
u
s
e
a
lar
g
e
d
atab
ase
w
h
i
ch
co
v
er
s
v
ar
io
u
s
w
ea
t
h
er
co
n
d
itio
n
s
.
A
s
a
r
esu
l
t
o
f
th
e
SVM
a
n
d
R
B
FNN
m
o
d
els'
co
m
p
u
tatio
n
al
co
m
p
le
x
it
y
,
s
u
c
h
a
d
atab
ase
is
v
er
y
lar
g
e
f
o
r
t
h
ese
m
o
d
el
s
an
d
th
er
ef
o
r
e
s
i
m
p
ler
m
o
d
els
s
h
o
u
ld
b
e
in
v
e
s
tig
a
ted
.
L
ea
s
t
s
q
u
ar
es
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
s
(
L
S
-
SVMs)
ar
e
th
e
ex
ten
d
ed
v
er
s
io
n
o
f
S
VM
s
,
p
r
o
p
o
s
ed
b
y
Su
y
k
e
n
s
et
al.
[
2
0
]
,
w
h
ich
b
e
n
ef
it
f
r
o
m
r
ed
u
ce
d
ca
lcu
latio
n
s
b
esid
es
h
i
g
h
er
g
en
er
aliza
t
i
o
n
ca
p
ab
ilit
y
[
2
1
]
.
I
n
t
h
is
p
ap
er
,
a
n
o
v
el
ap
p
r
o
ac
h
f
o
r
p
o
in
t
to
p
o
in
t
p
r
ed
ictio
n
o
f
t
h
e
g
e
n
er
ate
d
P
V
p
o
w
er
b
ased
o
n
L
S
-
SVMs
h
as
b
ee
n
p
r
o
p
o
s
ed
w
h
ich
ca
n
p
r
o
v
id
e
th
e
n
ec
ess
ar
y
f
o
r
ec
ast
in
g
ac
c
u
r
ac
y
an
d
d
o
es
n
o
t
s
u
f
f
er
f
r
o
m
th
e
h
ig
h
-
co
m
p
u
tatio
n
al
co
m
p
le
x
it
y
.
T
h
e
r
est
o
f
th
e
p
ap
er
is
o
r
g
an
ize
d
as
f
o
llo
w
s
:
Sect
io
n
3
d
escr
ib
es
th
e
least
s
q
u
ar
e
s
s
u
p
p
o
r
t v
ec
t
o
r
m
ac
h
in
e
s
an
d
S
ec
tio
n
4
p
r
esen
ts
t
h
e
r
esu
lts
a
n
d
an
al
y
s
is
.
3.
L
E
A
ST
S
Q
U
ARE
S SUP
P
O
RT
VE
CT
O
R
M
ACH
I
NE
S
Su
p
p
o
r
t
v
ec
to
r
m
ac
h
i
n
es
(
SV
Ms)
,
ar
e
s
u
p
er
v
is
ed
lear
n
in
g
m
o
d
el
s
w
h
ic
h
ar
e
u
s
ed
f
o
r
class
i
f
icatio
n
an
d
r
eg
r
es
s
io
n
a
n
al
y
s
i
s
.
I
n
iti
al
v
er
s
io
n
o
f
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
s
w
a
s
an
ex
ten
s
io
n
o
f
t
h
e
Ge
n
er
alize
d
P
o
r
tr
ait
alg
o
r
ith
m
,
w
h
ic
h
w
as
d
ev
elo
p
ed
in
1
9
6
0
s
.
Ho
w
ev
e
r
,
th
e
c
u
r
r
en
t
v
er
s
io
n
w
a
s
d
ev
elo
p
ed
b
y
Vap
n
i
k
an
d
h
i
s
co
w
o
r
k
er
s
at
A
T
&
T
B
ell
L
ab
o
r
ato
r
ies i
n
1
9
9
0
s
[
2
2
]
.
I
n
SVM
-
b
ased
r
eg
r
e
s
s
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ased
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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p
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r
esp
ec
ti
v
el
y
;
i
s
th
e
in
p
u
t
o
r
o
u
tp
u
t
a
n
d
is
th
e
co
r
r
esp
o
n
d
in
g
n
o
r
m
alize
d
v
alu
e.
