I
nte
rna
t
io
na
l J
o
urna
l o
f
P
o
wer
E
lect
ro
nics
a
nd
Driv
e
S
y
s
t
em
(
I
J
P
E
DS)
Vo
l.
1
7
,
No
.
1
,
Ma
r
ch
20
2
6
,
p
p
.
7
8
6
~
7
9
6
I
SS
N:
2
0
8
8
-
8
6
9
4
,
DOI
: 1
0
.
1
1
5
9
1
/ijp
ed
s
.
v
1
7
.
i
1
.
p
p
7
8
6
-
796
786
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
p
e
d
s
.
ia
esco
r
e.
co
m
Co
mpa
ra
tive a
na
ly
sis
of multi
-
o
u
t
put
ma
chin
e lear
ning
mo
dels
for so
la
r irradia
n
ce and wi
nd spee
d f
o
recas
ting:
A
c
a
se study
in
Ta
mil Na
du, Indi
a
S
.
S
el
v
i
1
,
N
.
S
h
a
n
t
h
i
2
,
L
a
ks
hm
i
D
h
a
nd
a
p
a
ni
3
,
M
.
B
h
o
o
p
a
t
h
i
4
,
T
.
S
a
t
h
is
h
K
u
ma
r
5
,
P
.
K
a
v
i
t
h
a
6
1
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
,
P
a
n
i
m
a
l
a
r
E
n
g
i
n
e
e
r
i
n
g
C
o
l
l
e
g
e
,
C
h
e
n
n
a
i
,
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
,
S
r
i
S
a
i
r
a
m
E
n
g
i
n
e
e
r
i
n
g
C
o
l
l
e
g
e
,
C
h
e
n
n
a
i
,
I
n
d
i
a
3
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
,
A
M
ET
U
n
i
v
e
r
s
i
t
y
,
C
h
e
n
n
a
i
,
I
n
d
i
a
4
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
,
C
h
e
n
n
a
i
I
n
st
i
t
u
t
e
o
f
Te
c
h
n
o
l
o
g
y
,
C
h
e
n
n
a
i
,
I
n
d
i
a
5
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
,
S
.
A
.
En
g
i
n
e
e
r
i
n
g
C
o
l
l
e
g
e
,
C
h
e
n
n
a
i
,
I
n
d
i
a
6
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
,
R
.
M
.
K
E
n
g
i
n
e
e
r
i
n
g
C
o
l
l
e
g
e
,
C
h
e
n
n
a
i
,
I
n
d
i
a
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Sep
1
3
,
2
0
2
5
R
ev
is
ed
Oct
2
4
,
2
0
2
5
Acc
ep
ted
Dec
1
1
,
2
0
2
5
Th
e
g
ro
wi
n
g
s
h
a
re
o
f
win
d
a
n
d
so
lar
e
n
e
rg
y
h
a
s
c
re
a
ted
c
h
a
ll
e
n
g
e
s
in
e
lec
tri
c
a
l
n
e
two
rk
s,
m
a
in
l
y
d
u
e
t
o
in
term
it
ten
c
y
,
flu
c
t
u
a
ti
o
n
s,
a
n
d
u
n
c
e
rtain
ty
.
Th
e
se
issu
e
s
a
ffe
c
t
p
o
we
r
sy
ste
m
sta
b
il
it
y
,
g
rid
o
p
e
ra
ti
o
n
s,
a
n
d
th
e
b
a
lan
c
e
b
e
twe
e
n
su
p
p
l
y
a
n
d
d
e
m
a
n
d
.
To
a
d
d
re
ss
t
h
is,
a
c
c
u
ra
te
p
re
d
icti
o
n
o
f
so
lar
irr
a
d
ian
c
e
a
n
d
wi
n
d
s
p
e
e
d
is
c
rit
ica
l
fo
r
in
te
g
ra
ti
n
g
re
n
e
wa
b
le
e
n
e
rg
y
i
n
to
p
o
we
r
sy
ste
m
s.
In
th
is
st
u
d
y
,
w
e
p
ro
p
o
se
a
m
u
lt
i
-
o
u
t
p
u
t
m
a
c
h
in
e
lea
rn
in
g
a
p
p
ro
a
c
h
t
o
p
re
d
ict
b
o
t
h
g
lo
b
a
l
h
o
rizo
n
tal
irrad
ian
c
e
(G
HI)
a
n
d
w
in
d
s
p
e
e
d
sim
u
lt
a
n
e
o
u
sl
y
.
