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ch
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lear
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Prin
cip
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p
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R
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[
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a
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Step
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a
l
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[
2
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.
Acc
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r
d
i
n
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W
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[
3
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,
p
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v
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ca
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p
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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Dr
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,
Vo
l.
16
,
No
.
4
,
Dec
em
b
er
20
25
:
2645
-
2
6
5
4
2646
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[
6
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7
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lo
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ap
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[
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etwo
r
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r
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d
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f
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r
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Prin
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[
2
2
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an
d
p
r
o
ce
s
s
in
g
ef
f
icien
cy
.
T
h
e
wo
r
k
s
in
[
2
3
]
–
[
2
5
]
s
ep
ar
ated
p
h
o
to
v
o
ltaic
f
o
r
ec
asts
in
to
th
r
ee
ca
teg
o
r
ies:
im
ag
e
-
b
ased
m
eth
o
d
s
u
tili
zin
g
clo
u
d
m
o
tio
n
v
ec
to
r
s
(
C
MV
s
)
,
s
tatis
t
ical
m
eth
o
d
s
(
e.
g
.
,
AR
I
MA
,
AI
-
b
ased
SVMs,
ANNs)
,
an
d
n
u
m
er
ical
wea
th
er
p
r
ed
ictio
n
(
NW
P)
m
o
d
els.
C
MV
s
em
p
lo
y
s
atellite
o
r
g
r
o
u
n
d
im
a
g
es
f
o
r
s
h
o
r
t
-
ter
m
f
o
r
ec
asts
,
wh
ile
NW
P
m
o
d
els,
lik
e
W
R
F
an
d
its
u
r
b
an
ized
eq
u
iv
alen
t
u
W
R
F,
d
o
well
ac
r
o
s
s
o
n
e
to
th
r
ee
-
d
ay
tim
escales.
Un
d
er
v
ar
io
u
s
co
n
d
itio
n
s
,
th
ese
tech
n
iq
u
es
in
cr
ea
s
e
th
e
ac
cu
r
ac
y
o
f
s
o
lar
f
o
r
ec
asti
n
g
b
y
tak
in
g
in
to
ac
co
u
n
t v
ar
io
u
s
tem
p
o
r
al
an
d
s
p
atial
r
eq
u
ir
e
m
en
ts
[
2
3
]
–
[
2
5
]
.
Fig
u
r
e
1
s
h
o
ws
th
e
Ab
io
d
Sid
C
h
eik
h
s
o
lar
p
o
wer
s
tatio
n
l
o
ca
tio
n
.
T
h
is
s
tu
d
y
m
ak
es
u
s
e
o
f
a
lar
g
e
d
ataset
f
r
o
m
a
2
3
MW
s
o
lar
f
ac
ilit
y
at
Ab
io
d
Sid
i
C
h
eik
h
,
Alg
er
ia,
wh
ich
in
clu
d
ed
7
2
6
,
0
1
2
m
ea
s
u
r
em
e
n
ts
tak
en
at
1
5
-
m
in
u
te
in
ter
v
als
b
etwe
en
J
an
u
ar
y
2
0
1
9
an
d
Dec
em
b
er
2
0
2
1
.
Date
an
d
tim
e,
s
u
n
ir
r
ad
iatio
n
,
tem
p
er
atu
r
e,
p
r
ess
u
r
e,
h
u
m
id
i
ty
,
win
d
s
p
ee
d
,
an
d
p
o
wer
o
u
tp
u
t
ar
e
th
e
s
ev
en
cr
u
cial
ch
a
r
ac
ter
is
tics
th
at
ar
e
r
ec
o
r
d
e
d
in
th
e
d
ataset.
Acc
o
r
d
in
g
to
s
ea
s
o
n
al
s
tu
d
y
,
en
er
g
y
p
r
o
d
u
ctio
n
p
ea
k
s
in
th
e
s
p
r
in
g
an
d
s
u
m
m
er
,
with
an
8
.
6
2
GW
o
u
tp
u
t in
Ap
r
il 2
0
1
9
.
Acc
o
r
d
in
g
t
o
s
tatis
tical
a
n
aly
s
is
,
th
e
av
er
ag
e
s
o
lar
ir
r
ad
iatio
n
is
4
5
5
W
/m
2
,
an
d
th
er
e
is
m
o
d
er
ate
v
ar
iatio
n
in
th
e
ir
r
a
d
ian
ce
,
h
u
m
id
ity
,
an
d
p
r
ess
u
r
e
(
3
3
–
6
0
%
C
V)
,
b
u
t
th
er
e
ar
e
n
o
tab
le
v
ar
iatio
n
s
in
tem
p
er
atu
r
e
an
d
win
d
s
p
ee
d
(
u
p
t
o
3
1
4
%
C
V)
.