Us
in
g
t
h
e
n
o
r
m
alize
d
m
ea
s
u
r
e
m
en
t
s
,
L
S
-
S
VM
m
o
d
el
s
w
er
e
i
m
p
le
m
en
ted
b
y
t
h
e
L
S
-
SVM
lab
to
o
lb
o
x
v
er
s
io
n
1
.
8
o
f
MA
T
L
A
B
.
I
n
t
h
is
to
o
lb
o
x
,
a
f
u
n
ct
io
n
i
s
p
r
o
v
id
ed
f
o
r
t
u
n
i
n
g
th
e
k
er
n
el
a
n
d
m
o
d
el
p
ar
a
m
e
ter
s
[
2
8
]
.
T
h
e
R
B
F
k
er
n
el
f
u
n
ctio
n
w
as
p
r
ef
er
r
ed
.
T
h
e
v
alu
es
o
f
th
e
r
eg
u
lar
izati
o
n
p
ar
am
eter
(
C
)
an
d
R
B
F
k
er
n
el
p
ar
am
e
ter
(
2
)
ar
e
lis
ted
in
T
ab
le
2
.
T
h
e
o
u
tp
u
ts
w
er
e
p
r
ed
ict
ed
b
y
t
h
e
m
o
d
els an
d
w
er
e
s
ca
led
to
th
eir
o
r
ig
in
a
l r
an
g
e
as
̂
=
∗
(
−
2
)
+
(
+
2
)
(
1
2
)
I
n
w
h
ic
h
̂
is
th
e
p
r
ed
icted
o
u
tp
u
t
in
t
h
e
o
r
ig
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al
r
a
n
g
e
a
n
d
is
th
e
n
o
r
m
alize
d
o
u
tp
u
t
p
r
ed
icted
b
y
th
e
m
o
d
els.
T
ab
le
2.
T
h
e
LS
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SVM
m
o
d
el
an
d
k
er
n
el
p
ar
a
m
e
ter
s
P
a
r
a
me
t
e
r
V
a
l
u
e
R
B
F
K
e
r
n
e
l
p
a
r
a
me
t
e
r
(
2
)
0
.
0
2
8
9
R
e
g
u
l
a
r
i
z
a
t
i
o
n
f
a
c
t
o
r
(
C
)
4
.
9
1
1
9
T
o
ev
alu
ate
th
e
ac
c
u
r
ac
y
o
f
t
h
e
L
S
-
SVM
m
o
d
els,
th
e
m
ea
s
u
r
e
m
en
t
s
o
f
t
h
e
g
e
n
er
ated
p
o
w
er
in
ea
ch
f
i
v
e
m
in
u
te
s
f
o
r
1
6
o
th
er
d
a
y
s
w
er
e
u
s
ed
.
Si
n
ce
t
h
e
w
ea
t
h
er
te
m
p
er
atu
r
e
i
s
m
ai
n
l
y
p
r
ed
icted
b
ased
o
n
its
v
alu
e
s
i
n
th
e
m
o
r
n
i
n
g
,
n
o
o
n
,
af
ter
n
o
o
n
an
d
a
t
n
i
g
h
t,
a
t
w
o
-
ter
m
e
x
p
o
n
en
t
ial
f
u
n
ctio
n
,
d
ef
i
n
ed
in
(
1
3
)
w
a
s
u
s
ed
f
o
r
m
o
d
elli
n
g
th
e
a
m
b
ien
t te
m
p
er
atu
r
e.
=
+
(1
3)
I
n
(
1
3
)
,
T
is
th
e
a
m
b
ien
t
te
m
p
er
at
u
r
e
in
d
eg
r
ee
s
C
elsi
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s
an
d
t
is
th
e
ti
m
e.
I
n
ca
s
e
o
f
th
e
s
o
lar
r
ad
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n
in
te
n
s
it
y
,
t
h
e
o
r
ig
i
n
a
l
m
ea
s
u
r
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m
en
t
s
w
er
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u
s
ed
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s
i
n
ce
th
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p
ar
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m
eter
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e
o
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tain
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ased
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th
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atellite
i
m
a
g
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B
ased
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n
th
e
tr
ain
ed
L
S
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SV
M
m
o
d
els
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d
t
h
e
p
r
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icted
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alu
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te
m
p
er
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in
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t
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ter
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t
h
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ate
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ter
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th
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ail
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o
r
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asted
p
o
w
er
w
as
o
b
tain
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b
y
s
u
m
m
atio
n
o
f
th
ese
v
alu
e
s
.
