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1
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Var
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g
e
n
er
atio
n
,
ac
h
iev
i
n
g
a
p
er
f
o
r
m
an
ce
r
atio
o
f
9
7
.
2
%.
Similar
ly
,
en
s
em
b
le
lear
n
in
g
tech
n
iq
u
es
h
av
e
b
ee
n
s
h
o
wn
to
f
u
r
th
e
r
en
h
a
n
ce
f
o
r
ec
asti
n
g
ac
cu
r
ac
y
.
C
h
ak
r
ab
o
r
ty
et
a
l.
[
5
]
r
e
p
o
r
te
d
th
at
en
s
em
b
le
m
o
d
els
s
u
ch
as
s
t
ac
k
in
g
an
d
v
o
tin
g
ac
h
iev
ed
p
r
ed
ictio
n
ac
cu
r
ac
ies
o
f
ap
p
r
o
x
im
ately
9
6
%,
h
ig
h
lig
h
tin
g
th
eir
ab
ilit
y
t
o
m
o
d
el
co
m
p
lex
s
o
lar
p
o
wer
d
y
n
am
ics.
Dee
p
lear
n
in
g
(
DL
)
m
eth
o
d
s
h
av
e
also
c
o
n
tr
ib
u
ted
t
o
im
p
r
o
v
ed
f
o
r
ec
asti
n
g
p
er
f
o
r
m
an
ce
.
Sh
ah
et
a
l.
[
6
]
d
em
o
n
s
tr
ated
t
h
at
in
co
r
p
o
r
atin
g
air
q
u
ality
in
d
ex
(
AQI
)
with
m
eteo
r
o
l
o
g
ica
l
f
ea
tu
r
es
y
ield
s
an
R
²
s
co
r
e
o
f
0
.
9
6
9
1
,
em
p
h
asizin
g
th
e
im
p
o
r
tan
ce
o
f
en
v
ir
o
n
m
en
tal
f
ac
to
r
s
b
e
y
o
n
d
t
r
ad
itio
n
al
wea
th
er
v
ar
iab
les.
Desp
ite
th
ese
ad
v
an
ce
m
en
ts
,
ch
allen
g
es
r
em
ai
n
d
u
e
to
d
ata
q
u
ality
is
s
u
es
,
r
eg
io
n
al
clim
atic
v
ar
iab
ilit
y
,
an
d
th
e
in
h
er
en
t in
ter
m
itten
cy
o
f
s
o
lar
ir
r
ad
ian
ce
[
7
]
.
Fu
tu
r
e
r
esear
ch
s
h
o
u
ld
f
o
cu
s
o
n
h
y
b
r
i
d
m
o
d
els
th
at
in
te
g
r
a
te
ML
with
p
h
y
s
ical
p
r
in
cip
l
es,
u
tili
ze
r
ea
l
-
tim
e
d
ata
s
tr
ea
m
s
,
an
d
co
n
s
id
er
ad
d
itio
n
al
e
n
v
ir
o
n
m
en
t
al
in
f
lu
en
ce
s
s
u
ch
as
air
p
o
llu
t
io
n
[
8
]
.
Ad
d
r
ess
in
g
s
ca
lab
ilit
y
an
d
co
m
p
u
tatio
n
al
ef
f
icien
cy
is
also
cr
itica
l
f
o
r
r
ea
l
-
wo
r
ld
d
ep
lo
y
m
en
t
[
9
]
.
Ov
er
all,
ML
-
d
r
iv
en
s
o
lar
f
o
r
ec
asti
n
g
p
lay
s
a
v
ital
r
o
le
in
s
u
p
p
o
r
tin
g
I
n
d
ia’
s
r
e
n
ewa
b
le
en
er
g
y
g
o
als
an
d
ad
v
an
cin
g
th
e
g
lo
b
al
s
h
if
t to
war
d
s
u
s
tain
ab
le
en
er
g
y
s
y
s
tem
s
[
1
0
]
.
2.
RE
L
AT
E
D
WO
RK
S
T
h
e
ap
p
licatio
n
o
f
ML
an
d
DL
tech
n
iq
u
es
h
as
s
u
b
s
tan
tially
im
p
r
o
v
ed
th
e
ac
c
u
r
ac
y
o
f
s
o
lar
p
o
wer
f
o
r
ec
asti
n
g
,
th
er
eb
y
s
u
p
p
o
r
tin
g
e
f
f
icien
t
en
er
g
y
m
an
ag
em
en
t
an
d
g
r
id
s
ta
b
ilit
y
.
