I
nte
rna
t
io
na
l J
o
urna
l o
f
I
nfo
rm
a
t
ics a
nd
Co
m
m
un
ica
t
io
n T
ec
hn
o
lo
g
y
(
I
J
-
I
CT
)
Vo
l.
1
5
,
No
.
1
,
Ma
r
ch
20
2
6
,
p
p
.
38
4
~
39
2
I
SS
N:
2252
-
8
7
7
6
,
DOI
:
1
0
.
1
1
5
9
1
/iji
ct
.
v
1
5
i
1
.
pp
38
4
-
39
2
384
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ict.
ia
esco
r
e.
co
m
O
ptimi
zing
so
la
r
energy
f
o
recas
tin
g
and site adj
ust
ment w
ith
ma
chine learning
techniqu
es
Deba
ni P
ra
s
a
d M
i
s
hra
1
,
J
a
y
a
nta
K
um
a
r
Sa
hu
2
,
So
ub
ha
g
y
a
Ra
nja
n Na
y
a
k
1
,
Anura
g
P
a
nd
a
1
,
P
riy
a
ns
hu
P
a
ra
m
j
it
Da
s
h
1
,
Su
re
nd
er
Red
dy
Sa
lk
uti
3
1
D
e
p
a
r
t
me
n
t
o
f
El
e
c
t
r
i
c
a
l
En
g
i
n
e
e
r
i
n
g
,
I
I
I
T
B
h
u
b
a
n
e
sw
a
r
,
B
h
u
b
a
n
e
sw
a
r
,
O
d
i
s
h
a
,
I
n
d
i
a
2
D
e
p
a
r
t
me
n
t
o
f
El
e
c
t
r
i
c
a
l
a
n
d
El
e
c
t
r
o
n
i
c
s E
n
g
i
n
e
e
r
i
n
g
,
V
i
g
n
a
n
'
s I
n
st
i
t
u
t
e
o
f
I
n
f
o
r
mat
i
o
n
T
e
c
h
n
o
l
o
g
y
,
V
i
sa
k
h
a
p
a
t
n
a
m,
I
n
d
i
a
3
D
e
p
a
r
t
me
n
t
o
f
R
a
i
l
r
o
a
d
a
n
d
El
e
c
t
r
i
c
a
l
E
n
g
i
n
e
e
r
i
n
g
,
W
o
o
s
o
n
g
U
n
i
v
e
r
s
i
t
y
,
D
a
e
j
e
o
n
,
R
e
p
u
b
l
i
c
o
f
K
o
r
e
a
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
J
u
n
1
,
2
0
2
4
R
ev
is
ed
J
u
l 7
,
2
0
2
5
Acc
ep
ted
Oct
7
,
2
0
2
5
Esti
m
a
ti
o
n
o
f
so
lar
ra
d
iati
o
n
is
a
k
e
y
tas
k
in
o
p
t
imiz
in
g
t
h
e
o
p
e
ra
ti
o
n
o
f
p
o
we
r
sy
ste
m
s
in
c
o
rp
o
ra
ti
n
g
h
ig
h
lev
e
ls
o
f
p
h
o
to
v
o
lt
a
ic
(P
V)
g
e
n
e
ra
ti
o
n
.
Th
is
p
a
p
e
r
d
isc
u
ss
e
s
th
e
a
p
p
li
c
a
ti
o
n
o
f
m
a
c
h
in
e
lea
rn
i
n
g
tec
h
n
iq
u
e
s,
n
a
m
e
ly
e
x
trem
e
g
ra
d
ien
t
b
o
o
stin
g
(XG
BT)
a
n
d
ra
n
d
o
m
fo
re
st
(RF
),
t
o
imp
r
o
v
e
a
c
c
u
ra
c
y
in
th
e
fo
re
c
a
stin
g
o
f
s
o
lar
ra
d
iatio
n
wh
il
e
a
d
a
p
ti
n
g
f
o
r
d
iffere
n
t
sites
.
Util
izin
g
d
a
tas
e
ts
su
c
h
a
s
m
e
teo
ro
lo
g
ica
l
a
n
d
so
lar
ra
d
iati
o
n
d
a
ta,
t
h
e
su
g
g
e
ste
d
m
o
d
e
ls
d
e
m
o
n
stra
te
t
h
e
e
n
h
a
n
c
e
m
e
n
t
o
f
fo
re
c
a
stin
g
a
c
c
u
ra
c
y
b
y
3
9
%
fro
m
t
ra
d
it
i
o
n
a
ll
y
a
p
p
li
e
d
sta
ti
stica
l
p
ra
c
ti
c
e
s.
Alo
n
g
with
th
is,
th
is
stu
d
y
a
lso
e
n
c
o
m
p
a
ss
e
s
h
o
w
e
n
d
o
g
e
n
o
u
s
a
n
d
e
x
o
g
e
n
o
u
s
fa
c
to
rs
c
o
u
l
d
b
e
in
v
o
lv
e
d
i
n
b
e
tt
e
r
p
re
d
icti
o
n
s
o
f
s
o
lar
e
n
e
rg
y
a
v
a
il
a
b
il
it
y
.
F
ro
m
o
u
r
fin
d
in
g
s,
XG
BT,
a
s
w
e
ll
a
s
o
th
e
r
m
a
c
h
in
e
lea
rn
in
g
tec
h
n
i
q
u
e
s,
d
o
e
n
jo
y
su
p
e
ri
o
r
p
e
rfo
rm
a
n
c
e
lev
e
ls wh
e
n
it
c
o
m
e
s to
th
e
f
o
r
e
c
a
stin
g
o
f
so
lar ra
d
iat
io
n
,
wh
ich
in
tu
r
n
p
r
o
m
o
tes
e
fficie
n
t
m
a
n
a
g
e
m
e
n
t
a
n
d
p
o
ten
ti
a
l
a
d
a
p
tatio
n
o
f
so
lar
e
n
e
rg
y
sy
ste
m
s.
T
h
is
st
u
d
y
d
e
m
o
n
stra
tes
h
o
w
t
h
is
las
t
g
e
n
e
ra
ti
o
n
o
f
a
lg
o
rit
h
m
s
c
o
u
l
d
b
e
a
p
p
l
ied
t
o
n
o
ti
c
e
a
b
l
y
imp
r
o
v
e
t
h
e
e
fficie
n
c
y
o
f
so
lar
p
o
we
r
f
o
re
c
a
stin
g
a
n
d
th
e
re
b
y
c
o
n
tri
b
u
te
t
o
m
o
re
su
sta
in
a
b
le
a
n
d
re
li
a
b
le
e
n
e
rg
y
sy
ste
m
s a
s a
b
y
p
r
o
d
u
c
t
o
f
th
a
t.
K
ey
w
o
r
d
s
:
E
x
tr
em
e
g
r
a
d
ien
t b
o
o
s
tin
g
Ma
ch
in
e
lear
n
in
g
R
an
d
o
m
f
o
r
est
R
en
ewa
b
le
en
er
g
y
So
lar
f
o
r
ec
asti
n
g
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Su
r
en
d
er
R
ed
d
y
Salk
u
ti
Dep
ar
tm
en
t o
f
R
ailr
o
ad
an
d
E
lectr
ical
E
n
g
in
ee
r
in
g
,
W
o
o
s
o
n
g
Un
iv
er
s
ity
J
ay
an
g
-
Do
n
g
,
Do
n
g
-
Gu
,
Dae
j
eo
n
,
3
4
6
0
6
,
R
ep
u
b
lic
o
f
Ko
r
e
a
E
m
ail: su
r
en
d
er
@
wsu
.
ac
.
k
r
1.
I
NT
RO
D
UCT
I
O
N
On
e
o
f
th
e
m
o
s
t
ab
u
n
d
an
t
r
e
n
ewa
b
le
s
o
u
r
ce
s
is
s
o
lar
en
er
g
y
,
attr
ac
tin
g
a
lo
t
o
f
atten
tio
n
at
ev
er
y
ch
an
ce
to
b
e
a
to
o
l
to
c
o
m
b
at
clim
ate
ch
an
g
e
an
d
ab
ate
th
e
u
s
e
o
f
f
o
s
s
il
f
u
els.
P
h
o
to
v
o
lta
ic
(
PV
)
tech
n
o
lo
g
y
h
as
b
ee
n
d
ev
elo
p
e
d
to
b
ec
o
m
e
ev
er
m
o
r
e
ac
ce
s
s
ib
l
e,
ef
f
i
cien
t,
an
d
ch
ea
p
er
with
th
e
p
r
eser
v
atio
n
o
f
s
o
lar
p
o
wer
s
u
p
p
ly
.
Yet,
v
a
r
iab
ilit
y
in
s
o
lar
r
a
d
iatio
n
m
ay
b
e
ca
u
s
ed
b
y
wea
th
er
co
n
d
itio
n
s
o
r
th
e
lo
ca
tio
n
o
f
in
s
tallatio
n
-
r
ea
d
ily
p
o
s
es
ch
all
en
g
es
f
o
r
p
o
wer
s
y
s
tem
o
p
e
r
a
to
r
s
[
1
]
-
[
3
]
.
Acc
u
r
ate
f
o
r
ec
asts
o
f
s
o
lar
r
ad
iatio
n
ar
e
im
p
o
r
tan
t
f
o
r
o
p
tim
izin
g
s
o
lar
p
o
wer
g
en
er
atio
n
in
th
e
i
n
ter
est
o
f
en
s
u
r
in
g
g
r
id
s
tab
ili
ty
an
d
m
a
x
im
izin
g
th
e
in
teg
r
atio
n
o
f
r
en
ewa
b
le
e
n
er
g
y
s
o
u
r
ce
s
in
to
th
e
elec
tr
ic
al
g
r
id
.
T
r
ad
itio
n
ally
,
m
o
s
t
f
o
r
ec
asti
n
g
m
eth
o
d
s
f
o
r
s
o
lar
r
a
d
iatio
n
r
ely
o
n
p
u
r
ely
s
tatis
tical
m
o
d
els
th
at
u
s
e
h
is
to
r
ical
d
ata
to
d
escr
ib
e
th
e
f
u
tu
r
e
a
v
ailab
ilit
y
o
f
s
o
lar
en
er
g
y
.
Desp
ite
th
eir
p
o
p
u
lar
ity
,
th
ese
m
eth
o
d
s
h
av
e
lim
ited
ap
p
licab
ilit
y
b
ec
au
s
e
th
ey
g
en
er
ally
ca
n
n
o
t
ac
co
u
n
t
f
o
r
in
tr
icate
en
v
ir
o
n
m
en
tal
f
ac
to
r
s
i
n
f
lu
e
n
cin
g
s
o
lar
r
ad
iatio
n
[
4
]
,
[
5
]
.
Ho
wev
er
,
m
ac
h
in
e
lear
n
in
g
tech
n
i
q
u
es
p
r
o
v
id
e
a
h
ig
h
ly
d
y
n
am
ic
an
d
ac
c
u
r
at
e
s
o
lu
tio
n
b
y
p
r
o
ce
s
s
in
g
lar
g
e
d
atasets
th
at
ca
n
b
r
in
g
o
u
t
th
e
p
atter
n
s
m
is
s
ed
b
y
o
th
er
m
o
d
els.
T
h
is
en
ab
les
th
e
ML
alg
o
r
ith
m
to
i
m
p
r
o
v
e
f
o
r
ec
asti
n
g
s
ig
n
if
ican
tly
as
well
as
ad
a
p
t
b
etter
th
an
tr
ad
itio
n
al
m
o
d
els.