T
h
e
p
r
e
d
icted
o
u
tp
u
ts
alo
n
g
s
id
e
w
it
h
th
eir
co
r
r
esp
o
n
d
in
g
tar
g
e
t
v
alu
e
s
ar
e
d
ep
icted
in
Fig
u
r
e
2
,
w
h
ic
h
d
ep
icts
t
h
e
L
S
-
SVM
m
o
d
el
s
'
p
r
ec
is
io
n
.
Fig
u
r
e
2
.
P
r
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icte
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an
d
m
ea
s
u
r
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v
alu
es o
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t
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V
g
e
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ated
p
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t sa
m
p
les
T
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p
r
e
d
ictio
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ac
cu
r
ac
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th
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f
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m
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s
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ated
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ased
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n
th
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m
a
x
i
m
u
m
ab
s
o
lu
te
p
er
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n
tag
e
er
r
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r
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M
A
PE
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an
d
th
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co
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f
icien
t o
f
d
eter
m
i
n
atio
n
(
R
2
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s
tatis
t
ical
in
d
ice
s
,
d
ef
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n
ed
i
n
(
1
4
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an
d
(
1
5
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.
=
∑
|
−
̂
|
=
1
∗
100%
(
1
4
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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8
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n
t
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(
1
4
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d
(
1
5
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,
an
d
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th
e
m
ea
s
u
r
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d
t
h
e
p
r
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d
icted
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u
tp
u
ts
,
r
e
s
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ec
tiv
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t
h
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u
m
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er
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s
a
m
p
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d
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d
m
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ts
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n
d
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e
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ea
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e
o
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th
e
m
ea
s
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r
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o
u
tp
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t,
ca
lc
u
lated
as
(
1
6
)
.
̅
=
∑
=
1
(
1
6
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T
h
e
ca
lcu
lated
v
a
lu
e
s
o
f
i
n
d
ic
es a
r
e
lis
ted
in
T
ab
le
3
.
T
ab
le
3
.
Mo
d
ellin
g
i
n
d
ices
2
3
.
5
5
%
9
6
.
7
3
5.
CO
NCLU
SI
O
N
A
cc
u
r
ate
f
o
r
ec
ast
in
g
o
f
t
h
e
g
en
er
ated
s
o
lar
p
h
o
to
v
o
ltaic
p
o
w
er
is
an
i
m
p
o
r
tan
t
tas
k
i
n
elec
tr
ic
d
is
tr
ib
u
tio
n
s
y
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te
m
.
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n
th
is
p
ap
er
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a
n
o
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el
p
o
in
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to
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t
f
o
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as
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ee
n
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f
o
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th
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p
u
r
p
o
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e,
w
h
ic
h
is
b
ased
o
n
t
h
e
least
s
q
u
ar
es
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
L
S
-
S
VM
)
.
T
h
e
r
ed
u
ce
d
n
ec
ess
ar
y
ca
lcu
latio
n
s
o
f
t
h
e
L
S
-
S
V
M
alg
o
r
ith
m
p
r
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id
ed
th
e
p
o
s
s
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y
o
f
u
s
i
n
g
a
lar
g
e
tr
ain
in
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d
atab
a
s
e,
co
r
r
esp
o
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d
in
g
to
th
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w
id
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u
m
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ical
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an
g
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o
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th
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a
m
b
ie
n
t
w
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t
h
er
te
m
p
er
at
u
r
e
an
d
s
o
lar
r
ad
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n
in
te
n
s
it
y
.
A
t
w
o
-
ter
m
e
x
p
o
n
en
tial
f
u
n
ct
io
n
w
as
u
s
ed
to
m
o
d
el
t
h
e
a
m
b
ien
t
te
m
p
er
at
u
r
e
a
n
d
th
e
t
e
m
p
er
atu
r
e
i
n
ea
c
h
5
-
m
i
n
u
te
i
n
ter
v
al
w
as
o
b
tain
e
d
.