Pro
b
ab
ilis
tic
f
o
r
ec
asti
n
g
m
eth
o
d
s
,
s
u
ch
as
q
u
an
tile
r
eg
r
ess
io
n
n
eu
r
al
n
etwo
r
k
s
,
h
av
e
b
ee
n
i
n
tr
o
d
u
ce
d
to
m
o
d
el
u
n
ce
r
tain
ties
ass
o
ciate
d
with
s
o
lar
p
o
wer
g
en
er
atio
n
[
1
1
]
.
E
n
s
em
b
le
lear
n
in
g
ap
p
r
o
a
ch
es
th
at
co
m
b
in
e
m
u
ltip
le
m
o
d
els
h
av
e
d
em
o
n
s
tr
ated
s
u
p
er
io
r
p
e
r
f
o
r
m
an
c
e
in
d
a
y
-
ah
ea
d
f
o
r
ec
asti
n
g
t
ask
s
b
y
ca
p
t
u
r
in
g
co
m
p
lex
n
o
n
-
lin
ea
r
r
elatio
n
s
h
i
p
s
[
1
2
]
.
Hy
b
r
id
DL
a
r
ch
itectu
r
es
in
t
eg
r
atin
g
c
o
n
v
o
lu
tio
n
n
eu
r
al
n
etwo
r
k
s
(
C
NNs)
an
d
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
L
STM
)
n
etwo
r
k
s
h
av
e
f
u
r
th
er
en
h
a
n
ce
d
p
r
e
d
ictio
n
ac
c
u
r
ac
y
b
y
ef
f
ec
tiv
ely
m
o
d
ellin
g
s
p
atial
an
d
tem
p
o
r
al
d
e
p
en
d
e
n
cies
in
s
o
lar
ir
r
ad
ian
ce
d
ata
[
1
3
]
.
T
r
a
n
s
f
er
lear
n
in
g
s
tr
ateg
ies
h
a
v
e
b
ee
n
ad
o
p
ted
to
im
p
r
o
v
e
f
o
r
ec
asti
n
g
ac
r
o
s
s
d
iv
er
s
e
clim
atic
r
eg
io
n
s
,
r
ed
u
cin
g
co
m
p
u
tatio
n
al
co
s
t
s
wh
ile
in
cr
ea
s
in
g
ad
ap
tab
ilit
y
[
1
4
]
.
B
a
y
e
s
i
a
n
n
eu
r
a
l
n
e
t
w
o
r
k
s
h
a
v
e
a
ls
o
b
e
e
n
em
p
l
o
y
e
d
f
o
r
u
n
c
e
r
t
a
i
n
t
y
q
u
a
n
t
if
i
c
a
t
i
o
n
,
s
u
p
p
o
r
t
i
n
g
m
o
r
e
i
n
f
o
r
m
e
d
d
e
c
i
s
i
o
n
-
m
a
k
in
g
i
n
p
o
w
e
r
g
r
i
d
o
p
e
r
a
t
i
o
n
s
[
1
5
]
.
A
d
d
i
t
i
o
n
a
l
l
y
,
w
e
a
t
h
e
r
p
a
t
t
e
r
n
r
e
c
o
g
n
i
t
i
o
n
c
o
m
b
i
n
e
d
w
i
t
h
e
n
s
e
m
b
l
e
l
e
a
r
n
in
g
h
a
s
c
o
n
t
r
i
b
u
t
e
d
t
o
m
o
r
e
r
o
b
u
s
t
a
n
d
r
e
l
i
a
b
l
e
f
o
r
e
c
as
t
i
n
g
m
o
d
e
l
s
[
1
6
]
.
R
ec
en
t
s
tu
d
ies
h
av
e
ex
p
l
o
r
ed
atten
tio
n
-
b
ased
L
STM
m
o
d
el
s
f
o
r
s
h
o
r
t
-
ter
m
s
o
lar
p
o
wer
p
r
ed
ictio
n
,
en
ab
lin
g
th
e
m
o
d
el
to
f
o
cu
s
o
n
th
e
m
o
s
t
r
elev
an
t
in
p
u
t
f
e
atu
r
es
[
1
7
]
.
T
h
e
in
teg
r
atio
n
o
f
r
ea
l
-
tim
e
wea
th
er
f
o
r
ec
asts
with
ML
m
o
d
els
h
as
f
u
r
th
er
im
p
r
o
v
e
d
p
r
e
d
ictio
n
p
er
f
o
r
m
a
n
ce
[
1
8
]
,
w
h
ile
d
ee
p
r
ein
f
o
r
ce
m
e
n
t
lear
n
in
g
a
p
p
r
o
ac
h
es
h
av
e
s
h
o
wn
ad
a
p
tab
ilit
y
b
y
d
y
n
am
ic
ally
ad
ju
s
tin
g
m
o
d
el
p
ar
am
eter
s
in
r
esp
o
n
s
e
t
o
en
v
ir
o
n
m
en
tal
ch
a
n
g
es
[
1
9
]
.