T
h
is
d
ataset
h
ig
h
lig
h
ts
th
e
r
e
g
io
n
'
s
p
o
ten
tial
f
o
r
p
r
o
d
u
cin
g
r
e
n
ewa
b
le
en
er
g
y
a
n
d
aid
s
in
th
e
cr
ea
tio
n
o
f
p
r
ec
i
s
e
s
o
lar
f
o
r
ec
asti
n
g
m
o
d
els b
y
o
f
f
er
in
g
in
s
ig
h
tf
u
l
in
f
o
r
m
atio
n
o
n
d
aily
an
d
s
ea
s
o
n
al
f
lu
ctu
atio
n
s
.
Fig
u
r
e
1
.
Ab
i
o
d
Sid
C
h
eik
h
s
o
lar
p
o
wer
s
tatio
n
lo
ca
tio
n
2.
M
E
T
H
O
D
T
h
e
s
u
p
p
ly
f
o
r
ec
ast
f
r
am
ewo
r
k
,
wh
ich
f
o
r
ec
asts
g
lo
b
al
h
o
r
izo
n
tal
ir
r
ad
ian
ce
(
GHI
)
f
o
r
PV
p
an
els
ac
r
o
s
s
s
h
o
r
t
to
lo
n
g
tim
e
h
o
r
i
zo
n
s
,
th
e
in
itial
s
tag
e
o
f
t
h
e
s
o
lar
en
er
g
y
f
o
r
ec
ast
m
eth
o
d
o
l
o
g
y
.
T
h
e
m
o
d
el
is
b
ased
o
n
a
h
u
g
e
d
ataset
th
at
was
g
ath
er
ed
u
s
in
g
a
p
y
r
a
n
o
m
eter
at
1
0
-
s
ec
o
n
d
in
te
r
v
als
o
v
er
a
p
er
io
d
o
f
s
ix
y
ea
r
s
.
Miss
in
g
v
alu
es,
o
u
tlier
s
,
an
d
clea
r
-
s
k
y
ir
r
ad
ia
n
ce
(
GH
I
cs)
ar
e
all
p
ar
t
o
f
d
ata
p
r
ep
r
o
ce
s
s
in
g
.
Nig
h
ttime
d
ata
is
also
r
em
o
v
ed
,
an
d
th
e
clea
r
-
s
k
y
in
d
e
x
(
k
cs)
is
ca
lcu
l
ated
.
T
o
en
s
u
r
e
b
etter
c
o
n
s
i
s
ten
cy
an
d
p
r
ec
is
io
n
o
f
th
e
d
ata
an
d
ca
p
tu
r
e
v
a
r
iatio
n
s
,
s
ea
s
o
n
al
tr
en
d
s
,
a
n
d
d
er
i
v
ativ
e
f
ea
tu
r
es
a
r
e
in
clu
d
ed
.
Key
GHI
m
ea
s
u
r
es,
d
er
iv
ativ
es,
an
d
s
ea
s
o
n
al
in
d
i
ca
to
r
s
ar
e
am
o
n
g
t
h
e
im
p
r
o
v
e
d
f
ea
tu
r
e
s
et
th
at
is
p
r
o
d
u
ce
d
b
y
d
o
wn
-
s
am
p
lin
g
th
e
d
ata
to
ac
c
o
m
m
o
d
ate
v
ar
i
o
u
s
f
o
r
ec
asti
n
g
in
te
r
v
als.
T
h
e
m
ea
n
ir
r
ad
ia
n
ce
ac
r
o
s
s
s
p
ec
if
ied
tim
e
p
er
io
d
s
is
p
r
ed
icted
lev
er
a
g
in
g
a
d
ee
p
le
ar
n
in
g
m
o
d
el
b
ased
o
n
L
STM
.
T
im
e
-
s
er
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cr
o
s
s
-
v
alid
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with
R
2
,
AE
,
an
d
R
MSE
m
etr
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s
er
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es
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alid
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p
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o
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th
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em
o
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tr
ated
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e
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u
g
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ested
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ch
itectu
r
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Fig
u
r
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er
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h
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d
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ata
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Evaluation Warning : The document was created with Spire.PDF for Python.
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6
9
4
E
n
h
a
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ce
d
in
teg
r
a
tio
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f ren
e
w
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t g
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h
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a
th
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ve
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2647
an
d
p
r
esen
tatio
n
.
PC
A
d
etec
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a
s
m
aller
c
o
llectio
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o
f
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c
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r
r
elate
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ith
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ile
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9
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3
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ata
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ar
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ce
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n
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ce
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ac
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es,
th
e
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at
aset is r
ed
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ce
d
to
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im
en
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ak
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g
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u
r
th
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aly
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ea
s
ier
.