Usi
n
g
th
e
tr
ain
ed
L
S
-
SVM
m
o
d
els
an
d
t
h
e
p
r
ed
icted
v
al
u
es
o
f
te
m
p
er
at
u
r
e
to
g
eth
er
w
it
h
t
h
e
v
al
u
es
o
f
s
o
lar
r
ad
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n
i
n
te
n
s
it
y
,
th
e
g
e
n
er
ated
elec
tr
ic
e
n
er
g
y
in
ea
ch
in
ter
v
al
w
a
s
o
b
tain
ed
an
d
th
e
f
o
r
ec
asted
to
tal
elec
tr
ic
en
er
g
y
w
as
ca
lc
u
la
ted
b
y
s
u
m
m
a
tio
n
o
f
t
h
e
o
b
t
ain
ed
v
al
u
es
i
n
ea
ch
d
ay
.
E
r
r
o
r
an
al
y
s
is
o
f
t
h
e
r
es
u
lts
s
h
o
w
t
h
at
t
h
e
p
r
o
p
o
s
ed
m
e
th
o
d
b
en
ef
it
s
f
r
o
m
a
h
i
g
h
d
e
g
r
ee
o
f
ac
cu
r
ac
y
a
n
d
g
en
er
aliza
tio
n
ca
p
ab
ilit
y
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a
n
d
r
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ce
s
t
h
e
co
m
p
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tatio
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co
m
p
lex
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m
p
ar
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e
p
r
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u
s
m
et
h
o
d
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d
th
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o
r
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ca
n
b
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s
ed
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o
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ac
cu
r
ate
f
o
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ec
asti
n
g
o
f
t
h
e
g
e
n
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at
ed
P
V
p
o
w
er
.
ACK
NO
WL
E
D
G
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M
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NT
S
T
h
e
p
r
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t
p
ap
er
is
b
a
s
ed
o
n
th
e
d
ata
o
f
a
2
0
KW
s
o
lar
p
o
w
er
p
lan
t
an
d
t
h
e
ae
r
ial
d
ata
lo
ca
ted
in
Un
i
v
er
s
it
y
o
f
B
ir
j
an
d
.
T
h
e
au
th
o
r
s
ar
e
ea
g
er
to
e
x
p
r
ess
t
h
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ee
p
est
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at
itu
d
e
to
t
h
e
P
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E
n
g
in
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g
Dep
ar
t
m
en
t o
f
th
e
U
n
i
v
er
s
it
y
o
f
B
ir
j
an
d
,
B
ir
j
an
d
,
I
r
an
.
RE
F
E
R
E
NC
E
S
[1
]
A
.
A
lq
a
h
tan
i
,
e
t
a
l.
,
"
P
h
o
to
v
o
lt
a
i
c
p
o
w
e
r
f
o
re
c
a
stin
g
m
o
d
e
l
b
a
se
d
o
n
n
o
n
li
n
e
a
r
sy
ste
m
id
e
n
ti
f
ic
a
ti
o
n
,
"
Ca
n
a
d
i
a
n
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
C
o
mp
u
t
e
r E
n
g
i
n
e
e
rin
g
,
v
o
l.
3
9
,
n
o
.
3
,
p
p
.
2
4
3
-
2
5
0
,
2
0
1
6
.
[2
]
A
.
N
a
w
a
b
jan
,
e
t
a
l.
,
"
A
F
ro
n
t
S
u
rf
a
c
e
Op
ti
m
iz
a
ti
o
n
S
t
u
d
y
f
o
r
P
h
o
to
v
o
l
taic
A
p
p
li
c
a
ti
o
n
,
"
T
EL
KOM
NIKA
(
T
e
lec
o
mm
u
n
ica
ti
o
n
,
Co
m
p
u
t
in
g
,
El
e
c
tro
n
ics
a
n
d
Co
n
tro
l)
,
v
o
l.
1
6
,
n
o
.
4
,
p
p
.
1
3
8
3
-
1
3
8
7
,
2
0
1
8
.
[3
]
T
.
T
.
T
e
o
,
e
t
a
l.