E
x
p
lain
ab
le
AI
tech
n
iq
u
es
h
a
v
e
g
ain
e
d
atten
tio
n
f
o
r
en
h
a
n
c
in
g
th
e
tr
a
n
s
p
ar
en
c
y
an
d
in
ter
p
r
etab
ilit
y
o
f
f
o
r
ec
a
s
tin
g
m
o
d
els,
wh
ic
h
is
cr
itic
al
f
o
r
b
u
ild
in
g
tr
u
s
t
am
o
n
g
g
r
id
o
p
er
ato
r
s
an
d
p
o
licy
m
ak
er
s
[
2
0
]
.
Hy
b
r
id
m
o
d
els
co
m
b
in
in
g
au
to
r
eg
r
e
s
s
iv
e
in
teg
r
ated
m
o
v
in
g
a
v
er
ag
e
(
AR
I
MA
)
with
L
STM
n
etwo
r
k
s
h
av
e
ef
f
ec
tiv
ely
ca
p
tu
r
ed
b
o
th
lin
ea
r
an
d
n
o
n
-
lin
ea
r
p
atter
n
s
in
s
o
lar
p
o
wer
d
ata
[
2
1
]
.
Ad
ap
tiv
e
n
eu
r
o
-
f
u
zz
y
in
f
er
en
ce
s
y
s
tem
s
(
AN
FIS)
h
av
e
also
b
ee
n
in
v
esti
g
ated
to
ad
d
r
ess
u
n
ce
r
tain
ties
ar
is
in
g
f
r
o
m
f
lu
ctu
atin
g
m
eteo
r
o
lo
g
ic
al
co
n
d
itio
n
s
[
2
2
]
.
Mo
r
eo
v
er
,
th
e
in
teg
r
atio
n
o
f
b
i
g
d
ata
an
a
ly
tics
with
ML
h
a
s
im
p
r
o
v
e
d
th
e
r
o
b
u
s
tn
ess
o
f
s
o
lar
p
o
wer
p
r
ed
ictio
n
s
[
2
3
]
,
wh
ile
ed
g
e
co
m
p
u
tin
g
-
b
ased
f
o
r
ec
asti
n
g
f
r
am
ewo
r
k
s
h
av
e
r
ed
u
ce
d
laten
cy
in
r
ea
l
-
tim
e
ap
p
licatio
n
s
[
2
4
]
.
Sp
atio
-
tem
p
o
r
al
g
r
a
p
h
co
n
v
o
lu
tio
n
n
etwo
r
k
s
h
av
e
b
ee
n
em
p
lo
y
ed
to
m
o
d
el
s
p
atial
d
ep
e
n
d
en
cies;
p
ar
t
icu
lar
ly
in
r
e
g
io
n
s
with
co
m
p
lex
g
eo
g
r
a
p
h
ical
ch
ar
ac
ter
is
tics
[
2
5
]
.
T
r
an
s
f
er
lear
n
in
g
with
f
o
u
n
d
atio
n
m
o
d
els
h
as
im
p
r
o
v
ed
s
h
o
r
t
-
ter
m
s
o
lar
ir
r
ad
ia
n
ce
f
o
r
ec
asti
n
g
w
h
ile
m
in
im
izin
g
r
etr
ain
in
g
r
eq
u
ir
em
en
ts
[
2
6
]
.
Mu
lti
-
lo
ca
tio
n
DL
m
o
d
els
h
a
v
e
en
h
an
ce
d
g
lo
b
al
p
r
ed
ictio
n
ca
p
ab
ilit
y
a
n
d
a
d
ap
tab
ilit
y
[
2
7
]
.
R
ec
en
t
AI
-
d
r
iv
en
wea
th
er
f
o
r
ec
asti
n
g
s
y
s
tem
s
h
av
e
f
u
r
th
er
im
p
r
o
v
e
d
r
en
ewa
b
le
en
er
g
y
p
r
ed
ictio
n
ac
cu
r
ac
y
[
2
8
]
.
I
n
ad
d
itio
n
,
s
atellite
an
d
s
k
y
-
im
ag
e
-
b
ased
DL
ap
p
r
o
ac
h
es
h
av
e
en
ab
led
ef
f
ec
tiv
e
in
tr
a
-
h
o
u
r
s
o
lar
f
o
r
ec
asti
n
g
b
y
ca
p
tu
r
in
g
clo
u
d
d
y
n
am
ics
an
d
r
ap
id
ir
r
ad
ia
n
ce
v
ar
iatio
n
s
[
2
9
]
,
[
3
0
]
.