B
y
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h
e
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m
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e
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ar
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ile
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e
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im
p
o
r
ta
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t
in
f
o
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m
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,
th
e
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A
tech
n
iq
u
e
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im
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lify
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atasets
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ize
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ch
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a
n
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ality
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ata
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im
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ality
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l
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m
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o
m
ai
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lik
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eteo
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Fig
u
r
e
2
.
Pro
p
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ed
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p
p
ly
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ec
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g
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r
am
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r
k
Ho
u
r
ly
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m
o
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th
ly
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al
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th
e
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tu
d
y
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e
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r
e
d
is
p
lay
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in
Fig
u
r
es
3
(
a
)
a
n
d
3
(
b
)
.
Me
asu
r
es
s
u
ch
as
m
ea
n
a
b
s
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te
er
r
o
r
(
MA
E
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o
o
t
m
ea
n
s
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u
ar
e
er
r
o
r
(
R
MSE
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,
R
2
,
an
d
Ad
ju
s
ted
R
2
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e
u
s
ed
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alu
ate
th
e
ef
f
ec
tiv
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n
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o
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ea
ch
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ac
h
in
e
lear
n
in
g
m
o
d
el
u
s
ed
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th
is
in
v
esti
g
atio
n
.
T
h
ese
m
ea
s
u
r
es
ev
alu
ate
th
e
m
o
d
els'
p
r
ed
icti
o
n
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er
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o
r
m
an
ce
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y
c
o
n
tr
asti
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g
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p
ec
ted
v
alu
es
(
Xi)
with
ac
tu
al
m
ea
s
u
r
em
en
ts
(
Yi)
.
T
h
e
co
m
p
u
tatio
n
s
also
tak
e
in
to
ac
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u
n
t
th
e
m
ea
n
o
f
th
e
o
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s
er
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alu
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t
o
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iv
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a
th
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g
h
ass
ess
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en
t
o
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th
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cu
r
ac
y
an
d
d
ep
en
d
a
b
ilit
y
o
f
th
e
m
o
d
els.
T
ab
le
1
s
h
o
ws
th
e
PC
A
f
in
d
in
g
s
an
d
s
p
ec
if
ic
p
r
o
p
er
ties
.
2
.
1
.
M
o
dels
o
f
f
o
re
ca
s
t
ing
T
h
e
s
ix
m
ac
h
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e
lear
n
in
g
f
o
r
e
ca
s
tin
g
tech
n
iq
u
es
ev
alu
ated
i
n
th
is
s
tu
d
y
in
clu
d
e
R
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n
eu
r
a
l
n
etwo
r
k
s
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NN
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,
k
-
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e
a
re
st
n
e
ig
h
b
o
r
(
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,
Ad
aBo
o
s
t,
L
R
,
an
d
GB
.
E
ac
h
alg
o
r
ith
m
a
p
p
lies
a
u
n
iq
u
e
lear
n
in
g
ap
p
r
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ac
h
an
d
o
p
tim
izatio
n
s
tr
ateg
y
t
o
im
p
r
o
v
e
f
o
r
ec
asti
n
g
ac
cu
r
a
cy
an
d
m
o
d
el
p
er
f
o
r
m
an
ce
.
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h
eir
co
m
p
ar
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v
e
ass
es
s
m
en
t
o
f
f
er
s
m
ea
n
in
g
f
u
l
in
s
ig
h
ts
in
to
id
e
n
tify
in
g
th
e
m
o
s
t
ef
f
ec
tiv
e
tec
h
n
iq
u
e
f
o
r
ac
h
iev
in
g
r
eliab
le
an
d
p
r
ec
is
e
f
o
r
ec
asti
n
g
r
esu
lts
.
2
.
1
.
1
.
Ra
nd
o
m
f
o
re
s
t
(
RF
)
T
h
e
r
an
d
o
m
f
o
r
est
(
R
F)
clas
s
if
ier
en
h
an
ce
s
p
r
ed
ictio
n
ac
c
u
r
ac
y
b
y
co
n
s
tr
u
ctin
g
m
u
ltip
l
e
d
ec
is
io
n
tr
ee
s
o
n
d
if
f
er
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t
s
u
b
s
ets
o
f
th
e
in
p
u
t
d
ata.
I
t
th
en
co
m
b
i
n
es
o
r
av
er
ag
es
th
e
r
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lts
f
r
o
m
th
ese
tr
ee
s
to
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r
o
d
u
ce
a
m
o
r
e
r
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le
an
d
s
tab
le
o
u
tp
u
t.
Fig
u
r
e
4
illu
s
tr
ates
th
e
s
ch
em
atic
d
iag
r
am
o
f
th
e
R
F
alg
o
r
ith
m
u
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ed
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th
is
s
tu
d
y
.