T
h
is
p
a
p
er
d
is
cu
s
s
es
a
m
eth
o
d
o
lo
g
y
to
im
p
r
o
v
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
Op
timiz
in
g
s
o
la
r
en
erg
y
fo
r
ec
a
s
tin
g
a
n
d
s
ite
a
d
ju
s
tmen
t
w
ith
ma
ch
in
e
le
a
r
n
in
g
…
(
De
b
a
n
i P
r
a
s
a
d
Mis
h
r
a
)
385
s
o
lar
r
ad
iatio
n
f
o
r
ec
asti
n
g
u
s
in
g
m
ac
h
in
e
lear
n
i
n
g
tech
n
iq
u
es
s
u
ch
as
ex
tr
em
e
g
r
ad
ien
t
b
o
o
s
tin
g
(
XGBo
o
s
t)
an
d
r
an
d
o
m
f
o
r
est
(
R
F)
.
T
h
e
en
d
o
g
e
n
o
u
s
in
teg
r
atio
n
o
f
en
d
o
g
e
n
o
u
s
/ex
o
g
en
o
u
s
d
ata
in
p
u
ts
is
u
s
ed
f
o
r
o
p
tim
izatio
n
o
f
f
o
r
ec
ast ac
cu
r
ac
y
an
d
s
ite
ad
ap
tatio
n
o
f
s
o
lar
en
er
g
y
s
y
s
tem
s
[
6
]
.
T
h
e
r
est
o
f
t
h
e
p
ap
e
r
is
p
r
es
en
ted
in
th
e
f
o
llo
win
g
way
.
I
n
s
ec
tio
n
2
d
escr
ib
es
th
e
m
et
h
o
d
o
lo
g
y
:
d
ata
co
llectio
n
,
p
r
ep
r
o
ce
s
s
in
g
,
an
d
im
p
lem
e
n
tatio
n
o
f
M
L
m
o
d
els.
I
n
s
ec
tio
n
3
r
ef
er
s
to
th
e
r
esu
lts
an
d
d
is
cu
s
s
es
th
e
p
er
f
o
r
m
a
n
ce
o
f
XGBT
an
d
R
F
m
o
d
els.
C
o
n
cl
u
s
io
n
s
b
ased
o
n
f
in
d
in
g
s
an
d
d
ir
ec
tio
n
s
f
o
r
f
u
r
th
er
r
esear
ch
ar
e
p
r
o
v
id
e
d
in
th
e
f
i
n
al
s
ec
tio
n
.
2.
P
RO
P
O
SE
D
M
E
T
H
O
DO
L
O
G
Y
T
h
e
f
o
llo
win
g
co
m
p
o
n
en
ts
m
ak
e
u
p
th
e
s
o
lar
r
ad
iatio
n
f
o
r
ec
asti
n
g
m
eth
o
d
,
wh
ic
h
is
d
e
p
icted
in
Fig
u
r
e
1
.
T
h
e
au
to
m
atic
wea
th
er
s
tatio
n
s
s
itu
ated
at
g
r
o
u
n
d
lev
el
n
ex
t
to
t
h
e
s
o
lar
p
o
wer
p
lan
ts
g
ath
er
m
eteo
r
o
lo
g
ical
d
ata
s
u
ch
as
clo
u
d
s
,
s
p
ee
d
o
f
th
e
win
d
,
s
u
n
s
h
in
e
d
u
r
atio
n
,
an
d
p
r
ess
u
r
e
in
th
e
atm
o
s
p
h
er
e.
First,
a
leg
it
an
d
ap
p
r
o
p
r
iate
s
et
o
f
d
ata
is
g
ain
ed
f
r
o
m
it,
wh
ich
h
as
s
o
lar
r
ad
iatio
n
as
d
ata.
Seco
n
d
:
k
ee
p
in
g
in
m
in
d
th
e
r
e
p
r
esen
tatio
n
o
f
d
ata,
d
ata
in
p
u
ts
,
a
n
d
d
a
ta
an
aly
s
is
[
7
]
,
[
8
]
.
Nex
t
is
a
s
tag
e
f
o
r
d
ata
p
r
e
-
p
r
o
ce
s
s
in
g
,
wh
ich
in
cl
u
d
es
o
u
tlier
s
an
d
m
is
s
in
g
d
ata
r
em
o
v
al.
No
r
m
aliza
tio
n
is
ac
co
m
p
lis
h
ed
f
o
r
th
is
p
u
r
p
o
s
e:
to
elim
in
ate
t
h
e
ex
tr
em
es
with
o
u
t
in
an
y
way
d
etr
ac
tin
g
f
r
o
m
th
eir
im
p
o
r
tan
ce
.
Nex
t,
we
m
o
v
e
t
o
f
ea
tu
r
e
s
elec
tio
n
.
Su
b
s
eq
u
en
tl
y
,
th
e
d
ata
s
h
o
u
l
d
b
e
p
lace
d
i
n
to
tr
ain
in
g
,
v
alid
ati
o
n
,
a
n
d
t
esti
n
g
d
atasets
,
an
d
th
e
d
ata
will
b
ec
o
m
e
v
ar
ied
alg
o
r
ith
m
ty
p
es.
T
h
er
ef
o
r
e,
X
GB
o
o
s
t
an
d
R
F
m
o
d
els
ar
e
u
s
ed
to
m
ea
s
u
r
e
th
e
p
r
ec
is
io
n
o
f
t
h
e
s
o
lar
r
a
d
iatio
n
p
r
ed
ictio
n
al
g
o
r
ith
m
s
[
9
]
.
Fig
u
r
e
1
.
Flo
wch
ar
t
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
o
lo
g
y
2
.
1
.
Da
t
a
a
na
ly
s
is
a
nd
da
t
a
prepro
ce
s
s
i
ng
Her
e
in
th
is
s
ec
tio
n
,
th
e
r
esu
lts
o
f
th
e
r
esear
ch
ar
e
illu
s
tr
ated
,
an
d
a
co
m
p
r
eh
e
n
s
iv
e
d
is
cu
s
s
io
n
is
also
d
o
n
e
[
1
0
]
.
R
esu
lts
ar
e
p
r
ese
n
ted
in
th
e
f
o
r
m
o
f
g
r
ap
h
s
,
f
ig
u
r
es,
tab
les,
an
d
o
th
er
s
th
at
f
ac
ilit
ate
ea
s
y
u
n
d
er
s
tan
d
i
n
g
b
y
th
e
r
ea
d
er
.
T
h
e
d
is
cu
s
s
io
n
is
d
o
n
e
in
n
u
m
er
o
u
s
s
u
b
-
s
ec
tio
n
s
.
T
h
e
p
r
o
p
er
in
itiatin
g
s
tep
o
f
th
e
s
o
lar
r
ad
iatio
n
f
o
r
ec
asti
n
g
m
o
d
el
th
r
o
u
g
h
ac
cu
r
ate
p
r
e
-
p
r
o
ce
s
s
in
g
p
r
o
to
co
ls
is
im
p
o
r
tan
t.
Data
clea
n
in
g
is
th
e
f
ir
s
t
s
tep
[
1
1
]
.
I
t
in
clu
d
es
th
e
f
o
r
m
atio
n
o
f
th
e
co
e
f
f
icien
t
f
r
o
m
d
if
f
er
en
t
s
ca
les
o
f
m
ea
s
u
r
em
en
t.
I
n
r
esear
ch
o
n
s
o
lar
lig
h
t,
o
n
l
y
th
o
s
e
ar
e
co
n
s
id
er
ed
th
at
ar
e
m
ad
e
d
u
r
in
g
th
e
d
ay
[
1
2
]
.
I
f
th
e
d
ata
f
r
o
m
th
e
en
tire
s
er
ies
wer
e
em
p
lo
y
ed
,
n
ea
r
ly
all
o
b
s
er
v
ed
v
al
u
es
wo
u
ld
b
e
ze
r
o
,
an
d
th
e
f
o
r
ec
ast
v
alu
es
wo
u
ld
also
b
e
ap
p
r
o
x
im
ated
to
ze
r
o
,
lead
i
n
g
th
e
m
o
d
el
to
o
v
er
esti
m
a
te
th
e
er
r
o
r
in
p
r
e
d
ictio
n
a
n
d
th
e
f
o
r
ec
asti
n
g
p
er
f
o
r
m
an
ce
[
1
3
]
-
[
1
5
]
.
T
h
e
I
QR
m
eth
o
d
is
u
s
ed
to
d
etec
t
o
u
tlier
s
in
d
ata
o
n
ce
it
is
o
r
d
er
ed
,
wh
e
r
e
ea
ch
q
u
ar
tile
co
n
tain
s
2
5
p
er
ce
n
t
o
f
th
e
to
tal
d
ata.
T
h
r
ee
s
ets
ar
e
cr
ea
ted
f
r
o
m
th
e
h
is
to
r
ic
al
d
ataset:
tr
ain
in
g
,
v
alid
atio
n
,
an
d
test
in
g
.
T
h
e
tr
ain
in
g
d
ata
s
et
is
th
e
b
asis
f
o
r
w
h
ich
ML
m
o
d
els
ar
e
r
e
q
u
ir
ed
[
1
6
]
,
[
1
7
]
.
T
h
is
co
m
es
with
th
e
r
eq
u
ir
ed
in
p
u
t
an
d
o
u
t
p
u
t.
Ho
wev
er
,
th
e
test
in
g
s
et
i
s
u
s
ed
as
th
e
m
o
d
el
p
er
f
o
r
m
an
ce
esti
m
ato
r
o
n
d
ata
n
o
t
in
v
o
lv
e
d
in
th
e
tr
ain
in
g
p
r
o
ce
s
s
.
7
0
%
o
f
th
e
d
ata
is
c
o
v
er
ed
b
y
th
e
tr
ain
in
g
s
et,
1
0
%
o
f
th
e
d
ata
is
with
th
e
v
alid
atio
n
s
et,
an
d
2
0
%
is
with
th
e
te
s
t
s
et
p
ar
am
eter
s
.
Hy
p
er
p
ar
a
m
ete
r
tu
n
in
g
is
ess
en
tia
l
an
d
cr
u
cial
to
th
e
alg
o
r
ith
m
’
s
p
er
f
o
r
m
an
ce
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
1
5
,
No
.
1
,
Ma
r
ch
20
2
6
:
38
4
-
39
2
386
2
.
2
.
M
a
chine le
a
rning
a
lg
o
ri
t
hm
s
V
ar
io
u
s
k
in
d
s
o
f
m
ac
h
in
e
l
ea
r
n
in
g
ap
p
r
o
ac
h
es
ar
e
u
s
ed
f
o
r
s
o
lar
en
er
g
y
f
o
r
ec
asti
n
g
,
n
am
ely
:
XGBo
o
s
t,
ar
tific
ial
n
eu
r
al
n
etwo
r
k
(
ANN
)
,
an
d
RF
.
T
h
ese
alg
o
r
ith
m
s
u
s
e
h
is
to
r
ical
wea
th
er
a
n
d
s
o
lar
r
ad
iatio
n
d
ata,
to
g
eth
er
with
o
th
er
im
p
o
r
tan
t
p
ar
am
ete
r
s
,
to
h
elp
f
o
r
ec
ast
s
o
lar
en
er
g
y
p
r
o
d
u
ctio
n
.
W
ith
th
e
ap
p
licatio
n
o
f
o
u
r
p
r
o
p
o
s
ed
m
eth
o
d
o
l
o
g
y
,
th
e
s
elec
tio
n
o
f
ML
alg
o
r
ith
m
s
is
ass
es
s
ed
in
ter
m
s
o
f
th
eir
ca
p
ab
ilit
y
to
war
d
th
e
p
r
o
d
u
cti
o
n
o
f
s
o
lar
r
ad
iatio
n
b
y
ev
alu
a
tin
g
th
eir
p
er
f
o
r
m
a
n
ce
.