Des
p
ite
th
ese
ad
v
an
ce
s
,
ch
allen
g
es
r
elate
d
to
d
ata
q
u
ality
,
clim
atic
v
ar
iab
ilit
y
,
s
ca
lab
ilit
y
,
an
d
co
m
p
u
tatio
n
al
ef
f
icien
cy
p
er
s
is
t
[
3
1
]
,
[
3
2
]
.
Fu
tu
r
e
r
esear
ch
s
h
o
u
ld
f
o
cu
s
o
n
h
y
b
r
id
p
h
y
s
ical
–
ML
m
o
d
els,
e
n
v
ir
o
n
m
en
tal
f
ac
to
r
i
n
teg
r
atio
n
,
an
d
ex
p
lain
ab
le
AI
to
f
u
r
th
e
r
en
h
an
ce
r
eliab
ilit
y
a
n
d
a
p
p
lica
b
ilit
y
[
3
3
]
–
[
3
5
]
.
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,
Vo
l.
1
5
,
No
.
2
,
Ap
r
il 2
0
2
6
:
1
3
6
2
-
1
3
7
0
1364
3.
M
E
T
H
O
D
A
s
tr
u
ctu
r
ed
m
eth
o
d
o
lo
g
y
h
a
s
b
ee
n
im
p
lem
en
ted
to
an
aly
ze
s
o
lar
p
o
wer
d
ata
an
d
p
r
e
d
ict
s
o
lar
p
o
wer
o
u
tp
u
t
u
s
in
g
th
e
r
a
n
d
o
m
f
o
r
est
r
eg
r
ess
o
r
.
T
h
e
p
r
o
ce
s
s
co
n
s
is
t
s
o
f
s
ev
er
al
k
ey
s
te
p
s
,
in
clu
d
in
g
d
ata
p
r
ep
r
o
ce
s
s
in
g
,
ex
p
lo
r
at
o
r
y
d
ata
an
aly
s
is
(
E
DA)
,
co
r
r
elatio
n
an
aly
s
is
,
tr
ain
-
test
s
p
litt
i
n
g
,
m
o
d
el
tr
ain
in
g
,
p
r
ed
ictio
n
,
an
d
ev
al
u
atio
n
.
E
a
ch
s
tag
e
h
as
b
ee
n
d
esig
n
e
d
to
en
s
u
r
e
ac
cu
r
ac
y
,
e
f
f
icien
cy
,
an
d
in
te
r
p
r
etab
ilit
y
in
s
o
lar
p
o
wer
f
o
r
ec
asti
n
g
.
Fig
u
r
e
1
r
ep
r
esen
ts
a
ML
wo
r
k
f
lo
w
f
o
r
p
r
e
d
ictin
g
s
o
lar
p
o
wer
o
u
tp
u
t
u
s
in
g
a
r
an
d
o
m
f
o
r
est
r
eg
r
ess
o
r
,
illu
s
tr
atin
g
th
e
k
ey
s
tep
s
,
in
clu
d
i
n
g
d
ata
ex
p
lo
r
ati
o
n
,
f
ea
tu
r
e
en
g
in
ee
r
in
g
,
tr
ai
n
-
test
s
p
litt
in
g
,
m
o
d
el
tr
ain
in
g
,
p
r
ed
i
ctio
n
,
ev
alu
atio
n
,
an
d
r
esu
lt v
i
s
u
aliza
tio
n
.
Fig
u
r
e
1
.
Me
th
o
d
o
lo
g
y
3
.
1
.
Da
t
a
c
o
llect
io
n a
nd
prepro
ce
s
s
ing
T
h
e
d
ataset
co
m
p
r
is
es
6
1
,
3
2
0
o
b
s
er
v
atio
n
s
co
llected
f
r
o
m
s
ev
en
m
ajo
r
I
n
d
ian
cities:
B
en
g
alu
r
u
,
C
h
en
n
ai,
Delh
i,
Hy
d
er
a
b
ad
,
Ko
lk
ata,
Mu
m
b
ai,
an
d
Pu
n
e.
I
t
in
clu
d
es
k
e
y
m
eteo
r
o
lo
g
ical
p
ar
am
eter
s
in
f
lu
en
cin
g
s
o
lar
p
o
wer
g
e
n
er
atio
n
,
s
u
ch
as
s
o
lar
r
a
d
iatio
n
,
tem
p
er
atu
r
e,
h
u
m
id
ity
,
win
d
s
p
ee
d
,
clo
u
d
co
v
er
,
an
d
a
ca
teg
o
r
ical
city
f
ea
tu
r
e
.