2
.
1
.
2
.
K
-
nea
re
s
t
neig
hb
o
r
(
K
NN)
T
h
e
s
u
p
er
v
is
ed
m
ac
h
in
e
lear
n
in
g
m
eth
o
d
KNN
class
if
ies
n
ew
d
ata
p
o
in
ts
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y
co
m
p
ar
i
n
g
th
em
to
ex
is
tin
g
ca
teg
o
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ies
b
ased
o
n
s
im
ilar
ity
.
T
h
e
p
r
o
ce
s
s
in
v
o
lv
es
s
elec
tin
g
th
e
n
u
m
b
e
r
o
f
n
eig
h
b
o
r
s
(
K)
,
ca
lcu
latin
g
th
e
E
u
clid
ea
n
d
is
tan
ce
to
f
in
d
th
e
n
ea
r
est
n
eig
h
b
o
r
s
,
lis
tin
g
th
ese
n
eig
h
b
o
r
s
,
co
u
n
tin
g
d
ata
p
o
in
ts
in
ea
ch
class
with
in
K,
an
d
ass
ig
n
in
g
th
e
n
ew
p
o
in
t
to
th
e
class
w
ith
th
e
h
ig
h
est
co
u
n
t.
T
h
e
E
u
clid
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n
d
is
tan
ce
f
o
r
m
u
la
is
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ef
in
e
d
as:
E
d
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(
x
2
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x
1
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2
(
y
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y
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.
T
h
is
s
im
p
le
y
et
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f
ec
tiv
e
alg
o
r
ith
m
is
wid
ely
u
s
ed
in
class
if
icatio
n
task
s
.
Fig
u
r
e
5
s
h
o
ws th
e
KNN
alg
o
r
ith
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
6
9
4
I
n
t J Po
w
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lec
&
Dr
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s
t
,
Vo
l.
16
,
No
.
4
,
Dec
em
b
er
20
25
:
2645
-
2
6
5
4
2648
(
a)
(
b
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Fig
u
r
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3
.
So
lar
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o
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g
en
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ati
o
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d
u
r
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g
t
h
e
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tu
d
y
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er
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d
:
(
a)
h
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u
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l
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d
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o
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ly
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n
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n
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al
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o
d
u
ctio
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th
r
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g
h
o
u
t t
h
e
r
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ch
p
e
r
io
d
T
ab
le
1
.
PC
A
r
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lts
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d
s
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ted
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ea
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%
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Fig
u
r
e
4
.
R
an
d
o
m
f
o
r
est alg
o
r
ith
m
Fig
u
r
e
5
.
K
-
n
ea
r
est n
eig
h
b
o
r
(
KNN)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Po
w
E
lec
&
Dr
i Sy
s
t
I
SS
N:
2088
-
8
6
9
4
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n
h
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n
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r
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e
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t g
r
id
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cy
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(
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ya
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h
r
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K
a
th
ir
ve
l
)
2649
2
.
1
.
3
.
Neura
l net
wo
rk
s
(
NNs)
Neu
r
al
n
etwo
r
k
s
(
NNs)
p
r
o
ce
s
s
s
o
lar
ir
r
ad
ian
ce
d
ata
t
h
r
o
u
g
h
lay
er
s
,
ad
ju
s
tin
g
weig
h
ts
an
d
b
iases
to
im
p
r
o
v
e
p
r
ed
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n
ac
cu
r
ac
y
.
I
n
th
is
s
tu
d
y
,
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p
lay
a
k
e
y
r
o
le
in
f
o
r
ec
asti
n
g
s
o
lar
p
h
o
to
v
o
ltaic
(
PV)
p
o
wer
,
aid
in
g
ef
f
icien
t so
lar
en
er
g
y
in
teg
r
atio
n
in
to
s
m
ar
t
g
r
id
s
.
F
ig
u
r
e
6
s
h
o
ws
a
n
eu
r
al
n
etwo
r
k
alg
o
r
ith
m
.
Z
=(
Ʃ
x
k
j
-
1
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k,
i
-
b
k
)
lim
its
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to
N
j
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1
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k
,
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e
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h
t
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ciate
d
with
th
e
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n
n
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f
r
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m
n
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e
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o
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r
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x
k
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e
p
r
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e
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ata
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d
b
y
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e
k
t
h
n
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e
in
t
h
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r
t
h
e
r
m
o
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e,
t
h
e
n
u
m
b
er
o
f
n
o
d
es in
lay
er
j −
1
is
d
en
o
t
ed
b
y
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.
T
h
e
ac
tiv
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n
f
u
n
ctio
n
th
en
r
ec
eiv
es th
e
to
tal.