Pre
d
ic
tin
g
s
o
lar
r
ad
iatio
n
an
d
s
ite
ad
ap
tatio
n
tec
h
n
iq
u
es
is
d
o
n
e
b
y
ar
tific
ial
in
tellig
en
c
e,
o
r
,
in
tech
n
ical
ter
m
s
,
th
e
XGBT
m
o
d
el.
2
.
3
.
XG
B
T
An
ef
f
ec
tiv
e
an
d
r
ec
o
g
n
ized
m
ac
h
in
e
lear
n
in
g
tech
n
i
q
u
e
b
u
ilt
o
n
th
e
g
r
ad
ien
t b
o
o
s
tin
g
f
r
am
ewo
r
k
is
ca
lled
XGBo
o
s
t
[
1
8
]
.
T
h
e
X
GB
o
o
s
t
alg
o
r
ith
m
u
s
es
a
tr
ee
en
s
em
b
le
m
o
d
el
to
p
r
ed
ict
th
e
o
u
tp
u
t
[
1
9
]
.
W
h
e
n
b
o
o
s
tin
g
,
tr
ee
s
ar
e
co
n
s
tr
u
cte
d
o
n
e
af
ter
th
e
o
th
e
r
s
o
th
at
e
ac
h
o
n
e
lear
n
s
f
r
o
m
a
n
d
r
ed
u
ce
s
th
e
m
is
tak
es
o
f
t
h
e
o
n
e
b
e
f
o
r
e
i
t
[
2
0
]
.
A
n
a
l
g
o
r
i
t
h
m
t
h
a
t
u
s
es
g
r
a
d
i
e
n
t
d
es
c
e
n
t
i
s
u
s
e
d
t
o
m
i
n
i
m
i
ze
t
h
e
e
r
r
o
r
s
w
h
e
n
n
e
w
m
o
d
e
l
s
a
r
e
a
d
d
e
d
.
T
h
e
X
GB
o
o
s
t
m
o
d
el
u
s
es
a
p
a
r
a
l
le
l
r
u
n
n
i
n
g
p
r
o
c
e
s
s
t
o
e
n
h
a
n
c
e
t
h
e
t
r
a
i
n
i
n
g
t
i
m
e
[
2
1
]
,
[
2
2
]
.
2
.
4
.
RF
m
o
del
A
u
s
ef
u
l
tr
ee
-
lear
n
in
g
m
eth
o
d
is
th
e
RF
alg
o
r
ith
m
.
I
n
th
e
tr
ain
in
g
s
tag
e,
it
r
esu
lts
in
m
an
y
d
ec
is
io
n
tr
ee
s
[
2
3
]
,
[
2
4
]
.
E
v
er
y
tr
ee
is
b
u
ilt
u
tili
zin
g
an
ar
b
itra
r
y
s
u
b
g
r
o
u
p
o
f
th
e
d
ata
s
et
to
ass
ess
a
r
an
d
o
m
s
u
b
s
et
o
f
q
u
alities
i
n
ea
ch
p
ar
titi
o
n
.
W
h
en
p
r
ed
ictin
g
,
th
e
alg
o
r
it
h
m
av
er
ag
es
o
r
v
o
tes
o
n
th
e
o
u
tp
u
t
o
f
ea
c
h
tr
ee
[
2
5
]
,
[
2
6
]
.
T
h
e
r
esu
lts
o
f
th
is
co
llectiv
e
d
ec
is
io
n
-
m
ak
in
g
p
r
o
ce
s
s
,
wh
ich
is
aid
ed
b
y
th
e
in
s
ig
h
ts
o
f
s
ev
er
al
tr
ee
s
,
ar
e
co
m
p
atib
le
a
n
d
p
r
ec
is
e.
E
ac
h
o
f
th
ese
tr
ee
s
r
ep
r
es
en
ts
a
d
if
f
e
r
en
t
e
x
p
er
t
with
a
f
o
cu
s
o
n
a
d
ef
in
ite
ar
ea
o
f
th
e
d
ata
[
27
]
,
[
2
8
]
.
E
s
s
en
tially
,
th
ey
p
er
f
o
r
m
s
ep
ar
at
ely
,
m
in
im
izin
g
th
e
p
o
s
s
ib
ilit
y
th
at
th
e
s
u
b
tleties
o
f
a
s
in
g
le
tr
ee
will
h
av
e
an
in
ap
p
r
o
p
r
iate
im
p
ac
t
o
n
t
h
e
m
o
d
el.
A
k
e
y
co
m
p
o
n
e
n
t
o
f
R
F
’
s
tr
ain
in
g
ac
ce
s
s
is
b
ag
g
in
g
,
wh
ich
in
v
o
l
v
es
tak
in
g
n
u
m
e
r
o
u
s
b
o
o
ts
tr
ap
s
am
p
les
f
r
o
m
th
e
o
r
ig
in
al
d
ata
an
d
u
s
in
g
th
em
to
s
am
p
le
r
ep
lace
m
en
t
in
s
tan
ce
s
.
I
n
co
n
clu
s
io
n
,
a
d
is
tin
ct
s
u
b
s
et
o
f
d
ata
is
u
s
ed
f
o
r
ea
ch
d
ec
is
io
n
tr
ee
,
w
h
ich
ad
d
s
d
iv
er
s
it
y
to
th
e
tr
ain
i
n
g
p
r
o
ce
s
s
an
d
f
o
r
tifie
s
th
e
m
o
d
el
[
2
9
]
,
[
30
].
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
I
n
th
is
s
ec
tio
n
,
two
m
ain
task
s
ar
e
p
er
f
o
r
m
ed
:
p
r
esen
tin
g
th
e
f
in
d
i
n
g
s
(
r
esu
lts
)
a
n
d
in
ter
p
r
e
tin
g
th
eir
s
ig
n
if
ican
ce
(
d
is
cu
s
s
io
n
)
.
Ov
e
r
all,
th
e
XGBo
o
s
t
m
o
d
el
d
em
o
n
s
tr
ates
r
o
b
u
s
t
p
er
f
o
r
m
a
n
ce
,
with
k
ey
f
ea
tu
r
es
d
r
iv
in
g
its
p
r
e
d
ictiv
e
p
o
wer
.
W
h
ile
ac
cu
r
ac
y
is
g
en
er
ally
h
ig
h
,
ad
d
r
ess
in
g
o
u
tlier
s
an
d
i
m
p
r
o
v
i
n
g
tem
p
o
r
al
alig
n
m
en
t
ca
n
f
u
r
th
er
o
p
tim
iz
e
th
e
m
o
d
el
’
s
r
eliab
ilit
y
an
d
p
r
ec
is
io
n
.
T
h
e
R
F
m
o
d
el
s
h
o
w
s
s
tr
o
n
g
p
r
ed
ictiv
e
p
er
f
o
r
m
an
ce
,
with
k
ey
f
ea
tu
r
es
d
r
iv
in
g
its
ac
cu
r
ac
y
.
W
h
ile
th
e
m
o
d
el
g
en
e
r
ally
p
r
ed
ic
ts
well,
ad
d
r
ess
in
g
o
u
tlier
s
an
d
p
e
r
io
d
s
o
f
d
is
cr
ep
an
cy
ca
n
f
u
r
th
e
r
en
h
a
n
ce
its
r
e
liab
ilit
y
an
d
p
r
ec
is
io
n
.
F
i
g
u
r
e
2
r
e
p
r
e
s
e
n
t
s
t
h
e
s
ca
t
t
e
r
p
l
o
t
b
e
t
w
e
e
n
t
r
u
e
a
n
d
e
x
p
e
c
t
e
d
v
a
l
u
e
s
f
o
r
t
h
e
s
o
l
a
r
r
a
d
ia
t
i
o
n
f
o
r
e
c
a
s
t
i
n
g
m
o
d
e
l
u
s
i
n
g
t
h
e
XGB
o
o
s
t
m
o
d
e
l
.
E
v
e
r
y
p
l
o
t
p
o
i
n
t
i
n
d
i
ca
t
es
a
d
a
t
a
p
o
i
n
t
.
E
v
e
r
y
p
o
i
n
t
’
s
X
c
o
o
r
d
i
n
a
t
e
i
n
d
i
c
a
te
s
t
h
e
a
c
t
u
al
s
o
l
a
r
r
a
d
i
at
i
o
n
v
a
l
u
e
(
g
r
o
u
n
d
t
r
u
t
h
)
f
o
r
t
h
a
t
d
a
t
a
p
o
in
t
.
T
h
e
y
-
c
o
o
r
d
i
n
a
t
e
r
e
p
r
e
s
e
n
ts
t
h
e
p
r
e
d
i
ct
e
d
s
o
l
a
r
r
a
d
i
a
t
i
o
n
v
a
l
u
e
g
e
n
e
r
at
e
d
b
y
t
h
e
X
GB
o
o
s
t
m
o
d
e
l
f
o
r
t
h
e
s
a
m
e
d
a
ta
p
o
i
n
t
.
Fi
g
u
r
e
3
r
ep
r
e
s
e
n
t
s
t
h
e
f
e
at
u
r
e
i
m
p
o
r
t
a
n
c
e
p
l
o
t
u
s
i
n
g
d
i
f
f
e
r
e
n
t
p
a
r
a
m
e
t
e
r
s
s
u
c
h
as
t
e
m
p
e
r
a
t
u
r
e
,
w
i
n
d
s
p
e
e
d
,
a
n
d
o
t
h
e
r
s
v
s
.
t
h
e
i
r
F
s
c
o
r
e
s
.
A
f
e
a
t
u
r
e
i
m
p
o
r
t
a
n
c
e
p
l
o
t
u
s
i
n
g
a
n
X
GB
o
o
s
t
m
o
d
e
l
i
l
l
u
s
t
r
at
es
t
h
e
s
i
g
n
i
f
i
c
a
n
c
e
o
f
m
a
n
y
a
tt
r
ib
u
t
e
s
i
n
f
o
r
e
c
a
s
t
i
n
g
t
h
e
d
e
s
i
r
e
d
v
a
r
i
a
b
l
e
.
E
a
c
h
v
e
r
t
i
c
a
l
b
a
r
r
e
p
r
e
s
e
n
t
s
a
f
e
a
t
u
r
e
(
o
r
v
a
r
i
a
b
l
e
)
u
s
e
d
i
n
t
h
e
X
G
B
o
o
s
t
m
o
d
e
l
.
F
e
a
t
u
r
e
s
w
i
t
h
ta
l
l
e
r
b
a
r
s
a
r
e
c
o
n
s
i
d
e
r
e
d
m
o
r
e
i
m
p
o
r
t
a
n
t
b
e
ca
u
s
e
t
h
e
y
h
a
v
e
a
b
i
g
g
e
r
i
m
p
a
ct
o
n
t
h
e
m
o
d
e
l
’
s
p
r
e
d
i
c
ti
o
n
s
.
T
h
e
i
m
p
o
r
t
a
n
c
e
o
f
e
a
c
h
f
e
a
t
u
r
e
i
s
q
u
a
n
t
i
f
i
e
d
b
y
a
n
u
m
e
r
i
c
a
l
s
c
o
r
e
,
w
h
i
c
h
is
k
n
o
w
n
a
s
t
h
e
F
s
c
o
r
e
.