Data
p
r
ep
r
o
ce
s
s
in
g
was
p
er
f
o
r
m
ed
to
en
s
u
r
e
q
u
ality
b
y
ad
d
r
ess
in
g
m
is
s
in
g
v
alu
es
th
r
o
u
g
h
m
ea
n
an
d
m
e
d
ian
im
p
u
tatio
n
a
n
d
d
etec
tin
g
o
u
tlier
s
u
s
in
g
th
e
in
ter
q
u
ar
tile
r
an
g
e
m
eth
o
d
.
C
ateg
o
r
ical
v
ar
iab
les we
r
e
o
n
e
-
h
o
t e
n
c
o
d
ed
,
an
d
f
ea
tu
r
e
s
ca
lin
g
was a
p
p
lied
wh
er
e
n
ec
ess
ar
y
.
3
.
2
.
E
x
plo
ra
t
o
ry
da
t
a
a
na
l
y
s
is
a
nd
co
rr
ela
t
io
n a
na
ly
s
is
E
DA
was
p
er
f
o
r
m
ed
to
g
ain
i
n
s
ig
h
ts
in
to
f
ea
tu
r
e
d
is
tr
ib
u
tio
n
s
an
d
r
elatio
n
s
h
ip
s
with
in
t
h
e
d
ataset.
Vis
u
aliza
tio
n
s
s
u
ch
as
h
is
to
g
r
am
s
an
d
p
air
p
l
o
ts
wer
e
u
s
ed
to
ex
am
in
e
d
ata
s
p
r
ea
d
an
d
p
atter
n
s
,
wh
ile
a
co
r
r
elatio
n
h
ea
tm
ap
id
e
n
tifie
d
d
ep
e
n
d
en
cies
b
etwe
en
v
ar
iab
les.
Pear
s
o
n
co
r
r
elatio
n
an
aly
s
is
ass
es
s
ed
r
elatio
n
s
h
ip
s
b
etwe
en
m
ete
o
r
o
lo
g
ical
f
ea
tu
r
es
an
d
s
o
lar
p
o
wer
o
u
tp
u
t.
Featu
r
es
with
h
ig
h
m
u
ltico
llin
ea
r
ity
wer
e
r
em
o
v
e
d
to
p
r
ev
en
t
o
v
er
f
itti
n
g
.
Fig
u
r
e
2
illu
s
tr
ates th
e
d
is
tr
ib
u
tio
n
o
f
k
ey
v
ar
iab
les,
s
u
p
p
o
r
tin
g
ef
f
ec
tiv
e
d
ata
u
n
d
e
r
s
tan
d
in
g
a
n
d
m
o
d
el
tr
ain
in
g
.
3
.
3
.
T
ra
in
-
t
est
s
pli
t
T
o
en
s
u
r
e
u
n
b
iased
ev
alu
atio
n
o
f
th
e
m
o
d
el,
t
h
e
d
ataset
h
as
b
ee
n
s
p
lit
in
to
tr
ain
in
g
(
8
0
%)
an
d
test
in
g
(
2
0
%)
s
u
b
s
ets.
T
h
is
s
tep
allo
ws
th
e
m
o
d
el
to
lear
n
f
r
o
m
h
is
to
r
ical
d
ata
wh
ile
b
ein
g
v
alid
ate
d
o
n
u
n
s
ee
n
d
ata.
T
h
e
s
p
lit
h
as
b
ee
n
p
er
f
o
r
m
ed
u
s
in
g
s
tr
atif
ie
d
s
am
p
lin
g
to
e
n
s
u
r
e
r
e
p
r
ese
n
tativ
e
d
is
tr
ib
u
tio
n
ac
r
o
s
s
d
if
f
er
en
t c
ities
an
d
wea
th
er
co
n
d
itio
n
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
Ma
ch
in
e
lea
r
n
in
g
-
b
a
s
ed
s
o
la
r
p
o
w
er p
r
ed
ictio
n
fo
r
ma
jo
r
I
n
d
ia
n
metro
cities
(
K
o
ma
l Ku
ma
r
N
a
p
a
)
1365
Fig
u
r
e
2
.
I
ll
u
s
tr
atio
n
o
f
f
ea
tu
r
e
d
is
tr
ib
u
tio
n
s
3
.
4
.
M
o
del
s
elec
t
io
n a
nd
t
ra
ini
ng
A
r
an
d
o
m
f
o
r
est
r
eg
r
ess
o
r
h
as
b
ee
n
em
p
lo
y
e
d
f
o
r
s
o
lar
p
o
wer
p
r
e
d
ictio
n
.