Fig
u
r
e
6
.
Neu
r
al
n
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r
k
2
.
1
.
4
.
Ada
B
o
o
s
t
T
h
r
o
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g
h
an
o
n
g
o
in
g
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r
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tly
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ize
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in
s
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ce
s
,
b
o
o
s
tin
g
tr
an
s
f
o
r
m
s
wea
k
m
o
d
els
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to
s
tr
o
n
g
er
p
r
e
d
ictio
n
s
.
B
y
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ly
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g
a
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ase
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.
.
.
,
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)
,
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s
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tten
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n
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lt
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h
e
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k
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er
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er
r
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r
r
ates
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o
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s
ed
o
v
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all
ac
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r
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y
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r
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d
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h
t)
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ased
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p
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ate
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Fig
u
r
e
7
s
h
o
ws th
e
A
da
B
o
o
s
t a
lg
o
r
ith
m
.
Fig
u
r
e
7
.
Ad
aBo
o
s
t
2
.
1
.
5
.
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ra
dient
b
o
o
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t
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G
B
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r
ad
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t
b
o
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g
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GB
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en
h
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n
ce
s
p
r
ed
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n
b
y
in
te
g
r
atin
g
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k
m
o
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els
,
u
s
u
ally
d
ec
is
io
n
tr
ee
s
,
iter
ativ
ely
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r
e
d
u
ce
er
r
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r
s
.
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t
o
p
tim
izes
a
lo
s
s
f
u
n
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n
,
r
ef
in
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s
u
s
in
g
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d
o
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ates
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ately
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atter
n
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im
p
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.
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o
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ip
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e.
g
.
,
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d
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)
an
d
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tp
u
t.
Usi
n
g
th
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:
Y
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+Ʃb
1
X1
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.
L
R
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en
tifie
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h
o
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f
ac
to
r
(
(
Xi)
)
im
p
ac
ts
th
e
s
o
lar
en
e
r
g
y
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
6
9
4
I
n
t J Po
w
E
lec
&
Dr
i Sy
s
t
,
Vo
l.
16
,
No
.
4
,
Dec
em
b
er
20
25
:
2645
-
2
6
5
4
2650
g
en
er
ated
(
(
Y)
)
,
m
ak
in
g
it
a
k
ey
to
o
l
f
o
r
p
r
ed
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g
p
h
o
to
v
o
ltaic
(
PV)
p
o
wer
.
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h
is
h
el
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s
o
p
tim
ize
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y
m
an
ag
em
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t
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s
m
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ased
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e
n
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n
m
en
tal
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o
n
d
itio
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s
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en
h
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n
cin
g
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f
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,
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n
d
in
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o
r
p
o
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ati
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g
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o
lar
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er
g
y
in
to
t
h
e
elec
tr
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g
r
id
.
Fig
u
r
e
8
s
h
o
ws
g
r
ad
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t
b
o
o
s
t
alg
o
r
ith
m
.
T
h
ese
m
o
d
els
wer
e
s
elec
ted
b
ec
au
s
e
th
ey
h
av
e
d
em
o
n
s
tr
ated
s
u
cc
ess
in
tim
e
-
s
e
r
ies
an
d
r
eg
r
ess
io
n
task
s
,
as we
ll a
s
th
eir
ca
p
ac
ity
to
id
en
tify
n
o
n
lin
ea
r
co
r
r
elatio
n
s
in
d
ata
o
n
s
o
lar
ir
r
a
d
ian
ce
.
Fig
u
r
e
8
.
Gr
a
d
ien
t
b
o
o
s
t
2
.
1
.
6
.
I
m
pro
v
ed
d
is
cus
s
io
n o
n P
CA
r
et
ent
io
n a
nd
im
pa
ct
o
n m
o
del
perf
o
r
m
a
nce
T
o
s
tr
en
g
th
e
n
th
e
m
eth
o
d
o
l
o
g
ical
clar
ity
,
esp
ec
ially
c
o
n
ce
r
n
in
g
PC
A,
we
in
clu
d
ed
a
n
a
n
al
y
s
is
o
f
th
e
ex
p
lain
ed
v
a
r
ian
ce
b
y
p
r
in
ci
p
al
co
m
p
o
n
en
ts
.
Fig
u
r
e
9
ill
u
s
tr
ates
th
e
v
ar
iatio
n
th
at
ea
ch
m
ajo
r
co
m
p
o
n
e
n
t
ex
p
lain
s
,
co
n
f
ir
m
in
g
th
at
th
e
f
ir
s
t
f
o
u
r
co
m
p
o
n
e
n
ts
ca
p
tu
r
e
9
9
.
3
%
o
f
th
e
to
tal
v
ar
ian
ce
.