T
h
e
stu
d
y
u
se
s
h
ist
o
rica
l
m
e
teo
ro
lo
g
ica
l
d
a
ta
o
b
ta
in
e
d
fro
m
th
e
Na
ti
o
n
a
l
S
o
lar
Ra
d
iatio
n
Da
tab
a
se
(NSRDB)
fo
r
Tam
il
Na
d
u
,
In
d
ia.
S
i
x
re
g
re
ss
io
n
a
lg
o
r
it
h
m
s
:
li
n
e
a
r
re
g
re
ss
io
n
,
g
ra
d
ien
t
b
o
o
sti
n
g
,
ra
n
d
o
m
F
o
re
st,
e
x
trem
e
g
ra
d
ien
t
b
o
o
sti
n
g
(X
G
B),
li
g
h
t
g
ra
d
ie
n
t
b
o
o
sti
n
g
m
a
c
h
in
e
(Li
g
h
tG
BM
),
a
n
d
c
a
teg
o
rica
l
b
o
o
stin
g
(Ca
tB
o
o
st)
a
re
tes
ted
u
n
d
e
r
id
e
n
ti
c
a
l
c
o
n
d
i
ti
o
n
s.
M
o
d
e
l
h
y
p
e
rp
a
ra
m
e
ters
we
re
tu
n
e
d
u
sin
g
G
rid
S
e
a
rc
h
CV
a
n
d
Ba
y
e
sia
n
o
p
ti
m
iza
ti
o
n
t
o
e
n
s
u
re
ro
b
u
st
p
e
rfo
rm
a
n
c
e
.
Be
fo
re
m
o
d
e
li
n
g
,
a
c
o
m
p
re
h
e
n
siv
e
sta
ti
stica
l
a
n
a
l
y
si
s,
in
c
l
u
d
i
n
g
i
n
p
u
t
fe
a
tu
re
d
istri
b
u
ti
o
n
a
n
d
c
o
rre
latio
n
a
n
a
ly
sis,
wa
s
c
o
n
d
u
c
ted
.
M
o
d
e
l
a
c
c
u
ra
c
y
wa
s
e
v
a
l
u
a
ted
u
si
n
g
RM
S
E,
M
AE,
a
n
d
R
²
m
e
tri
c
s
o
n
b
o
th
train
i
n
g
a
n
d
tes
ti
n
g
d
a
tas
e
ts.
T
h
e
re
su
lt
s
sh
o
we
d
t
h
a
t
e
n
se
m
b
le
tree
-
b
a
se
d
m
e
th
o
d
s
o
u
tp
e
rfo
rm
e
d
th
e
b
a
se
li
n
e
li
n
e
a
r
m
o
d
e
l.
Am
o
n
g
th
e
m
,
Ca
tBo
o
st
p
ro
d
u
c
e
d
th
e
b
e
st
re
su
lt
s
f
o
r
G
HI
p
re
d
ictio
n
,
wh
il
e
ra
n
d
o
m
fo
re
st
d
e
li
v
e
re
d
th
e
m
o
st
re
li
a
b
le
win
d
sp
e
e
d
fo
re
c
a
sts,
d
e
m
o
n
stra
ti
n
g
str
o
n
g
p
re
d
ictiv
e
c
a
p
a
b
il
it
y
fo
r
re
n
e
wa
b
le
e
n
e
rg
y
a
p
p
li
c
a
ti
o
n
s.
K
ey
w
o
r
d
s
:
E
n
s
em
b
le
lear
n
in
g
Gr
ad
ien
t b
o
o
s
tin
g
Mu
lti
-
o
u
tp
u
t r
e
g
r
ess
io
n
So
lar
ir
r
ad
ian
ce
f
o
r
ec
asti
n
g
W
in
d
s
p
ee
d
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
:
S
.