Fig
u
r
e
4
s
h
o
ws
ac
tu
al
v
er
s
u
s
ex
p
ec
ted
v
alu
es
o
v
er
tim
e
f
o
r
an
XG
B
o
o
s
t
m
o
d
el.
T
h
e
b
lu
e
lin
e
r
ep
r
esen
t
s
th
e
ac
tu
al
v
alu
es
o
v
er
Un
ix
tim
e,
wh
er
ea
s
th
e
o
r
an
g
e
lin
e
r
ep
r
esen
ts
th
e
p
r
ed
icted
v
alu
es
o
v
er
Un
ix
tim
e.
Fig
u
r
e
5
r
ep
r
esen
ts
th
e
g
r
ap
h
th
at
p
r
ed
icts
th
e
ac
cu
r
ac
y
o
f
th
e
m
o
d
e
l.
T
h
e
X
-
ax
is
r
ep
r
esen
ts
d
en
s
ity
,
an
d
th
e
Y
-
ax
is
s
h
o
ws
h
o
w
th
e
ac
tu
al
an
d
ex
p
ec
t
ed
v
alu
es
d
if
f
er
f
r
o
m
ea
ch
o
th
er
.
Fig
u
r
e
6
s
h
o
ws
th
e
ac
tu
al
v
er
s
u
s
ex
p
ec
ted
v
alu
es
o
f
a
RF
m
o
d
el.
A
v
is
u
al
r
ep
r
esen
tatio
n
m
ea
s
u
r
es
h
o
w
well
th
e
tr
u
e
v
alu
es
m
atch
th
e
m
o
d
el
’
s
p
r
ed
ictio
n
s
.
E
ac
h
p
lo
t
p
o
in
t
i
n
d
icate
s
a
d
ata
p
o
in
t.
E
v
er
y
d
ata
p
o
in
t
’
s
x
-
co
o
r
d
in
a
te
in
d
icate
s
its
r
ea
l
v
alu
e,
wh
ile
th
e
R
F
m
o
d
el
’
s
p
r
ed
ict
ed
v
alu
e
f
o
r
th
e
s
am
e
d
ata
p
o
in
t
is
r
ep
r
ese
n
ted
b
y
th
e
y
-
c
o
o
r
d
in
ate.
Fig
u
r
e
7
s
h
o
ws
th
e
ac
tu
al
v
e
r
s
u
s
ex
p
ec
ted
v
alu
es
o
v
er
tim
e
f
o
r
a
RF
.
T
h
e
X
-
a
x
is
s
h
o
ws
th
e
Un
i
x
tim
e,
wh
ile
th
e
Y
-
ax
is
s
h
o
ws
th
e
v
a
lu
es
o
f
th
e
tar
g
et
v
ar
iab
le
th
at
th
e
RF
m
o
d
el
is
p
r
ed
ictin
g
.
T
h
e
tar
g
et
v
ar
iab
le
’
s
ac
tu
al
v
alu
es
ar
e
r
ep
r
esen
ted
as
d
ata
p
o
in
ts
o
n
th
e
g
r
a
p
h
.
T
h
e
tar
g
et
v
ar
iab
le
p
r
e
d
icted
v
alu
es
p
r
o
d
u
ce
d
b
y
th
e
RF
m
o
d
el
a
r
e
also
p
lo
tted
o
n
th
e
g
r
ap
h
.
T
h
e
d
is
tr
ib
u
ti
o
n
o
f
d
if
f
er
en
ce
s
b
etwe
en
ac
tu
al
an
d
ex
p
ec
ted
v
alu
es
f
o
r
a
RF
m
o
d
el
is
s
h
o
wn
in
Fig
u
r
e
8
,
wh
ich
p
r
o
v
id
es
in
s
ig
h
ts
i
n
to
th
e
er
r
o
r
s
m
ad
e
b
y
th
e
m
o
d
el
ac
r
o
s
s
v
ar
io
u
s
p
r
ed
ictio
n
s
.
T
h
e
d
is
cr
ep
an
cy
b
etwe
en
th
e
ac
tu
al
an
d
e
x
p
ec
t
ed
v
alu
es
f
o
r
ea
c
h
d
a
ta
p
o
in
t
is
d
is
p
lay
ed
o
n
th
e
h
o
r
izo
n
tal
ax
is
.
T
h
is
d
is
cr
ep
a
n
cy
is
co
m
p
u
te
d
as
th
e
ac
tu
al
v
alu
e
m
in
u
s
th
e
p
r
ed
icte
d
v
al
u
e
an
d
is
co
m
m
o
n
l
y
r
ef
er
r
ed
t
o
as
th
e
r
esid
u
al
o
r
p
r
ed
ictio
n
er
r
o
r
.
Usu
ally
,
th
e
d
en
s
ity
o
f
d
ata
p
o
in
ts
with
a
s
p
ec
if
ic
d
if
f
er
en
ce
b
etwe
en
ac
tu
al
an
d
an
ticip
ate
d
v
alu
es is
s
h
o
wn
b
y
th
e
v
e
r
tical
ax
is
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
Op
timiz
in
g
s
o
la
r
en
erg
y
fo
r
ec
a
s
tin
g
a
n
d
s
ite
a
d
ju
s
tmen
t
w
ith
ma
ch
in
e
le
a
r
n
in
g
…
(
De
b
a
n
i P
r
a
s
a
d
Mis
h
r
a
)
387
Fig
u
r
e
2
.
Scatter
p
l
o
t b
etwe
en
ac
tu
al
an
d
p
r
ed
icted
v
alu
es
Fig
u
r
e
3
.
Featu
r
e
im
p
o
r
ta
n
ce
o
f
XGBT
m
o
d
el
Fig
u
r
e
4
.
Gr
a
p
h
s
h
o
win
g
ac
tu
al
v
s
.
p
r
ed
icted
v
alu
es o
v
e
r
tim
e
f
o
r
t
h
e
XGBo
o
s
t
m
o
d
el
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
1
5
,
No
.
1
,
Ma
r
ch
20
2
6
:
38
4
-
39
2
388
Fig
u
r
e
5
.
Gr
a
p
h
s
h
o
win
g
th
e
d
is
tr
ib
u
tio
n
o
f
d
if
f
er
e
n
ce
s
b
etwe
en
ac
tu
al
an
d
p
r
ed
icte
d
v
alu
e
s
Fig
u
r
e
6
.
Scatter
p
l
o
t o
f
ac
tu
al
v
s
.
p
r
ed
icted
v
alu
es
o
f
R
F
m
o
d
el
Fig
u
r
e
7
.
Gr
a
p
h
s
h
o
win
g
Actu
al
v
s
.
p
r
ed
icted
Valu
es o
v
er
ti
m
e
o
f
R
F M
o
d
el
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
Op
timiz
in
g
s
o
la
r
en
erg
y
fo
r
ec
a
s
tin
g
a
n
d
s
ite
a
d
ju
s
tmen
t
w
ith
ma
ch
in
e
le
a
r
n
in
g
…
(
De
b
a
n
i P
r
a
s
a
d
Mis
h
r
a
)
389
Fig
u
r
e
8
.
Gr
a
p
h
s
h
o
win
g
th
e
d
is
tr
ib
u
tio
n
o
f
d
if
f
er
e
n
ce
s
b
etwe
en
ac
tu
al
an
d
p
r
ed
icte
d
v
alu
e
s
o
f
th
e
R
F m
o
d
el
W
h
en
we
d
is
cu
s
s
R
MSE
to
m
ac
h
in
e
lear
n
in
g
,
we
ar
e
m
o
s
tly
d
is
cu
s
s
in
g
it
s
u
s
e
as
a
p
er
f
o
r
m
a
n
ce
m
etr
ic
f
o
r
p
r
e
d
ictio
n
-
o
r
f
o
r
ec
asti
n
g
-
b
ased
alg
o
r
ith
m
s
.
A
b
asic
b
u
ild
in
g
b
l
o
ck
i
n
s
tatis
tical
an
aly
s
is
an
d
m
ac
h
in
e
lear
n
in
g
,
th
e
r
o
o
t
m
ea
n
s
q
u
ar
e
er
r
o
r
p
r
o
v
id
es
a
s
tr
aig
h
tf
o
r
war
d
b
u
t
u
s
ef
u
l
m
e
asu
r
e
o
f
p
r
ed
ictio
n
er
r
o
r
.
On
th
e
o
th
er
h
a
n
d
,
a
lar
g
er
R
MSE
in
d
icate
s
a
lar
g
er
d
if
f
er
en
ce
b
etwe
en
th
e
ex
p
ec
ted
an
d
o
b
s
er
v
ed
r
esu
lts
.
As
m
en
tio
n
ed
in
T
ab
l
e
1
,
th
e
R
MSE
v
alu
e
o
f
th
e
X
GB
o
o
s
t
was
ca
lcu
lated
an
d
f
o
u
n
d
t
o
b
e
1
0
1
.
5
8
1
8
,
wh
er
ea
s
th
e
R
MSE
v
alu
e
o
f
t
h
e
RF
m
o
d
el
was
f
o
u
n
d
to
b
e
1
9
0
.
2
1
.
As
m
en
tio
n
e
d
ab
o
v
e
,
lo
wer
th
e
R
MSE
v
alu
e,
an
d
th
e
m
o
d
el
ca
n
p
r
e
d
ict
m
o
r
e
ac
cu
r
ately
.
So
XG
B
o
o
s
t
m
o
d
el
p
r
ed
ictio
n
s
ar
e
m
o
r
e
ac
cu
r
ate,
an
d
it
g
iv
es a
co
r
r
ec
t e
s
tim
ate
o
f
th
e
av
er
ag
e
d
e
v
iatio
n
b
e
twee
n
th
e
ex
p
ec
ted
a
n
d
ac
tu
al
v
alu
es in
th
e
d
ataset.
T
ab
le
1
.
Statis
tical
ev
alu
atio
n
in
d
ices
M
o
d
e
l
s
M
e
a
n
sq
u
a
r
e
d
e
r
r
o
r
(
M
S
E
)
R
o
o
t
me
a
n
sq
u
a
r
e
d
e
r
r
o
r
(
R
M
S
E
)
X
G
B
o
o
st
m
o
d
e
l
1
0
3
1
8
.
8
7
6
9
1
0
1
.
5
8
1
8
R
a
n
d
o
m
f
o
r
e
s
t
1
9
0
.
2
1
4
9
1
9
0
.
2
1
4
9
4.
CO
NCLU
SI
O
NS
T
h
e
p
r
o
p
o
s
ed
r
esear
ch
s
tr
iv
es to
d
eter
m
in
e
th
e
ef
f
icien
cy
o
f
f
o
r
ec
asti
n
g
s
o
lar
r
ad
iatio
n
b
y
XGBo
o
s
t,
RF
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
.
XGBT
o
u
tp
er
f
o
r
m
e
d
th
e
class
ical
m
eth
o
d
s
with
an
im
p
r
o
v
e
m
en
t
r
ate
o
f
3
9
%
an
d
g
av
e
b
etter
r
esu
lts
with
lo
wer
R
MSE
co
m
p
ar
e
d
to
tr
ad
itio
n
al
m
eth
o
d
s
,
h
en
ce
e
s
tab
lis
h
in
g
s
u
p
er
io
r
f
o
r
ec
asti
n
g
ca
p
ab
ilit
y
.
I
t
ca
n
also
b
e
in
f
er
r
ed
f
r
o
m
th
e
a
n
aly
s
is
th
at
an
R
F
m
o
d
el
also
p
r
o
v
ed
to
g
iv
e
g
o
o
d
r
esu
lts
.