W
e
r
etain
ed
f
o
u
r
p
r
in
cip
al
co
m
p
o
n
e
n
ts
,
as
th
ey
p
r
eser
v
e
9
9
.
3
%
o
f
th
e
o
r
i
g
in
al
d
ata
v
ar
ian
ce
,
g
r
ea
tly
lo
we
r
in
g
d
im
e
n
s
io
n
ality
wh
ile
g
u
ar
an
teei
n
g
litt
le
in
f
o
r
m
atio
n
l
o
s
s
.
T
h
is
r
ed
u
cti
o
n
en
h
an
ce
d
m
o
d
el
ef
f
icien
cy
b
y
:
d
ec
r
ea
s
in
g
co
m
p
u
tatio
n
al
co
m
p
le
x
ity
,
im
p
r
o
v
in
g
tr
ain
i
n
g
tim
e,
r
ed
u
ci
n
g
o
v
e
r
f
itti
n
g
r
is
k
,
an
d
en
h
a
n
c
in
g
in
ter
p
r
etab
ilit
y
f
o
r
m
o
d
els
s
en
s
itiv
e
to
m
u
ltic
o
llin
ea
r
ity
.
T
h
is
PC
A
tr
an
s
f
o
r
m
atio
n
p
o
s
itiv
ely
im
p
ac
ted
al
l
m
ac
h
in
e
lear
n
in
g
m
o
d
els,
p
a
r
ticu
lar
ly
lin
ea
r
r
eg
r
ess
io
n
an
d
Ad
aBo
o
s
t,
w
h
ich
b
e
n
ef
ited
f
r
o
m
th
e
cle
ar
er
s
ep
ar
atio
n
o
f
in
f
lu
en
tial f
ea
tu
r
es a
n
d
r
e
d
u
ce
d
n
o
is
e
in
th
e
d
ataset.
Fig
u
r
e
9
.
E
x
p
lain
ed
v
ar
ian
ce
r
atio
b
y
p
r
in
cip
al
co
m
p
o
n
e
n
ts
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
s
tu
d
y
ev
al
u
ated
m
u
ltip
le
m
o
d
els
f
o
r
s
o
lar
p
o
wer
p
r
ed
ictio
n
u
s
in
g
PC
A
-
tr
an
s
f
o
r
m
e
d
f
ea
tu
r
es.
Key
f
in
d
in
g
s
in
clu
d
e:
-
GB
m
o
d
el:
Hig
h
ac
cu
r
ac
y
with
ad
j
-
R
²
=
0
.
9
5
6
1
9
an
d
R
²
=
0
.
9
5
7
0
5
.
-
KNN
m
o
d
el:
Mo
d
er
ate
p
e
r
f
o
r
m
an
ce
with
ad
j
-
R
²
=
0
.
5
4
1
7
a
n
d
R
²
=
0
.
8
4
4
8
.
-
NN
m
o
d
el:
Mo
d
est p
er
f
o
r
m
a
n
ce
with
ad
j
-
R
²
=
0
.
5
8
2
8
a
n
d
R
²
=
0
.
5
9
1
0
.
-
R
F
m
o
d
el:
Stro
n
g
p
er
f
o
r
m
a
n
c
e
with
ad
j
-
R
²
=
0
.
9
5
1
4
a
n
d
R
²
=
0
.
9
5
2
4
.
-
Ad
aBo
o
s
t
m
o
d
el:
B
est p
er
f
o
r
m
an
ce
with
ad
j
-
R
²
=
0
.
9
9
6
2
0
an
d
R
²
=
0
.
9
9
6
2
8
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Po
w
E
lec
&
Dr
i Sy
s
t
I
SS
N:
2088
-
8
6
9
4
E
n
h
a
n
ce
d
in
teg
r
a
tio
n
o
f ren
e
w
a
b
le
en
erg
y
a
n
d
s
ma
r
t g
r
id
efficien
cy
w
ith
…
(
Ja
ya
s
h
r
ee
K
a
th
ir
ve
l
)
2651
-
L
R
m
o
d
el:
E
x
ce
llen
t
p
er
f
o
r
m
a
n
ce
with
ad
j
-
R
²
=
0
.
9
9
2
3
9
an
d
R
²
=
0
.
9
9
2
3
2
.
Ad
aBo
o
s
t
an
d
r
id
g
e
r
eg
r
ess
io
n
s
h
o
wed
th
e
h
ig
h
est
ac
cu
r
ac
y
,
m
ak
in
g
th
e
m
th
e
m
o
s
t
r
eliab
le
m
o
d
els
f
o
r
s
o
lar
p
o
wer
f
o
r
ec
asti
n
g
.