Selv
i
Dep
ar
tm
en
t o
f
E
lectr
ical
an
d
E
lectr
o
n
ics E
n
g
in
ee
r
i
n
g
,
Pan
i
m
alar
E
n
g
in
ee
r
in
g
C
o
lleg
e
C
h
en
n
ai
,
T
am
il
N
ad
u
,
I
n
d
ia
E
m
ail:
s
elv
is
elv
ar
aj@
g
m
ail.
c
o
m
1.
I
NT
RO
D
UCT
I
O
N
R
esear
ch
o
n
m
u
lti
-
o
u
tp
u
t
m
a
ch
in
e
lear
n
in
g
m
o
d
els
f
o
r
s
o
lar
ir
r
ad
ian
ce
an
d
win
d
s
p
ee
d
f
o
r
ec
asti
n
g
h
as
b
ec
o
m
e
m
o
r
e
im
p
o
r
tan
t
i
n
r
ec
en
t
y
ea
r
s
.
T
h
is
is
b
ec
au
s
e
p
o
wer
g
r
id
s
ar
e
n
o
w
ta
k
in
g
in
m
o
r
e
r
en
ewa
b
le
en
er
g
y
,
an
d
it
cr
ea
tes
p
r
o
b
lem
s
lik
e
in
ter
m
itten
cy
an
d
v
ar
iab
ilit
y
th
at
m
ak
e
b
alan
cin
g
s
u
p
p
ly
an
d
d
em
an
d
h
ar
d
er
[
1
]
,
[
2
]
.
Ov
er
th
e
last
d
ec
ad
e,
th
er
e
h
as
b
ee
n
a
lo
t
o
f
p
r
o
g
r
ess
with
en
s
em
b
le
le
ar
n
in
g
m
eth
o
d
s
an
d
b
o
o
s
tin
g
alg
o
r
ith
m
s
s
u
ch
as
g
r
ad
ien
t
b
o
o
s
tin
g
r
e
g
r
ess
o
r
,
e
x
tr
em
e
g
r
a
d
ien
t
b
o
o
s
tin
g
(
X
GB
)
r
eg
r
ess
o
r
,
lig
h
t
g
r
ad
ien
t
b
o
o
s
tin
g
m
ac
h
i
n
e
(
L
ig
h
tGB
M)
,
an
d
ca
teg
o
r
ical
b
o
o
s
tin
g
(
C
atB
o
o
s
t)
.
T
h
ese
m
eth
o
d
s
h
a
v
e
s
h
o
w
n
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
C
o
mp
a
r
a
tive
a
n
a
lysi
s
o
f m
u
lti
-
o
u
tp
u
t m
a
ch
in
e
lea
r
n
i
n
g
mo
d
els fo
r
s
o
la
r
ir
r
a
d
ia
n
ce
…
(
S
.
S
elvi
)
787
b
etter
ac
cu
r
ac
y
in
p
r
ed
ictin
g
s
o
lar
an
d
win
d
e
n
er
g
y
[
3
]
,
[
4
]
.
At
th
e
s
am
e
tim
e,
th
e
g
l
o
b
al
s
o
lar
ca
p
ac
ity
g
r
ew
f
ast,
r
ea
ch
in
g
8
4
9
GW
in
2
0
2
1
,
wh
ich
s
h
o
ws
wh
y
ac
c
u
r
ate
f
o
r
ec
asti
n
g
is
n
o
w
v
er
y
p
r
ac
tical
f
o
r
g
r
id
s
tab
ilit
y
an
d
p
lan
n
in
g
[
5
]
.
Ma
ch
i
n
e
lear
n
in
g
i
s
also
b
ein
g
u
s
ed
m
o
r
e
b
ec
au
s
e
tr
ad
itio
n
al
p
h
y
s
ical
o
r
s
tatis
tical
m
o
d
els
o
f
ten
f
ail
to
ca
p
t
u
r
e
th
e
co
m
p
lex
an
d
n
o
n
-
lin
ea
r
n
atu
r
e
o
f
m
eteo
r
o
lo
g
ical
d
ata
[
6
]
,
[
7
]
.
E
v
en
with
th
ese
im
p
r
o
v
em
e
n
ts
,
th
er
e
ar
e
s
till
c
h
allen
g
es.