T
h
is
m
o
d
e
l
h
as
b
ee
n
ch
o
s
en
d
u
e
to
its
ab
ilit
y
to
h
an
d
le
n
o
n
-
lin
ea
r
ity
,
f
ea
t
u
r
e
im
p
o
r
tan
ce
r
an
k
in
g
,
a
n
d
r
o
b
u
s
tn
ess
ag
ain
s
t
o
v
er
f
itti
n
g
.
T
h
e
r
an
d
o
m
f
o
r
es
t
alg
o
r
ith
m
o
p
e
r
ates
b
y
tr
ain
i
n
g
m
u
ltip
le
d
ec
is
io
n
tr
ee
s
o
n
d
if
f
er
en
t
s
u
b
s
ets
o
f
th
e
d
ataset
an
d
av
e
r
ag
in
g
th
eir
p
r
ed
ictio
n
s
.
T
h
e
r
eg
r
ess
io
n
o
u
tp
u
t is ca
lcu
lated
as (
1
)
.
^
=
1
∑
(
)
=
1
(
1
)
W
h
er
e
^
is
th
e
p
r
ed
icted
s
o
lar
p
o
wer
o
u
tp
u
t,
(
)
is
th
e
p
r
ed
ictio
n
f
r
o
m
th
e
i
th
d
ec
is
io
n
tr
ee
,
an
d
is
th
e
to
tal
n
u
m
b
er
o
f
tr
ee
s
in
th
e
f
o
r
est.
T
h
e
m
o
d
el
h
as b
ee
n
tr
ai
n
ed
u
s
in
g
th
e
m
ea
n
ab
s
o
lu
te
er
r
o
r
(
MA
E
)
lo
s
s
f
u
n
ctio
n
,
g
iv
en
b
y
(
2
)
.
=
1
∑
(
−
^
)
=
1
(
2
)
W
h
er
e
r
ep
r
esen
ts
ac
tu
al
v
alu
es,
an
d
^
r
ep
r
esen
ts
p
r
ed
icte
d
v
alu
es
.
Hy
p
er
p
ar
am
eter
tu
n
in
g
h
as
b
ee
n
p
er
f
o
r
m
ed
u
s
in
g
g
r
id
s
ea
r
ch
c
r
o
s
s
-
v
alid
atio
n
(
Gr
id
Sear
ch
C
V)
to
o
p
tim
ize
th
e
f
o
llo
win
g
p
ar
am
eter
s
:
n
u
m
b
er
o
f
tr
ee
s
(
n
_
esti
m
ato
r
s
)
,
m
ax
i
m
u
m
tr
ee
d
ep
th
(
m
ax
_
d
ep
th
)
,
m
in
im
u
m
s
am
p
les
p
e
r
s
p
lit
(
m
in
_
s
am
p
les_
s
p
lit),
an
d
m
in
im
u
m
s
am
p
les p
er
lea
f
(
m
in
_
s
am
p
les_
leaf
)
.
3
.
5
.
P
re
dict
io
n
a
nd
m
o
del e
v
a
lua
t
io
n
Af
ter
tr
ain
in
g
,
th
e
m
o
d
el
wa
s
ap
p
lied
to
th
e
test
d
atase
t
t
o
p
r
ed
ict
s
o
lar
p
o
wer
o
u
t
p
u
t
(
k
W
)
an
d
ev
alu
ated
u
s
in
g
s
tan
d
ar
d
p
er
f
o
r
m
an
ce
m
etr
ics.
MA
E
q
u
an
ti
f
ied
th
e
av
er
ag
e
p
r
ed
ictio
n
er
r
o
r
,
wh
ile
r
o
o
t
m
ea
n
s
q
u
ar
ed
er
r
o
r
(
R
MSE
)
em
p
h
a
s
ized
lar
g
er
d
ev
iatio
n
s
.
T
h
e
R
²
s
co
r
e
ass
es
s
ed
th
e
m
o
d
el’
s
ab
ilit
y
to
ex
p
lain
v
ar
ian
ce
in
p
o
wer
o
u
tp
u
t.
T
o
g
eth
er
,
th
ese
m
etr
ics
p
r
o
v
i
d
ed
a
c
o
m
p
r
eh
en
s
iv
e
e
v
alu
a
tio
n
o
f
p
r
e
d
ictiv
e
ac
cu
r
ac
y
,
d
em
o
n
s
tr
atin
g
th
e
m
o
d
el’
s
ef
f
ec
tiv
en
ess
an
d
r
eliab
ilit
y
in
f
o
r
ec
asti
n
g
s
o
lar
p
o
wer
g
en
er
atio
n
.