Usi
n
g
th
e
m
o
s
t
r
ec
en
t
s
o
lar
g
r
id
f
o
r
ec
asti
n
g
m
o
d
el
m
etr
ics:
W
ith
an
R
MSE
o
f
0
.
5
5
7
k
W
/m
²
an
d
a
n
R
2
o
f
0
.
9
9
6
,
A
d
aBo
o
s
t
with
PC
A
co
n
tin
u
es
to
b
e
a
t
o
p
p
e
r
f
o
r
m
er
,
d
em
o
n
s
tr
atin
g
its
ef
f
ec
tiv
en
ess
in
r
eg
r
ess
io
n
task
s
b
y
s
u
cc
ess
f
u
lly
r
e
d
u
cin
g
m
is
tak
es.
W
ith
th
e
lo
west
R
M
S
E
o
f
0
.
5
1
0
k
W
/m
2
,
L
R
with
PC
A
m
ain
tain
s
its
ex
ce
p
tio
n
al
p
r
ec
is
io
n
a
n
d
is
h
en
ce
v
er
y
d
e
p
en
d
a
b
le
f
o
r
lin
ea
r
co
r
r
elatio
n
s
.
T
ab
le
2
s
h
o
ws th
e
Per
f
o
r
m
an
c
e
in
d
icato
r
s
d
u
r
in
g
tr
ai
n
in
g
f
o
r
d
if
f
er
en
t
m
o
d
els.
T
ab
le
2
.
Per
f
o
r
m
an
ce
m
etr
ics th
r
o
u
g
h
o
u
t
d
if
f
er
e
n
t m
o
d
els wh
en
tr
ain
in
g
M
o
d
e
l
Th
i
s
r
e
se
a
r
c
h
O
t
h
e
r
st
u
d
i
e
s
ADJ
-
R
2
R
M
S
E
M
A
E
R
2
R
2
K
N
N
.
8
4
2
6
2
9
.
4
8
4
5
9
.
8
.
8
4
.
8
7
7
A
d
a
B
o
o
st
.
9
9
5
5
5
7
.
9
5
3
7
3
.
9
.
9
9
7
.
9
0
2
G
r
a
d
i
e
n
t
b
o
o
s
t
.
9
5
5
6
2
2
.
6
7
4
9
8
.
6
1
.
9
5
8
.
9
2
4
R
a
n
d
o
m f
o
r
e
s
t
.
9
5
2
5
4
8
.
9
3
6
6
.
1
7
.
9
5
1
.
9
4
N
e
u
r
a
l
n
e
t
w
o
r
k
.
5
8
3
1
7
8
1
.
2
8
5
6
.
4
9
.
5
9
2
.
5
9
6
Li
n
e
a
r
r
e
g
r
e
s
si
o
n
.
9
9
1
5
1
0
.
1
3
5
7
.
4
7
1
.
9
9
1
0
3
1
W
ith
th
e
lo
west
R
2
o
f
0
.
5
8
2
an
d
th
e
g
r
ea
test
R
MSE
o
f
1
.
7
8
1
k
W
/m
2
,
NN
co
n
tin
u
e
to
lag
b
eh
in
d
,
s
u
g
g
esti
n
g
th
at
th
eir
ac
cu
r
ac
y
f
o
r
s
o
lar
f
o
r
ec
asti
n
g
is
r
estricte
d
.
Alth
o
u
g
h
th
e
y
wo
r
k
well,
o
th
er
m
o
d
els
lik
e
R
F
an
d
Gr
ad
ie
n
t
b
o
o
s
t
h
a
v
e
d
r
awb
ac
k
s
s
u
ch
o
v
e
r
f
itti
n
g
an
d
h
ig
h
p
r
o
ce
s
s
in
g
r
eq
u
ir
em
e
n
ts
.
T
h
e
ef
f
ec
tiv
e
n
ess
o
f
PV
s
y
s
tem
s
i
s
al
s
o
g
r
ea
tly
im
p
ac
ted
b
y
v
ar
ia
b
les
in
clu
d
in
g
d
u
s
t
d
ep
o
s
itio
n
,
tem
p
er
at
u
r
e
v
ar
iatio
n
s
,
an
d
clea
n
in
g
p
r
ac
tices.
Fo
r
d
y
n
a
m
ic
s
o
lar
g
r
id
s
y
s
tem
s
to
r
e
tain
p
r
ed
ictio
n
ac
cu
r
ac
y
an
d
ad
ju
s
t
to
ch
an
g
in
g
clim
atic
co
n
d
itio
n
s
,
r
eg
u
lar
m
o
d
el
r
etr
ain
in
g
with
u
p
d
ated
d
atasets
i
s
ess
en
tial.