,
"
F
o
re
c
a
stin
g
o
f
p
h
o
to
v
o
lt
a
ic
p
o
w
e
r
u
sin
g
e
x
tr
e
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lea
rn
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M
a
c
h
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e
,
"
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n
o
v
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S
ma
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Gr
id
T
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-
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GT
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IA)
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h
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.
2
0
1
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.
[4
]
J.
S
h
i
,
e
t
a
l.
,
"
F
o
re
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a
stin
g
p
o
w
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r
o
u
t
p
u
t
o
f
p
h
o
to
v
o
l
taic
sy
ste
m
s
b
a
se
d
o
n
w
e
a
th
e
r
c
las
si
f
ica
ti
o
n
a
n
d
su
p
p
o
rt
v
e
c
to
r
m
a
c
h
in
e
s,"
IEE
E
T
ra
n
sa
c
ti
o
n
s o
n
In
d
u
stry
Ap
p
li
c
a
t
io
n
s
,
v
o
l.
4
8
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o
3
,
p
p
.
1
0
6
4
-
1
0
6
9
,
M
a
y
2
0
1
2
.
[5
]
In
tern
a
ti
o
n
a
l
E
n
e
rg
y
Ag
e
n
c
y
,
Ke
y
wo
rld
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e
rg
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st
a
ti
stic
,
I
n
tern
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ti
o
n
a
l
E
n
e
rg
y
Ag
e
n
c
y
,
2
0
1
4
.
[6
]
Z.
L
i
,
e
t
a
l.
,
"
Da
y
-
a
h
e
a
d
h
o
u
r
ly
p
h
o
to
v
o
l
taic
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e
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e
ra
ti
o
n
f
o
re
c
a
s
ti
n
g
u
sin
g
e
x
trem
e
lea
rn
in
g
m
a
c
h
in
e
,
"
T
h
e
5
th
An
n
u
a
l
I
EE
E
I
n
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
Cy
b
e
r
T
e
c
h
n
o
lo
g
y
i
n
Au
t
o
ma
t
io
n
,
Co
n
tro
l
a
n
d
I
n
te
ll
ig
e
n
t
S
y
ste
ms
,
S
h
e
n
y
a
n
g
,
Ch
in
a
,
Ju
n
e
8
-
1
2
,
2
0
1
5
.
[7
]
K.
Ka
wa
b
e
a
n
d
K.
T
a
n
a
k
a
,
"
I
m
p
a
c
t
o
f
d
y
n
a
m
ic
b
e
h
a
v
io
r
o
f
p
h
o
to
v
o
lt
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ic
p
o
w
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r
g
e
n
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ra
ti
o
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sy
st
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m
s
o
n
s
h
o
rt
-
term
v
o
lt
a
g
e
sta
b
il
it
y
,
"
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
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r S
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ms
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.
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p
.
3
4
1
6
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4
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v
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2
0
1
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[8
]
M
.
A
.
Aw
a
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,
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t
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,
"
I
m
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istri
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"
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8
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1
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p
p
.
4
5
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,
2
0
1
5
.
[9
]
S
.
Ef
tek
h
a
rn
e
jad
,
e
t
a
l.
,
"
Op
ti
m
a
l
g
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n
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ra
ti
o
n
d
isp
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tch
w
it
h
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ig
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tratio
n
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f
p
h
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t
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v
o
lt
a
ic
g
e
n
e
ra
ti
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n
,
"
IEE
E
T
ra
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c
ti
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rg
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6
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.
3
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1
0
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3
–
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0
,
Ju
l.
2
0
1
5
.
[1
0
]
J.
L
iu
,
e
t
a
l.
,
"
A
n
i
m
p
ro
v
e
d
p
h
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lt
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ic
p
o
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g
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w
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ss
istan
c
e
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f
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ro
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in
d
e
x
d
a
ta,
"
IEE
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T
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sa
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ti
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o
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S
u
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le E
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e
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y
,
v
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l.
6
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.
2
,
p
p
.
4
3
4
–
4
4
2
,
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p
r.
2
0
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2211
[1
1
]
C.
Ya
n
g
,
e
t
a
l.