Ho
wev
er
,
th
is
is
n
o
t
as
ac
cu
r
ate
as
t
h
at
p
r
o
v
id
ed
b
y
XGBT.
Su
ch
r
esu
lts
in
d
icate
th
e
p
r
o
s
p
ec
ts
i
n
wh
ich
m
ac
h
in
e
lear
n
in
g
ca
n
o
p
tim
ize
s
o
lar
en
er
g
y
f
o
r
ec
as
tin
g
an
d
s
ite
ad
ap
tatio
n
to
in
t
eg
r
ate
s
o
lar
en
er
g
y
in
to
p
o
wer
g
r
id
s
ef
f
ec
tiv
ely
.
F
u
r
th
er
m
a
k
in
g
u
s
e
o
f
lar
g
e
d
at
asets
as
well
as
ad
v
an
ce
d
alg
o
r
ith
m
s
,
p
r
ed
ictio
n
s
ar
e
ev
en
t
u
ally
g
e
n
er
ated
th
a
t
tu
r
n
o
u
t
m
o
r
e
r
eliab
le.
Fu
r
th
er
s
tep
s
co
u
ld
t
h
en
f
o
cu
s
o
n
r
ea
l
-
tim
e
d
ata
in
clu
s
io
n
an
d
in
tr
o
d
u
ce
n
ew
m
ac
h
in
e
-
lear
n
i
n
g
tech
n
iq
u
es
f
o
r
th
e
f
u
r
th
e
r
im
p
r
o
v
em
en
t
o
f
ac
cu
r
ac
y
ac
r
o
s
s
d
if
f
er
en
t
r
eg
io
n
s
.
ACK
NO
WL
E
DG
E
M
E
NT
S
T
h
is
r
esear
ch
wo
r
k
was
s
u
p
p
o
r
ted
b
y
th
e
“
W
o
o
s
o
n
g
Un
iv
er
s
ity
’
s
Aca
d
em
ic
R
esear
ch
Fu
n
d
in
g
-
202
5
”
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
1
5
,
No
.
1
,
Ma
r
ch
20
2
6
:
38
4
-
39
2
390
F
UNDING
I
NF
O
R
M
A
T
I
O
N
T
h
is
r
esear
ch
wo
r
k
was
s
u
p
p
o
r
ted
by
“
W
o
o
s
o
n
g
Un
iv
e
r
s
ity
’
s
Aca
d
em
ic
R
esear
ch
Fu
n
d
in
g
-
2
0
2
5
”
.
AUTHO
R
CO
NT
RI
B
UT
I
O
NS ST
A
T
E
M
E
N
T
T
h
is
jo
u
r
n
al
u
s
es
th
e
C
o
n
tr
ib
u
to
r
R
o
les
T
ax
o
n
o
m
y
(
C
R
ed
iT)
to
r
ec
o
g
n
ize
in
d
iv
id
u
al
au
th
o
r
co
n
tr
ib
u
tio
n
s
,
r
ed
u
ce
au
th
o
r
s
h
ip
d
is
p
u
tes,
an
d
f
ac
ilit
ate
co
llab
o
r
atio
n
.
Na
m
e
o
f
Aut
ho
r
C
M
So
Va
Fo
I
R
D
O
E
Vi
Su
P
Fu
Deb
an
i Pr
asad
Mish
r
a
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
J
ay
an
ta
Ku
m
ar
Sah
u
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
So
u
b
h
ag
y
a
R
an
jan
Nay
ak
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
An
u
r
ag
Pan
d
a
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
Priy
an
s
h
u
Par
am
jit
Dash
✓
✓
✓
✓
✓
✓
✓
✓
✓
Su
r
en
d
er
R
ed
d
y
Salk
u
ti
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
C
:
C
o
n
c
e
p
t
u
a
l
i
z
a
t
i
o
n
M
:
M
e
t
h
o
d
o
l
o
g
y
So
:
So
f
t
w
a
r
e
Va
:
Va
l
i
d
a
t
i
o
n
Fo
:
Fo
r
mal
a
n
a
l
y
s
i
s
I
:
I
n
v
e
s
t
i
g
a
t
i
o
n
R
:
R
e
so
u
r
c
e
s
D
:
D
a
t
a
C
u
r
a
t
i
o
n
O
:
W
r
i
t
i
n
g
-
O
r
i
g
i
n
a
l
D
r
a
f
t
E
:
W
r
i
t
i
n
g
-
R
e
v
i
e
w
&
E
d
i
t
i
n
g
Vi
:
Vi
su
a
l
i
z
a
t
i
o
n
Su
:
Su
p
e
r
v
i
s
i
o
n
P
:
P
r
o
j
e
c
t
a
d
mi
n
i
st
r
a
t
i
o
n
Fu
:
Fu
n
d
i
n
g
a
c
q
u
i
si
t
i
o
n
CO
NF
L
I
C
T
O
F
I
N
T
E
R
E
S
T
ST
A
T
E
M
E
NT
Au
th
o
r
s
s
tate
n
o
co
n
f
lict o
f
in
t
er
est.
I
NF
O
RM
E
D
CO
NS
E
N
T
W
e
h
av
e
o
b
tain
ed
in
f
o
r
m
ed
c
o
n
s
en
t f
r
o
m
all
in
d
iv
id
u
als in
c
lu
d
ed
in
t
h
is
s
tu
d
y
.
E
T
H
I
CAL AP
P
RO
V
AL
T
h
is
ar
ticle
d
o
es
n
o
t
co
n
tain
an
y
s
tu
d
ies
with
h
u
m
a
n
p
ar
ti
cip
an
ts
o
r
an
im
al
s
tu
d
ies
p
er
f
o
r
m
ed
b
y
an
y
o
f
th
e
au
th
o
r
s
.
DATA AV
AI
L
AB
I
L
I
T
Y
T
h
e
d
atasets
u
s
ed
an
d
/
o
r
an
al
y
ze
d
d
u
r
in
g
th
e
c
u
r
r
en
t
s
tu
d
y
av
ailab
le
f
r
o
m
th
e
c
o
r
r
esp
o
n
d
in
g
au
th
o
r
[
SR
S],
o
n
r
ea
s
o
n
ab
le
r
e
q
u
ests
.
RE
F
E
R
E
NC
E
S
[
1
]
H
.
D
i
n
ç
e
r
,
S
.
Y
ü
k
s
e
l
,
Ç
.
Ç
a
ğ
l
a
y
a
n
,
D
.
Y
a
v
u
z
,
a
n
d
D
.
K
a
r
a
r
o
ğ
l
u
,
“
C
a
n
r
e
n
e
w
a
b
l
e
e
n
e
r
g
y
i
n
v
e
s
t
me
n
t
s
b
e
a
s
o
l
u
t
i
o
n
t
o
t
h
e
e
n
e
r
g
y
-
so
u
r
c
e
d
h
i
g
h
i
n
f
l
a
t
i
o
n
p
r
o
b
l
e
m?
,
”
i
n
Ma
n
a
g
i
n
g
I
n
f
l
a
t
i
o
n
a
n
d
S
u
p
p
l
y
C
h
a
i
n
D
i
sr
u
p
t
i
o
n
s i
n
t
h
e
G
l
o
b
a
l
Ec
o
n
o
m
y
,
I
G
I
G
l
o
b
a
l
,
2
0
2
2
,
p
p
.
2
2
0
–
2
3
8
.
[
2
]
F
.
A
k
r
a
m,
F
.
A
s
g
h
a
r
,
M
.
A
.
M
a
j
e
e
d
,
W
.
A
mj
a
d
,
M
.
O
.
M
a
n
z
o
o
r
,
a
n
d
A
.
M
u
n
i
r
,
“
T
e
c
h
n
o
-
e
c
o
n
o
mi
c
o
p
t
i
m
i
z
a
t
i
o
n
a
n
a
l
y
si
s
o
f
st
a
n
d
-
a
l
o
n
e
r
e
n
e
w
a
b
l
e
e
n
e
r
g
y
sy
st
e
m
f
o
r
r
e
m
o
t
e
a
r
e
a
s,”
S
u
st
a
i
n
a
b
l
e
E
n
e
r
g
y
T
e
c
h
n
o
l
o
g
i
e
s
a
n
d
Ass
e
ssm
e
n
t
s
,
v
o
l
.
3
8
,
p
.
1
0
0
6
7
3
,
A
p
r
.
2
0
2
0
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
set
a
.
2
0
2
0
.
1
0
0
6
7
3
.
[
3
]
A
.
R
a
z
mj
o
o
,
A
.
G
h
a
z
a
n
f
a
r
i
,
P
.
A
.
Ø
s
t
e
r
g
a
a
r
d
,
a
n
d
S
.
A
b
e
d
i
,
“
D
e
s
i
g
n
a
n
d
a
n
a
l
y
si
s
o
f
g
r
i
d
-
c
o
n
n
e
c
t
e
d
s
o
l
a
r
p
h
o
t
o
v
o
l
t
a
i
c
sy
s
t
e
ms
f
o
r
su
s
t
a
i
n
a
b
l
e
d
e
v
e
l
o
p
me
n
t
o
f
r
e
mo
t
e
a
r
e
a
s,
”
E
n
e
r
g
i
e
s
,
v
o
l
.
1
6
,
n
o
.
7
,
p
.
3
1
8
1
,
M
a
r
.
2
0
2
3
,
d
o
i
:
1
0
.
3
3
9
0
/
e
n
1
6
0
7
3
1
8
1
.
[
4
]
D
.
J
h
u
n
j
h
u
n
w
a
l
l
a
,
D
.
P
.
M
i
s
h
r
a
,
D
.
H
e
m
b
r
a
m,
a
n
d
S
.
R
.
S
a
l
k
u
t
i
,
“
R
e
v
o
l
u
t
i
o
n
i
z
i
n
g
d
o
mes
t
i
c
so
l
a
r
p
o
w
e
r
s
y
st
e
ms
w
i
t
h
I
o
T
-
e
n
a
b
l
e
d
b
l
o
c
k
c
h
a
i
n
t
e
c
h
n
o
l
o
g
y
,
”
I
n
t
e
rn
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
Ap
p
l
i
e
d
P
o
w
e
r
En
g
i
n
e
e
ri
n
g
,
v
o
l
.
1
3
,
n
o
.
1
,
p
p
.
2
5
5
–
2
6
2
,
M
a
r
.
2
0
2
4
,
d
o
i
:
1
0
.
1
1
5
9
1
/
i
j
a
p
e
.
v
1
3
.
i
1
.
p
p
2
5
5
-
262.
[
5
]
G
.
S
.
K
h
a
sr
a
g
h
i
,
“
D
e
v
e
l
o
p
i
n
g
a
m
u
l
t
i
-
a
g
e
n
t
b
a
s
e
d
si
m
u
l
a
t
i
o
n
m
o
d
e
l
o
f
u
s
e
r
s
’
w
a
y
f
i
n
d
i
n
g
a
s
a
r
e
p
r
e
se
n
t
a
t
i
o
n
a
n
d
p
o
st
o
c
c
u
p
a
n
c
y
e
v
a
l
u
a
t
i
o
n
(
P
O
E)
t
o
o
l
i
n
a
h
o
sp
i
t
a
l
,
”
Pro
m
e
t
h
e
u
s
,
v
o
l
.
4
,
p
p
.
1
0
2
–
1
0
5
,
2
0
2
0
.
[
6
]
S
.
T
.
S
h
a
b
e
s
t
a
r
i
,
A
.
K
a
s
a
e
i
a
n
,
M
.
A
.
V
a
z
i
r
i
R
a
d
,
H
.