Mo
d
el
p
er
f
o
r
m
an
ce
in
ter
p
r
e
tatio
n
:
Ad
aBo
o
s
t
p
er
f
o
r
m
ed
b
etter
b
ec
au
s
e
it
co
u
l
d
a
d
ap
tiv
ely
s
tr
en
g
th
en
wea
k
lear
n
er
s
,
wh
ich
m
ad
e
it
r
esis
tan
t
to
n
o
is
e
an
d
an
o
m
alies
in
d
ata
o
n
s
o
lar
ir
r
ad
iatio
n
.
T
h
e
r
o
b
u
s
t
lin
ea
r
co
r
r
elatio
n
s
th
at
PC
A
p
r
eser
v
ed
,
h
o
wev
er
,
wer
e
ad
v
an
tag
eo
u
s
to
L
R
,
wh
ich
m
ad
e
it
p
er
f
ec
t
f
o
r
clea
n
,
lo
w
-
d
im
e
n
s
io
n
al
d
atasets
.
C
o
n
n
ec
tio
n
to
th
e
d
ata
f
ea
tu
r
es
:
T
h
e
tem
p
er
at
u
r
e
an
d
win
d
s
p
ee
d
in
th
e
d
ataset
v
ar
y
g
r
ea
tly
,
wh
e
r
ea
s
th
e
h
u
m
id
ity
an
d
ir
r
ad
ian
ce
v
ar
y
m
o
d
er
ately
.
Ad
aBo
o
s
t's
r
e
-
weig
h
tin
g
tech
n
iq
u
e
allo
wed
it
to
h
an
d
le
o
u
tlier
s
ef
f
icien
tly
,
wh
ile
PC
A
m
ain
tain
ed
lin
ea
r
s
tr
u
ctu
r
e,
wh
ich
m
ad
e
it
ap
p
r
o
p
r
iate
f
o
r
L
R
.
T
ab
le
3
tr
ad
e
-
o
f
f
s
s
u
m
m
ar
y
o
u
tlin
es
th
e
co
m
p
ar
ativ
e
s
tr
en
g
th
s
an
d
co
n
s
tr
ain
ts
o
f
s
ev
er
a
l
m
ac
h
in
e
lear
n
in
g
m
o
d
els
f
o
r
s
o
lar
p
r
e
d
ictio
n
.
I
t
h
ig
h
lig
h
ts
Ad
aBo
o
s
t
'
s
ac
cu
r
ac
y
,
L
R
’
s
s
im
p
licity
,
an
d
th
e
b
alan
ce
b
etw
ee
n
p
er
f
o
r
m
an
ce
an
d
co
m
p
lex
ity
ac
r
o
s
s
m
o
d
els.
T
ab
le
4
B
en
ch
m
ar
k
in
g
t
ab
le
co
m
p
ar
es
th
e
p
r
o
p
o
s
e
d
m
o
d
el’
s
f
o
r
ec
asti
n
g
p
er
f
o
r
m
a
n
ce
with
r
ec
en
t
s
tu
d
ies
u
s
in
g
d
if
f
er
e
n
t
d
atasets
an
d
tech
n
iq
u
es.
T
h
e
r
esu
lts
d
em
o
n
s
tr
ate
th
at
Ad
aBo
o
s
t o
u
tp
er
f
o
r
m
e
d
o
th
e
r
s
with
th
e
l
o
west R
M
SE
an
d
h
ig
h
est R
².
T
ab
le
3
.
T
r
a
d
e
-
o
f
f
s
s
u
m
m
ar
y
S
l
.
n
o
M
o
d
e
l
S
t
r
e
n
g
t
h
s
Li
mi
t
a
t
i
o
n
s
1
A
d
a
B
o
o
st
H
i
g
h
a
c
c
u
r
a
c
y
,
r
o
b
u
st
t
o
n
o
i
se
C
o
m
p
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1
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c
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tere
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p
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sy
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m
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sm
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p
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rg
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tab
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sin
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d
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s
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c
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tac
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y
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field
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Re
p
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ro
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u
,
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d
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sin
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r
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f
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n
tl
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rk
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ss
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rtme
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tri
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tro
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g
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g
e
(Au
to
n
o
m
o
u
s),
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h
e
n
n
a
i
.
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is
a
l
ife
m
e
m
b
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Tec
h
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Ed
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c
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ti
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n
(IS
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)
.
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sp
e
c
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ti
o
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s
in
c
lu
d
e
p
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we
r
sy
ste
m
s,
sm
a
rt
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ri
d
,
v
o
lt
a
g
e
sta
b
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ty
,
a
n
d
re
n
e
wa
b
l
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n
e
rg
y
s
y
ste
m
s
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
iad
a
ick
a
lam
@g
m
a
il
.
c
o
m
.
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