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
p
r
ed
ictiv
e
p
er
f
o
r
m
an
ce
o
f
th
e
r
an
d
o
m
f
o
r
est
r
eg
r
ess
o
r
in
f
o
r
ec
asti
n
g
s
o
lar
p
o
wer
o
u
tp
u
t
ac
r
o
s
s
m
ajo
r
I
n
d
ia
n
m
etr
o
cities
d
em
o
n
s
tr
ates
r
em
ar
k
ab
le
ac
cu
r
a
cy
.
T
h
e
m
o
d
el
ac
h
ie
v
ed
an
R
²
s
co
r
e
o
f
0
.
9
9
9
9
,
in
d
icatin
g
a
n
ea
r
-
p
er
f
ec
t
co
r
r
elatio
n
b
etwe
en
p
r
ed
icted
a
n
d
ac
tu
al
v
alu
es.
T
h
is
ex
ce
p
tio
n
ally
h
ig
h
s
co
r
e
s
u
g
g
ests
th
at
th
e
m
o
d
el
ef
f
ec
tiv
ely
ca
p
tu
r
es
th
e
co
m
p
le
x
r
elatio
n
s
h
ip
s
b
etwe
en
m
eteo
r
o
lo
g
ical
p
ar
am
eter
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
1
5
,
No
.
2
,
Ap
r
il 2
0
2
6
:
1
3
6
2
-
1
3
7
0
1366
an
d
s
o
lar
p
o
we
r
g
e
n
er
atio
n
.
Ad
d
itio
n
ally
,
t
h
e
MA
E
was
ca
lcu
lated
at
0
.
1
5
k
W
,
h
ig
h
li
g
h
tin
g
th
e
m
in
im
al
d
ev
iatio
n
b
etwe
en
p
r
ed
icted
a
n
d
ac
tu
al
v
alu
es.
E
x
tr
em
e
g
r
a
d
ien
t
b
o
o
s
tin
g
(
XGBo
o
s
t
)
an
d
g
r
a
d
ien
t
b
o
o
s
tin
g
r
eg
r
ess
o
r
(
GB
R
)
also
s
h
o
wed
ex
ce
llen
t
p
e
r
f
o
r
m
an
ce
with
R
²
s
co
r
es
ab
o
v
e
0
.
9
8
a
n
d
r
elativ
ely
lo
w
er
r
o
r
m
etr
ics,
m
ak
in
g
th
em
s
u
itab
le
alter
n
ativ
es,
esp
ec
ially
i
n
s
ce
n
ar
io
s
r
e
q
u
ir
in
g
s
ca
lab
le
an
d
f
ast
tr
ain
in
g
s
o
lu
tio
n
s
.
T
h
ese
en
s
em
b
le
-
b
a
s
ed
m
o
d
els
ef
f
ec
tiv
ely
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ce
o
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er
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itti
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im
p
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o
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u
m
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ar
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es th
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o
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el
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ce
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o
r
s
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lar
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p
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ictio
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b
y
6
r
e
g
r
ess
o
r
s
.
T
ab
le
1
.
Mo
d
el
p
er
f
o
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m
a
n
ce
c
o
m
p
ar
is
o
n
M
o
d
e
l
R
² Sc
o
r
e
M
A
E
(
k
W
)
R
M
S
E
(
k
W
)
R
a
n
d
o
m
f
o
r
e
s
t
r
e
g
r
e
ss
o
r
0
.
9
9
9
9
0
.
1
5
0
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1
9
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u
p
p
o
r
t
v
e
c
t
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r
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r
e
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r
(
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R
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0
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9
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8
2
0
.
6
7
0
.
8
9
G
B
R
0
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9
8
1
4
0
.
4
2
0
.
5
4
X
G
B
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o
st
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e
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e
sso
r
0
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9
8
4
1
0
.
3
9
0
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5
1
Li
n
e
a
r
r
e
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r
e
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si
o
n
0
.
8
9
4
7
1
.
1
2
1
.
3
7
D
e
c
i
s
i
o
n
t
r
e
e
r
e
g
r
e
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o
r
0
.
9
3
2
3
0
.
8
3
1
.
0
4
T
h
e
SVR
an
d
d
ec
is
io
n
tr
ee
r
eg
r
ess
o
r
p
er
f
o
r
m
ed
m
o
d
er
atel
y
well,
with
R
²
v
alu
es
ar
o
u
n
d
0
.
9
5
an
d
0
.
9
3
,
r
esp
ec
tiv
ely
.
W
h
ile
SV
R
is
k
n
o
wn
f
o
r
h
an
d
lin
g
n
o
n
-
lin
ea
r
d
ata
ef
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ec
tiv
ely
,
its
p
er
f
o
r
m
an
ce
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g
h
tly
less
co
m
p
etitiv
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m
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ar
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s
em
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le
ap
p
r
o
ac
h
es.