F
o
r
o
o
t
a
n
F
a
r
d
,
W
.
M
.
Y
a
n
,
a
n
d
F
.
P
o
u
r
f
a
y
a
z
,
“
T
e
c
h
n
o
-
f
i
n
a
n
c
i
a
l
e
v
a
l
u
a
t
i
o
n
o
f
a
h
y
b
r
i
d
r
e
n
e
w
a
b
l
e
s
o
l
u
t
i
o
n
f
o
r
s
u
p
p
l
y
i
n
g
t
h
e
p
r
e
d
i
c
t
e
d
p
o
w
e
r
o
u
t
a
g
e
s
b
y
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
me
t
h
o
d
s
i
n
r
u
r
a
l
a
r
e
a
s,
”
Re
n
e
w
a
b
l
e
E
n
e
r
g
y
,
v
o
l
.
1
9
4
,
p
p
.
1
3
0
3
–
1
3
2
5
,
J
u
l
.
2
0
2
2
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
r
e
n
e
n
e
.
2
0
2
2
.
0
5
.
1
6
0
.
[
7
]
K
.
J.
N
a
m,
S
.
H
w
a
n
g
b
o
,
a
n
d
C
.
K
.
Y
o
o
,
“
A
d
e
e
p
l
e
a
r
n
i
n
g
-
b
a
s
e
d
f
o
r
e
c
a
st
i
n
g
mo
d
e
l
f
o
r
r
e
n
e
w
a
b
l
e
e
n
e
r
g
y
s
c
e
n
a
r
i
o
s
t
o
g
u
i
d
e
su
st
a
i
n
a
b
l
e
e
n
e
r
g
y
p
o
l
i
c
y
:
A
c
a
s
e
st
u
d
y
o
f
K
o
r
e
a
,
”
R
e
n
e
w
a
b
l
e
a
n
d
S
u
st
a
i
n
a
b
l
e
E
n
e
r
g
y
Re
v
i
e
w
s
,
v
o
l
.
1
2
2
,
p
.
1
0
9
7
2
5
,
A
p
r
.
2
0
2
0
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
r
ser
.
2
0
2
0
.
1
0
9
7
2
5
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
Op
timiz
in
g
s
o
la
r
en
erg
y
fo
r
ec
a
s
tin
g
a
n
d
s
ite
a
d
ju
s
tmen
t
w
ith
ma
ch
in
e
le
a
r
n
in
g
…
(
De
b
a
n
i P
r
a
s
a
d
Mis
h
r
a
)
391
[
8
]
A
.
K
h
o
sr
a
v
i
,
L.
M
a
c
h
a
d
o
,
a
n
d
R
.
O
.
N
u
n
e
s
,
“
T
i
me
-
s
e
r
i
e
s
p
r
e
d
i
c
t
i
o
n
o
f
w
i
n
d
sp
e
e
d
u
s
i
n
g
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
a
l
g
o
r
i
t
h
ms:
a
c
a
s
e
st
u
d
y
O
so
r
i
o
w
i
n
d
f
a
r
m,
B
r
a
z
i
l
,
”
A
p
p
l
i
e
d
E
n
e
r
g
y
,
v
o
l
.
2
2
4
,
p
p
.
5
5
0
–
5
6
6
,
A
u
g
.
2
0
1
8
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
a
p
e
n
e
r
g
y
.
2
0
1
8
.
0
5
.
0
4
3
.
[
9
]
S
.
J.
A
l
-
S
w
a
i
e
d
i
,
A
.
I
.
A
l
t
mi
mi
,
a
n
d
A
.
A
.
A
l
-
K
h
a
l
i
d
i
,
“
D
e
s
i
g
n
o
f
a
su
s
t
a
i
n
a
b
l
e
c
i
t
y
i
n
I
r
a
q
u
si
n
g
S
A
M
p
r
o
g
r
a
m
t
o
c
a
l
c
u
l
a
t
e
r
e
n
e
w
a
b
l
e
e
n
e
r
g
y
,
”
I
r
a
q
i
J
o
u
r
n
a
l
o
f
S
c
i
e
n
ce
,
v
o
l
.
6
2
,
n
o
.
1
1
,
p
p
.
4
4
7
5
–
4
4
8
8
,
D
e
c
.
2
0
2
1
,
d
o
i
:
1
0
.
2
4
9
9
6
/
i
j
s
.
2
0
2
1
.
6
2
.
1
1
(
S
I
)
.
2
8
.
[
1
0
]
A
.
B
o
r
e
t
t
i
,
“
I
n
t
e
g
r
a
t
i
o
n
o
f
so
l
a
r
t
h
e
r
mal
a
n
d
p
h
o
t
o
v
o
l
t
a
i
c
,
w
i
n
d
,
a
n
d
b
a
t
t
e
r
y
e
n
e
r
g
y
st
o
r
a
g
e
t
h
r
o
u
g
h
A
I
i
n
N
EO
M
c
i
t
y
,
”
En
e
r
g
y
a
n
d
AI
,
v
o
l
.
3
,
p
.
1
0
0
0
3
8
,
M
a
r
.
2
0
2
1
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
g
y
a
i
.
2
0
2
0
.
1
0
0
0
3
8
.
[
1
1
]
M
.
Te
m
i
z
a
n
d
I
.
D
i
n
c
e
r
,
“
D
e
v
e
l
o
p
men
t
o
f
so
l
a
r
a
n
d
w
i
n
d
b
a
s
e
d
h
y
d
r
o
g
e
n
e
n
e
r
g
y
s
y
st
e
ms
f
o
r
su
st
a
i
n
a
b
l
e
c
o
mm
u
n
i
t
i
e
s,”
En
e
r
g
y
C
o
n
v
e
rsi
o
n
a
n
d
Ma
n
a
g
e
m
e
n
t
,
v
o
l
.
2
6
9
,
p
.
1
1
6
0
9
0
,
O
c
t
.
2
0
2
2
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
n
c
o
n
ma
n
.
2
0
2
2
.
1
1
6
0
9
0
.
[
1
2
]
M
.
A
b
d
e
l
sat
t
a
r
,
M
.
A
.
I
smei
l
,
M
.
M
.
A
.
A
z
i
m
Za
y
e
d
,
A
.
A
b
d
e
l
m
o
e
t
y
,
a
n
d
A
.
Ema
d
-
E
l
d
e
e
n
,
“
A
ssess
i
n
g
mac
h
i
n
e
l
e
a
r
n
i
n
g
a
p
p
r
o
a
c
h
e
s
f
o
r
p
h
o
t
o
v
o
l
t
a
i
c
e
n
e
r
g
y
p
r
e
d
i
c
t
i
o
n
i
n
s
u
st
a
i
n
a
b
l
e
e
n
e
r
g
y
s
y
st
e
ms,”
I
E
EE
Ac
c
e
ss
,
v
o
l
.
1
2
,
p
p
.
1
0
7
5
9
9
–
1
0
7
6
1
5
,
2
0
2
4
,
d
o
i
:
1
0
.
1
1
0
9
/
A
C
C
ESS
.
2
0
2
4
.
3
4
3
7
1
9
1
.
[
1
3
]
D
.
P
.
M
i
s
h
r
a
,
S
.
Je
n
a
,
R
.
S
e
n
a
p
a
t
i
,
A
.
P
a
n
i
g
r
a
h
i
,
a
n
d
S
.
R
.
S
a
l
k
u
t
i
,
“
G
l
o
b
a
l
s
o
l
a
r
r
a
d
i
a
t
i
o
n
f
o
r
e
c
a
s
t
u
si
n
g
a
n
e
n
sem
b
l
e
l
e
a
r
n
i
n
g
a
p
p
r
o
a
c
h
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
rn
a
l
o
f
Po
w
e
r
El
e
c
t
r
o
n
i
c
s
a
n
d
D
ri
v
e
S
y
st
e
m
s
,
v
o
l
.
1
4
,
n
o
.
1
,
p
p
.
4
9
6
–
50
5
,
M
a
r
.
2
0
2
3
,
d
o
i
:
1
0
.
1
1
5
9
1
/
i
j
p
e
d
s.
v
1
4
.
i
1
.
p
p
4
9
6
-
5
0
5
.
[
1
4
]
K
.
O
l
c
a
y
,
S
.
G
i
r
a
y
T
u
n
c
a
,
a
n
d
M
.
A
r
i
f
O
z
g
u
r
,
“
F
o
r
e
c
a
s
t
i
n
g
a
n
d
p
e
r
f
o
r
m
a
n
c
e
a
n
a
l
y
s
i
s
o
f
e
n
e
r
g
y
p
r
o
d
u
c
t
i
o
n
i
n
s
o
l
a
r
p
o
w
e
r
p
l
a
n
t
s
u
si
n
g
l
o
n
g
s
h
o
r
t
-
t
e
r
m
mem
o
r
y
(
LS
TM
)
a
n
d
r
a
n
d
o
m
f
o
r
e
s
t
mo
d
e
l
s,
”
I
EEE
Ac
c
e
ss
,
v
o
l
.
1
2
,
p
p
.
1
0
3
2
9
9
–
1
0
3
3
1
2
,
2
0
2
4
,
d
o
i
:
1
0
.
1
1
0
9
/
A
C
C
ESS
.
2
0
2
4
.
3
4
3
2
5
7
4
.
[
1
5
]
S
.
P
a
g
i
d
i
p
a
l
a
a
n
d
V
.
S
a
n
d
e
e
p
,
“
O
p
t
i
mal
p
l
a
n
n
i
n
g
o
f
e
l
e
c
t
r
i
c
v
e
h
i
c
l
e
c
h
a
r
g
i
n
g
s
t
a
t
i
o
n
s
a
n
d
d
i
s
t
r
i
b
u
t
e
d
g
e
n
e
r
a
t
o
r
s
w
i
t
h
n
e
t
w
o
r
k
r
e
c
o
n
f
i
g
u
r
a
t
i
o
n
i
n
sm
a
r
t
d
i
s
t
r
i
b
u
t
i
o
n
n
e
t
w
o
r
k
s
c
o
n
s
i
d
e
r
i
n
g
u
n
c
e
r
t
a
i
n
t
i
e
s
,
”
Me
a
su
r
e
m
e
n
t
:
S
e
n
s
o
rs
,
v
o
l
.
3
6
,
2
0
2
4
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
me
a
se
n
.
2
0
2
4
.
1
0
1
4
0
0
.
[
1
6
]
K
.
A
r
a
r
Ta
h
i
r
,
M
.
Z
a
m
o
r
a
n
o
,
a
n
d
J.
O
r
d
ó
ñ
e
z
G
a
r
c
í
a
,
“
S
c
i
e
n
t
i
f
i
c
ma
p
p
i
n
g
o
f
o
p
t
i
m
i
sa
t
i
o
n
a
p
p
l
i
e
d
t
o
m
i
c
r
o
g
r
i
d
s
i
n
t
e
g
r
a
t
e
d
w
i
t
h
r
e
n
e
w
a
b
l
e
e
n
e
r
g
y
s
y
s
t
e
ms,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
rn
a
l
o
f
E
l
e
c
t
r
i
c
a
l
P
o
w
e
r
a
n
d
E
n
e
rg
y
S
y
s
t
e
m
s
,
v
o
l
.
1
4
5
,
p
.
1
0
8
6
9
8
,
F
e
b
.
2
0
2
3
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
i
j
e
p
e
s.
2
0
2
2
.
1
0
8
6
9
8
.
[
1
7
]
A
.
B
a
l
a
l
,
S
.
D
i
n
k
h
a
h
,
F
.
S
h
a
h
a
b
i
,
M
.