,
"
M
u
lt
it
im
e
-
sc
a
le
d
a
ta
-
d
riv
e
n
sp
a
ti
o
-
tem
p
o
ra
l
f
o
re
c
a
st
o
f
p
h
o
to
v
o
lt
a
ic
g
e
n
e
ra
ti
o
n
,
"
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
S
u
sta
i
n
a
b
le E
n
e
rg
y
,
v
o
l.
6
,
n
o
1
,
p
p
.
1
0
4
-
1
1
2
,
2
0
1
5
.
[1
2
]
L
.
A
.
F
e
rn
a
n
d
e
z
-
Jim
e
n
e
z
,
e
t
a
l
.
,
"
S
h
o
rt
-
term
p
o
w
e
r
f
o
re
c
a
stin
g
s
y
ste
m
f
o
r
p
h
o
t
o
v
o
lt
a
ic
p
lan
t
s,
"
J
o
u
rn
a
l
o
f
Ren
e
wa
b
le E
n
e
rg
y
,
v
o
l
4
4
,
p
p
.
3
1
1
-
3
1
7
,
2
0
1
2
.
[1
3
]
C.
Be
n
S
a
lah
,
e
t
a
l.
,
"
W
a
v
e
let
a
u
to
re
g
re
ss
iv
e
f
o
re
c
a
stin
g
o
f
c
li
m
a
t
ic
p
a
ra
m
e
ter
s
f
o
r
p
h
o
to
v
o
lt
a
ic
sy
ste
m
s,
"
T
h
e
8
th
In
ter
n
a
t
io
n
a
l
M
u
lt
i
-
Co
n
fer
e
n
c
e
o
n
S
y
ste
ms
,
S
i
g
n
a
ls
a
n
d
De
v
ice
s (
S
S
D)
,
2
0
1
1
.
[1
4
]
W
.
Y.
Ch
a
n
g
,
"
P
o
w
e
r
g
e
n
e
ra
ti
o
n
f
o
re
c
a
stin
g
o
f
so
lar
p
h
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to
v
o
lt
a
ic
s
y
st
e
m
u
sin
g
ra
d
ial
b
a
sis
f
u
n
c
ti
o
n
n
e
u
ra
l
n
e
tw
o
rk
,"
T
ra
n
s
T
e
c
h
Pu
b
li
c
a
t
i
o
n
s
Fro
n
t
ier
s
o
f
Gr
e
e
n
Bu
il
d
in
g
,
M
a
ter
ia
ls
a
n
d
Civil
En
g
i
n
e
e
rin
g
,
p
p
.
1
2
6
2
-
1
2
6
5
,
2
0
1
3
.
[1
5
]
M
.
H.
A
lo
m
a
ri
,
e
t
a
l.
,
"
S
o
lar
p
h
o
t
o
v
o
lt
a
ic
p
o
w
e
r
f
o
re
c
a
stin
g
in
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rd
a
n
u
sin
g
a
rti
f
icia
l
n
e
u
ra
l
n
e
tw
o
rk
s,
"
In
ter
n
a
t
io
n
a
l
J
o
u
rn
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l
o
f
E
lec
trica
l
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n
d
C
o
mp
u
ter
En
g
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rin
g
,
v
o
l
8
,
n
o
1
,
F
e
b
.
2
0
1
8
.
[1
6
]
M
.
G
.
Kr
a
tze
n
b
e
rg
a
n
d
H.
G
.
Be
y
e
r,
"
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o
lar
ra
d
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n
p
re
d
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o
n
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se
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o
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th
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c
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b
in
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ti
o
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f
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m
e
rica
l
we
a
th
e
r
p
re
d
ictio
n
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o
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a
n
d
a
ti
m
e
s
e
r
ies
p
re
d
ictio
n
m
o
d
e
l,
"
T
h
e
1
st
In
ter
n
a
ti
o
n
a
l
Co
n
g
re
ss
o
n
He
a
t
Co
o
l
Bu
il
d
i
n
g
,
L
isb
o
n
,
P
o
rt
u
g
a
l,
2
0
0
8
.
[1
7
]
J.
S
.
Ku
m
a
ri
a
n
d
C.
S
.