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is
i
o
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t
r
ee
s
,
th
o
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g
h
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ter
p
r
eta
b
le,
ten
d
ed
to
o
v
e
r
f
it,
wh
ich
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cr
ea
s
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r
e
d
ictio
n
e
r
r
o
r
s
.
L
astl
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lin
ea
r
r
e
g
r
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io
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ield
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e
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o
west
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s
co
r
e
o
f
0
.
8
9
4
7
a
n
d
th
e
h
ig
h
est
er
r
o
r
s
(
MA
E
:
1
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1
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3
7
k
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,
in
d
ic
atin
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its
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itatio
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s
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llin
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m
p
lex
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non
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lin
ea
r
p
atter
n
s
ass
o
ciate
d
with
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o
lar
p
o
wer
g
e
n
er
atio
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.
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h
e
lo
w
er
r
o
r
r
ates
af
f
ir
m
t
h
e
r
o
b
u
s
tn
ess
o
f
th
e
m
o
d
el
in
h
a
n
d
lin
g
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iv
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s
e
clim
atic
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n
d
itio
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ac
r
o
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s
d
if
f
er
en
t
g
eo
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r
ap
h
ical
r
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io
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s
.
Giv
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ig
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if
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v
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in
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er
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Fig
u
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3
v
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o
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Fig
u
r
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.
Featu
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Featu
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ates
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ased
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Fig
u
r
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5
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i
g
u
r
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4
.
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e
at
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m
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t
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F
i
g
u
r
e
5
.
A
c
t
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al
v
s
.
p
r
e
d
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ct
e
d
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l
a
r
p
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w
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u
t
p
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t
A
m
ajo
r
s
tr
en
g
th
o
f
th
is
s
tu
d
y
lies
in
its
p
r
ac
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elev
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n
ce
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g
y
m
an
ag
em
e
n
t
an
d
p
o
lic
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o
r
m
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latio
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.
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u
r
ate
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o
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asti
n
g
s
u
p
p
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ts
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s
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ak
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g
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o
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m
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y
s
to
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ag
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d
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alan
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,
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d
d
is
tr
ib
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tio
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la
n
n
in
g
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n
th
e
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o
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tex
t
o
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I
n
d
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s
a
m
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itio
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s
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y
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g
ets,
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e
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r
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o
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ased
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o
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g
m
o
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n
s
u
b
s
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tially
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r
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e
th
e
ef
f
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cy
a
n
d
r
eliab
ilit
y
o
f
s
o
lar
en
e
r
g
y
u
tili
za
tio
n
.
Su
ch
p
r
e
d
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e
f
r
am
e
wo
r
k
s
en
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le
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o
licy
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ak
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s
to
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lan
o
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tim
al
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lar
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ar
m
lo
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tio
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s
,
en
h
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ce
g
r
i
d
in
f
r
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ct
u
r
e,
an
d
d
esig
n
ef
f
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tiv
e
e
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er
g
y
s
to
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a
g
e
s
tr
ateg
ies
to
m
an
a
g
e
in
ter
m
itten
cy
.
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n
ad
d
itio
n
,
r
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en
tial
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d
co
m
m
er
cial
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o
lar
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s
er
s
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n
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en
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it
f
r
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m
r
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ec
asts
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y
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etter
esti
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atin
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er
g
y
g
en
er
atio
n
,
o
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tim
izin
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n
s
u
m
p
tio
n
p
atter
n
s
,
an
d
p
la
n
n
in
g
b
ac
k
u
p
p
o
we
r
r
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ir
e
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ts
.
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ite
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e
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o
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g
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e
r
f
o
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m
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ce
o
f
th
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
,
ce
r
tain
lim
itatio
n
s
r
em
ain
.
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h
e
d
ataset
em
p
lo
y
ed
in
th
is
s
tu
d
y
is
lim
ited
to
h
is
to
r
ical
wea
th
e
r
d
ata
f
r
o
m
t
h
e
y
ea
r
2
0
2
3
.
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n
c
o
r
p
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2
5
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t
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ss
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i
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p
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rtme
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t
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m
p
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ter
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rin
g
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t
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o
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u
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s
h
m
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iah
Un
iv
e
rsity
,
Va
d
d
e
sw
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ra
m
,
G
u
n
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r
(d
ist),
An
d
h
ra
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ra
d
e
sh
,
In
d
ia.
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re
se
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rc
h
in
tere
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c
l
u
d
e
m
a
c
h
in
e
lea
rn
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g
,
d
a
ta
m
in
in
g
,
a
n
d
c
lo
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d
c
o
m
p
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ti
n
g
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
b
m
a
n
in
d
h
a
r@k
l
u
n
i
v
e
rsity
.
in
.
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