H
e
r
r
e
r
a
,
a
n
d
Y
.
L.
C
h
u
a
n
g
,
“
A
r
e
v
i
e
w
o
n
m
u
l
t
i
l
e
v
e
l
i
n
v
e
r
t
e
r
t
o
p
o
l
o
g
i
e
s
,
”
Em
e
rg
i
n
g
S
c
i
e
n
c
e
J
o
u
rn
a
l
,
v
o
l
.
6
,
n
o
.
1
,
p
p
.
1
8
5
–
2
0
0
,
F
e
b
.
2
0
2
2
,
d
o
i
:
1
0
.
2
8
9
9
1
/
ESJ
-
2
0
2
2
-
06
-
01
-
0
1
4
.
[
1
8
]
S
.
D
a
t
t
a
,
A
.
B
a
u
l
,
G
.
C
.
S
a
r
k
e
r
,
P
.
K
.
S
a
d
h
u
,
a
n
d
D
.
R
.
H
o
d
g
e
s,
“
A
c
o
m
p
r
e
h
e
n
si
v
e
r
e
v
i
e
w
o
f
t
h
e
a
p
p
l
i
c
a
t
i
o
n
o
f
mac
h
i
n
e
l
e
a
r
n
i
n
g
i
n
f
a
b
r
i
c
a
t
i
o
n
a
n
d
i
m
p
l
e
me
n
t
a
t
i
o
n
o
f
p
h
o
t
o
v
o
l
t
a
i
c
sy
s
t
e
ms,”
I
EEE
Ac
c
e
ss
,
v
o
l
.
1
1
,
p
p
.
7
7
7
5
0
–
7
7
7
7
8
,
2
0
2
3
,
d
o
i
:
1
0
.
1
1
0
9
/
A
C
C
ESS
.
2
0
2
3
.
3
2
9
8
5
4
2
.
[
19]
P
.
P
o
u
r
mal
e
k
i
,
W
.
A
g
u
t
u
,
A
.
R
e
z
a
e
i
,
a
n
d
N
.
P
o
u
r
m
a
l
e
k
i
,
“
Te
c
h
n
o
-
e
c
o
n
o
mi
c
a
n
a
l
y
si
s
o
f
a
1
2
-
k
W
p
h
o
t
o
v
o
l
t
a
i
c
sy
s
t
e
m
u
si
n
g
a
n
e
f
f
i
c
i
e
n
t
m
u
l
t
i
p
l
e
l
i
n
e
a
r
r
e
g
r
e
ssi
o
n
mo
d
e
l
p
r
e
d
i
c
t
i
o
n
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
rn
a
l
o
f
R
o
b
o
t
i
c
s
a
n
d
C
o
n
t
r
o
l
S
y
st
e
m
s
,
v
o
l
.
2
,
n
o
.
2
,
p
p
.
3
7
0
–
3
7
8
,
J
u
n
.
2
0
2
2
,
d
o
i
:
1
0
.
3
1
7
6
3
/
i
j
r
c
s
.
v
2
i
2
.
7
0
2
.
[
2
0
]
S
.
V
.
A
f
sh
a
r
,
S
.
Es
h
a
g
h
i
,
a
n
d
I
.
K
i
m,
“
P
a
t
t
e
r
n
a
n
a
l
y
s
i
s
o
f
v
i
r
t
u
a
l
l
a
n
d
s
c
a
p
e
w
i
t
h
i
n
e
d
u
c
a
t
i
o
n
a
l
g
a
mes
,
”
J
o
u
rn
a
l
o
f
D
i
g
i
t
a
l
L
a
n
d
sc
a
p
e
Arc
h
i
t
e
c
t
u
re
,
v
o
l
.
7
,
n
o
.
2
0
2
2
,
p
p
.
4
3
5
–
4
4
2
,
2
0
2
2
.
[
2
1
]
S
.
K
a
l
l
i
o
a
n
d
M
.
S
i
r
o
u
x
,
“
P
h
o
t
o
v
o
l
t
a
i
c
p
o
w
e
r
p
r
e
d
i
c
t
i
o
n
f
o
r
so
l
a
r
mi
c
r
o
-
g
r
i
d
o
p
t
i
m
a
l
c
o
n
t
r
o
l
,
”
E
n
e
r
g
y
Re
p
o
r
t
s
,
v
o
l
.
9
,
p
p
.
5
9
4
–
6
0
1
,
M
a
r
.
2
0
2
3
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
g
y
r
.
2
0
2
2
.
1
1
.
0
8
1
.
[
2
2
]
S
.
K
.
P
a
n
d
a
,
P
.
R
a
y
,
a
n
d
D
.
P
.
M
i
sh
r
a
,
“
A
n
e
f
f
i
c
i
e
n
t
s
h
o
r
t
-
t
e
r
m
e
l
e
c
t
r
i
c
p
o
w
e
r
l
o
a
d
f
o
r
e
c
a
st
i
n
g
u
si
n
g
h
y
b
r
i
d
t
e
c
h
n
i
q
u
e
s,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
C
o
m
p
u
t
e
r
I
n
f
o
rm
a
t
i
o
n
S
y
st
e
m
s
a
n
d
I
n
d
u
s
t
ri
a
l
M
a
n
a
g
e
m
e
n
t
Ap
p
l
i
c
a
t
i
o
n
s
,
v
o
l
.
1
2
,
p
p
.
3
8
7
–
3
9
7
,
2
0
2
0
.
[
2
3
]
N
.
Y
a
n
g
,
H
.
H
o
f
ma
n
n
,
J
.
S
u
n
,
a
n
d
Z
.
S
o
n
g
,
“
R
e
ma
i
n
i
n
g
u
s
e
f
u
l
l
i
f
e
p
r
e
d
i
c
t
i
o
n
o
f
l
i
t
h
i
u
m
-
i
o
n
b
a
t
t
e
r
i
e
s
w
i
t
h
l
i
mi
t
e
d
d
e
g
r
a
d
a
t
i
o
n
h
i
s
t
o
r
y
u
s
i
n
g
r
a
n
d
o
m
f
o
r
e
st
,
”
I
EEE
T
ra
n
s
a
c
t
i
o
n
s
o
n
T
ra
n
s
p
o
r
t
a
t
i
o
n
El
e
c
t
ri
f
i
c
a
t
i
o
n
,
v
o
l
.
1
0
,
n
o
.
3
,
p
p
.
5
0
4
9
–
5
0
6
0
,
S
e
p
.
2
0
2
4
,
d
o
i
:
1
0
.
1
1
0
9
/
TTE
.
2
0
2
3
.
3
3
2
3
9
7
6
.
[
2
4
]
Y
.
S
.
K
i
m,
H
.
Y
.
Jo
o
,
J.
W
.
K
i
m,
S
.
Y
.
Je
o
n
g
,
a
n
d
J.
H
.
M
o
o
n
,
“
U
se
o
f
a
b
i
g
d
a
t
a
a
n
a
l
y
s
i
s i
n
r
e
g
r
e
ssi
o
n
o
f
s
o
l
a
r
p
o
w
e
r
g
e
n
e
r
a
t
i
o
n
o
n
m
e
t
e
o
r
o
l
o
g
i
c
a
l
v
a
r
i
a
b
l
e
s
f
o
r
a
K
o
r
e
a
n
s
o
l
a
r
p
o
w
e
r
p
l
a
n
t
,
”
Ap
p
l
i
e
d
S
c
i
e
n
c
e
s
(
S
w
i
t
zer
l
a
n
d
)
,
v
o
l
.
1
1
,
n
o
.
4
,
p
p
.
1
–
1
0
,
F
e
b
.
2
0
2
1
,
d
o
i
:
1
0
.
3
3
9
0
/
a
p
p
1
1
0
4
1
7
7
6
.
[
2
5
]
H
.
D
e
m
o
l
l
i
,
A
.
S
.
D
o
k
u
z
,
A
.
E
c
e
m
i
s
,
a
n
d
M
.
G
o
k
c
e
k
,
“
W
i
n
d
p
o
w
e
r
f
o
r
e
c
a
s
t
i
n
g
b
a
s
e
d
o
n
d
a
i
l
y
w
i
n
d
s
p
e
e
d
d
a
t
a
u
s
i
n
g
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
a
l
g
o
r
i
t
h
m
s
,
”
E
n
e
r
g
y
C
o
n
v
e
r
s
i
o
n
a
n
d
M
a
n
a
g
e
m
e
n
t
,
v
o
l
.
1
9
8
,
p
.
1
1
1
8
2
3
,
O
c
t
.
2
0
1
9
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
n
c
o
n
m
a
n
.
2
0
1
9
.
1
1
1
8
2
3
.
[
2
6
]
M
.
A
.
R
u
s
h
d
i
,
S
.
Y
o
s
h
i
d
a
,
K
.
W
a
t
a
n
a
b
e
,
a
n
d
Y
.
O
h
y
a
,
“
M
a
c
h
i
n
e
l
e
a
r
n
i
n
g
a
p
p
r
o
a
c
h
e
s
f
o
r
t
h
e
r
m
a
l
u
p
d
r
a
f
t
p
r
e
d
i
c
t
i
o
n
i
n
w
i
n
d
s
o
l
a
r
t
o
w
e
r
sy
s
t
e
ms
,
”
Re
n
e
w
a
b
l
e
En
e
rg
y
,
v
o
l
.
1
7
7
,
p
p
.
1
0
0
1
–
1
0
1
3
,
N
o
v
.
2
0
2
1
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
r
e
n
e
n
e
.
2
0
2
1
.
0
6
.
0
3
3
.
[
2
7
]
P
.
S
r
a
v
a
n
t
h
i
,
V
.
S
a
n
d
e
e
p
,
“
S
o
l
v
i
n
g
r
e
a
l
i
s
t
i
c
r
e
a
c
t
i
v
e
p
o
w
e
r
m
a
r
k
e
t
c
l
e
a
r
i
n
g
p
r
o
b
l
e
m
o
f
w
i
n
d
-
t
h
e
r
ma
l
p
o
w
e
r
sy
s
t
e
m
w
i
t
h
sy
st
e
m
se
c
u
r
i
t
y
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
rn
a
l
o
f
Em
e
r
g
i
n
g
E
l
e
c
t
ri
c
P
o
w
e
r
S
y
s
t
e
m
s
,
v
o
l
.
2
3
,
n
o
.
2
,
p
p
.
1
2
5
-
1
4
4
,
2
0
2
2
,
d
o
i
:
1
0
.
1
5
1
5
/
i
j
e
e
p
s
-
2
0
2
1
0
0
6
0
.
[
2
8
]
T.
A
l
S
k
a
i
f
,
S
.
D
e
v
,
L.
V
i
ss
e
r
,
M
.
H
o
ssari
,
a
n
d
W
.
v
a
n
S
a
r
k
,
“
A
s
y
st
e
ma
t
i
c
a
n
a
l
y
si
s
o
f
m
e
t
e
o
r
o
l
o
g
i
c
a
l
v
a
r
i
a
b
l
e
s
f
o
r
P
V
o
u
t
p
u
t
p
o
w
e
r
e
s
t
i
m
a
t
i
o
n
,
”
R
e
n
e
w
a
b
l
e
E
n
e
r
g
y
,
v
o
l
.
1
5
3
,
p
p
.
1
2
–
2
2
,
J
u
n
.
2
0
2
0
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
r
e
n
e
n
e
.
2
0
2
0
.
0
1
.
1
5
0
.
[
2
9
]
T.
M
.
Y
.
K
h
a
n
e
t
a
l
.