Ba
b
u
,
"
M
a
th
e
m
a
ti
c
a
l
m
o
d
e
li
n
g
a
n
d
si
m
u
latio
n
o
f
p
h
o
to
v
o
lt
a
ic
c
e
ll
u
sin
g
m
a
tl
a
b
-
si
m
u
li
n
k
e
n
v
iro
n
m
e
n
t,
"
In
ter
n
a
ti
o
n
a
l
J
o
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l
o
f
E
lec
trica
l
a
n
d
Co
mp
u
ter
E
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g
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v
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l
2
,
n
o
1
,
F
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b
.
2
0
1
2
.
[1
8
]
A
.
Ha
m
m
e
r
,
e
t
a
l.
,
"
S
o
lar
e
n
e
rg
y
a
ss
e
ss
m
e
n
t
u
sin
g
re
m
o
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se
n
sin
g
tec
h
n
o
l
o
g
ies
,
"
Rem
o
te
S
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n
sin
g
o
f
En
v
iro
n
me
n
t
,
p
p
.
4
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-
4
3
2
,
v
o
l.
8
6
,
n
o
.
3
,
2
0
0
3
.
[1
9
]
S
.
Jie
,
e
t
a
l.
,
"
F
o
re
c
a
stin
g
p
o
w
e
r
Ou
tp
u
t
o
f
p
h
o
to
v
o
lt
a
ic
sy
ste
m
s
b
a
se
d
o
n
w
e
a
th
e
r
c
la
ss
i
f
ica
ti
o
n
a
n
d
su
p
p
o
rt
v
e
c
to
r
m
a
c
h
in
e
s,
"
IEE
E
T
ra
n
sa
c
ti
o
n
s o
n
In
d
u
stry
Ap
p
li
c
a
t
io
n
s
,
v
o
l.
4
8
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o
3
,
p
p
.
1
0
6
4
-
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0
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9
,
2
0
1
2
.
[2
0
]
J.
A
.
S
u
y
k
e
n
s,
T
.
V
a
n
G
e
ste
l,
J.
De
Bra
b
a
n
ter,
"
L
e
a
st sq
u
a
re
s su
p
p
o
rt
v
e
c
to
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m
a
c
h
in
e
s
,
"
W
o
rld
S
c
i
e
n
ti
f
ic,
2
0
0
2
.
[2
1
]
T
.
V
a
n
G
e
ste
l
,
e
t
a
l.
,
"
Be
n
c
h
m
a
rk
in
g
lea
st
sq
u
a
re
s
su
p
p
o
rt
v
e
c
to
r
m
a
c
h
in
e
c
las
sif
i
e
rs,
"
M
a
c
h
in
e
lea
rn
in
g
,
v
o
l.
5
4
,
n
o
1
,
p
p
.
5
-
3
2
,
2
0
0
4
.
[2
2
]
C.
Co
rtes
a
n
d
V
.
V
a
p
n
ik
,
"
S
u
p
p
o
rt
-
v
e
c
to
r
n
e
tw
o
rk
s,
"
M
a
c
h
in
e
L
e
a
rn
in
g
,
v
o
l.
2
0
,
n
o
3
,
p
p
.
2
7
3
,
1
9
9
5
.
[2
3
]
G
.
T
.
N
g
o
,
e
t
a
l.
,
"
Im
a
g
e
re
tri
e
v
a
l
w
it
h
re
lev
a
n
c
e
fe
e
d
b
a
c
k
u
sin
g
S
V
M
a
c
ti
v
e
lea
rn
in
g
,
"
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
,
v
o
l.
6
,
n
o
.
6
,
p
p
.
3
2
-
3
8
,
2
0
1
6
.
[
24
]
L
.
Ge
n
g
,
e
t
a
l.
,
"
F
o
re
c
a
stin
g
ra
n
g
e
v
o
latil
it
y
u
sin
g
su
p
p
o
r
t
v
e
c
to
r
m
a
c
h
in
e
s
w
it
h
im
p
ro
v
e
d
P
S
O
a
lg
o
rit
h
m
s,
"
T
e
lko
mn
ika
(
T
e
lec
o
mm
u
n
ica
ti
o
n
Co
mp
u
t
in
g
El
e
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tro
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