,
“
O
p
t
i
m
u
m
l
o
c
a
t
i
o
n
a
n
d
i
n
f
l
u
e
n
c
e
o
f
t
i
l
t
a
n
g
l
e
o
n
p
e
r
f
o
r
ma
n
c
e
o
f
s
o
l
a
r
P
V
p
a
n
e
l
s,
”
J
o
u
rn
a
l
o
f
T
h
e
rm
a
l
An
a
l
y
si
s
a
n
d
C
a
l
o
r
i
m
e
t
r
y
,
v
o
l
.
1
4
1
,
n
o
.
1
,
p
p
.
5
1
1
–
5
3
2
,
D
e
c
.
2
0
2
0
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
0
9
7
3
-
0
1
9
-
0
9
0
8
9
-
5.
[
3
0
]
A
.
B
a
l
a
l
a
n
d
T
.
D
a
l
l
a
s
,
“
T
h
e
i
n
f
l
u
e
n
c
e
o
f
t
i
l
t
a
n
g
l
e
o
n
o
u
t
p
u
t
f
o
r
a
r
e
s
i
d
e
n
t
i
a
l
4
k
W
s
o
l
a
r
P
V
s
y
s
t
e
m
,
”
i
n
2
0
2
1
I
E
E
E
4
t
h
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
P
o
w
e
r
a
n
d
E
n
e
r
g
y
A
p
p
l
i
c
a
t
i
o
n
s
,
I
C
P
E
A
2
0
2
1
,
O
c
t
.
2
0
2
1
,
p
p
.
1
3
1
–
1
3
4
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
P
E
A
5
2
7
6
0
.
2
0
2
1
.
9
6
3
9
2
6
2
.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
De
b
a
n
i
Pra
sa
d
Mi
shr
a
c
u
rre
n
tl
y
se
rv
e
s
a
s
a
n
a
ss
istan
t
p
r
o
fe
ss
o
r
a
n
d
t
h
e
h
e
a
d
o
f
th
e
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
De
p
a
rt
m
e
n
t
a
t
t
h
e
I
n
tern
a
ti
o
n
a
l
In
stit
u
te o
f
I
n
fo
rm
a
ti
o
n
Tec
h
n
o
lo
g
y
Bh
u
b
a
n
e
sw
a
r,
Od
ish
a
.
He
c
o
m
p
lete
d
h
is
b
a
c
h
e
lo
r
’
s
d
e
g
re
e
in
E
lec
tri
c
a
l
En
g
in
e
e
rin
g
fro
m
Bij
u
P
a
tn
a
ik
Un
i
v
e
rsity
o
f
Tec
h
n
o
l
o
g
y
,
O
d
ish
a
in
2
0
0
6
,
f
o
ll
o
we
d
b
y
a
m
a
ste
r
’
s
d
e
g
re
e
in
P
o
we
r
S
y
ste
m
s
fro
m
IIT
De
l
h
i,
I
n
d
ia
i
n
2
0
1
0
.
I
n
2
0
1
9
,
h
e
s
u
c
c
e
ss
fu
ll
y
o
b
tain
e
d
h
is
P
h
.
D.
i
n
P
o
we
r
S
y
ste
m
s
fro
m
Ve
e
r
S
u
re
n
d
ra
S
a
i
Un
i
v
e
rsity
o
f
Tec
h
n
o
l
o
g
y
,
Od
ish
a
,
I
n
d
ia.
Wi
t
h
a
p
ro
f
o
u
n
d
a
c
a
d
e
m
ic
b
a
c
k
g
ro
u
n
d
a
n
d
e
x
ten
si
v
e
k
n
o
wle
d
g
e
o
f
p
o
we
r
sy
ste
m
s,
h
e
a
c
ti
v
e
ly
e
n
g
a
g
e
s
in
tea
c
h
in
g
a
n
d
re
se
a
rc
h
a
c
ti
v
it
ies
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
d
e
b
a
n
i@ii
it
-
b
h
.
a
c
.
i
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
1
5
,
No
.
1
,
Ma
r
ch
20
2
6
:
38
4
-
39
2
392
J
a
y
a
n
t
a
K
u
m
a
r
S
a
h
u
is
p
re
s
e
n
tl
y
wo
r
k
in
g
a
s
a
ss
istan
t
p
ro
fe
ss
o
r
i
n
De
p
a
rtme
n
t
o
f
El
e
c
tri
c
a
l
En
g
i
n
e
e
rin
g
,
NIIS
In
stit
u
te
o
f
En
g
in
e
e
rin
g
a
n
d
Tec
h
n
o
l
o
g
y
,
Bh
u
b
a
n
e
sw
a
r,
Od
ish
a
,
I
n
d
ia.
He
c
o
m
p
lete
d
h
is
P
h
.
D
.
i
n
El
e
c
tri
c
a
l
E
n
g
i
n
e
e
rin
g
fro
m
KIIT
De
e
m
e
d
to
b
e
Un
iv
e
rsity
,
Bh
u
b
a
n
e
sw
a
r,
Od
ish
a
,
In
d
ia,
in
t
h
e
field
o
f
M
P
P
T
b
a
se
d
S
o
lar
P
V
S
y
ste
m
.
He
re
c
e
iv
e
d
h
is
M
.
Tec
h
.
d
e
g
re
e
in
El
e
c
tri
c
a
l
En
g
i
n
e
e
rin
g
fro
m
C.
V.
Ra
m
a
n
Co
ll
e
g
e
o
f
En
g
i
n
e
e
rin
g
u
n
d
e
r
Bij
u
P
a
tt
a
n
a
ik
Un
i
v
e
rsity
o
f
Tec
h
n
o
l
o
g
y
i
n
2
0
1
4
.
He
c
o
m
p
lete
d
h
is
B
.
T
ech
.
d
e
g
re
e
i
n
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
ics
En
g
i
n
e
e
rin
g
fr
o
m
S
il
i
c
o
n
In
sti
tu
te
o
f
Tec
h
n
o
lo
g
y
,
Od
ish
a
;
In
d
ia.
His
re
se
a
rc
h
i
n
tere
st
in
c
lu
d
e
s
re
n
e
wa
b
le
e
n
e
rg
y
a
n
d
p
o
we
r
e
lec
tro
n
ics
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
ja
y
a
n
tajk
s
2
0
2
1
@
g
m
a
il
.
c
o
m
.
S
o
u
b
h
a
g
y
a
Ra
n
ja
n
Na
y
a
k
is
p
u
rsu
i
n
g
a
b
a
c
h
e
l
o
r
o
f
tec
h
n
o
l
o
g
y
(B.
Tec
h
.
)
i
n
t
h
e
stre
a
m
o
f
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
ics
En
g
in
e
e
rin
g
a
t
t
h
e
I
n
tern
a
ti
o
n
a
l
I
n
stit
u
te
o
f
I
n
fo
rm
a
ti
o
n
Tec
h
n
o
l
o
g
y
Bh
u
b
a
n
e
sw
a
r
(IIIT
BH),
Od
ish
a
,
I
n
d
ia
(Ba
tch
2
0
2
1
-
2
0
2
5
).
His
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
re
n
e
wa
b
le
e
n
e
rg
y
,
m
a
c
h
in
e
lea
rn
in
g
,
a
n
d
a
rti
ficia
l
in
telli
g
e
n
c
e
.
He
c
a
n
b
e
c
o
n
tac
ted
b
y
e
m
a
il
:
b
3
2
1
0
7
1
@iii
t
-
b
h
.
a
c
.
i
n
.
Anu
r
a
g
P
a
n
d
a
is
p
u
rsu
i
n
g
B.
Tec
h
.
in
t
h
e
stre
a
m
o
f
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
ics
En
g
i
n
e
e
rin
g
a
t
th
e
I
n
tern
a
ti
o
n
a
l
I
n
stit
u
te
o
f
In
f
o
rm
a
ti
o
n
Tec
h
n
o
l
o
g
y
Bh
u
b
a
n
e
sw
a
r
(IIIT
BH),
Od
ish
a
,
I
n
d
ia
(Ba
tch
2
0
2
1
-
2
0
2
5
)
.
His
re
se
a
rc
h
i
n
tere
sts
i
n
c
lu
d
e
r
e
n
e
wa
b
le
e
n
e
rg
y
,
m
a
c
h
i
n
e
lea
rn
in
g
,
a
n
d
a
rti
ficia
l
i
n
te
ll
ig
e
n
c
e
.
He
c
a
n
b
e
c
o
n
tac
ted
b
y
e
m
a
il
:
b
3
2
1
0
0
9
@
ii
it
-
b
h
.
a
c
.
i
n
.
Priy
a
n
sh
u
P
a
r
a
m
jit
D
a
sh
is
p
u
rsu
i
n
g
a
B.
Tec
h
.
i
n
th
e
stre
a
m
o
f
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
ics
En
g
in
e
e
rin
g
a
t
th
e
In
t
e
rn
a
ti
o
n
a
l
I
n
stit
u
te o
f
In
f
o
rm
a
ti
o
n
Tec
h
n
o
lo
g
y
B
h
u
b
a
n
e
sw
a
r
(IIIT
BH),
Od
ish
a
,
In
d
ia
(Ba
tch
2
0
2
1
-
2
0
2
5
).
His
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
re
n
e
wa
b
le
e
n
e
rg
y
,
m
a
c
h
in
e
lea
rn
in
g
,
a
n
d
a
rti
ficia
l
in
te
ll
i
g
e
n
c
e
.
He
c
a
n
b
e
c
o
n
tac
te
d
b
y
e
m
a
il
:
b
3
2
1
0
5
7
@i
ii
t
-
b
h
.
a
c
.
i
n
.
S
u
r
e
n
d
e
r
Re
d
d
y
S
a
lk
u
ti
re
c
e
iv
e
d
a
P
h
.
D.
d
e
g
re
e
i
n
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g
fr
o
m
th
e
In
d
ian
I
n
stit
u
te
o
f
Tec
h
n
o
l
o
g
y
,
Ne
w
De
lh
i,
I
n
d
ia,
in
2
0
1
3
.
He
wa
s
a
p
o
st
d
o
c
t
o
ra
l
re
se
a
rc
h
e
r
a
t
Ho
wa
rd
Un
iv
e
rsity
,
Was
h
i
n
g
t
o
n
,
DC,
USA,
fro
m
2
0
1
3
to
2
0
1
4
.
He
is
c
u
rre
n
tl
y
a
n
a
ss
o
c
iate
p
ro
fe
ss
o
r
a
t
t
h
e
De
p
a
rtme
n
t
o
f
Ra
il
ro
a
d
a
n
d
El
e
c
tri
c
a
l
En
g
i
n
e
e
rin
g
,
W
o
o
so
n
g
Un
iv
e
rsity
,
Da
e
jeo
n
,
S
o
u
t
h
Ko
r
e
a
.
His
c
u
rre
n
t
re
se
a
rc
h
in
tere
st
s
in
c
lu
d
e
m
a
rk
e
t
c
lea
rin
g
,
in
c
lu
d
in
g
re
n
e
wa
b
le
e
n
e
rg
y
so
u
rc
e
s,
d
e
m
a
n
d
re
sp
o
n
se
,
a
n
d
sm
a
rt
g
rid
d
e
v
e
l
o
p
m
e
n
t
wit
h
in
teg
ra
ti
o
n
o
f
win
d
a
n
d
s
o
lar
p
h
o
t
o
v
o
lt
a
ic
e
n
e
rg
y
so
u
rc
e
s.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
su
re
n
d
e
r@ws
u
.
a
c
.
k
r
.
Evaluation Warning : The document was created with Spire.PDF for Python.