B
u
ild
in
g
m
o
d
els
th
at
ca
n
p
r
ed
ict
b
o
th
s
o
lar
ir
r
ad
i
an
ce
an
d
win
d
s
p
ee
d
at
th
e
s
am
e
tim
e,
an
d
d
o
it we
l
l a
cr
o
s
s
d
if
f
er
e
n
t w
ea
th
er
co
n
d
itio
n
s
,
is
n
o
t
ea
s
y
[
8
]
,
[
9
]
.
A
l
o
t o
f
ea
r
lier
s
tu
d
ies
o
n
ly
lo
o
k
at
s
in
g
le
-
o
u
tp
u
t m
o
d
els,
o
r
th
ey
u
s
e
m
o
d
els with
o
u
t f
u
ll h
y
p
e
r
p
ar
am
ete
r
tu
n
in
g
o
r
f
ea
tu
r
e
s
elec
tio
n
,
wh
ich
m
ak
es
th
em
less
r
eliab
le
[
1
0
]
–
[
1
2
]
.
B
u
t
r
esear
ch
er
s
s
till
d
o
n
’
t
r
ea
lly
ag
r
ee
o
n
wh
ic
h
b
o
o
s
tin
g
m
eth
o
d
wo
r
k
s
t
h
e
b
est.
So
m
e
p
a
p
er
s
s
ay
C
atB
o
o
s
t
is
s
tr
o
n
g
er
wh
en
th
er
e
ar
e
ca
te
g
o
r
ical
f
ea
tu
r
es,
wh
ile
o
th
er
s
f
o
u
n
d
XGB
o
r
L
ig
h
tGB
M
g
av
e
b
etter
ac
cu
r
ac
y
,
d
ep
e
n
d
in
g
o
n
th
e
d
ataset
o
r
ca
s
e
[
1
3
]
,
[
1
4
]
.
So
,
th
er
e
is
s
till
a
g
ap
wh
en
it
c
o
m
es
to
p
ick
in
g
a
n
d
tu
n
in
g
th
e
r
ig
h
t
m
u
lti
-
o
u
t
p
u
t
m
o
d
els
f
o
r
r
e
n
ewa
b
le
e
n
er
g
y
f
o
r
ec
asti
n
g
,
a
n
d
th
is
ca
n
im
p
ac
t g
r
i
d
r
eliab
ilit
y
an
d
also
th
e
o
v
er
all
c
o
s
t [
1
5
]
.
T
h
is
r
esear
ch
is
f
r
am
e
d
u
n
d
er
en
s
em
b
le
m
ac
h
in
e
lear
n
in
g
,
w
h
er
e
s
ev
er
al
b
ase
lear
n
er
s
ar
e
co
m
b
in
ed
to
im
p
r
o
v
e
p
r
ed
ictio
n
s
[
1
6
]
.
Mu
lti
-
o
u
tp
u
t
r
eg
r
ess
io
n
,
wh
ic
h
m
o
d
els
s
ev
er
al
d
ep
en
d
en
t
v
ar
iab
les
at
o
n
ce
,
a
n
d
b
o
o
s
tin
g
alg
o
r
ith
m
s
,
wh
ich
iter
ativ
ely
im
p
r
o
v
e
wea
k
lear
n
er
s
b
y
f
o
cu
s
in
g
o
n
h
ar
d
e
r
in
s
tan
ce
s
,
ar
e
th
e
k
ey
c
o
n
ce
p
ts
h
er
e
[
1
7
]
.
T
h
e
in
ter
p
l
ay
o
f
f
ea
tu
r
e
s
elec
tio
n
,
h
y
p
e
r
p
ar
am
eter
tu
n
in
g
,
a
n
d
m
o
d
el
ar
ch
itectu
r
e
f
o
r
m
s
th
e
b
ase
f
o
r
b
u
il
d
in
g
r
o
b
u
s
t
f
o
r
ec
asti
n
g
s
y
s
tem
s
th
at
ca
n
h
an
d
le
th
e
s
to
ch
asti
c
n
atu
r
e
o
f
s
o
lar
i
r
r
ad
ian
ce
an
d
win
d
s
p
ee
d
[
1
8
]
.
T
h
e
aim
o
f
th
is
p
a
p
er
is
to
cr
itically
ev
alu
ate
an
d
co
m
p
ar
e
t
h
e
g
r
ad
ien
t
b
o
o
s
ti
n
g
r
e
g
r
ess
o
r
,
XGB
r
eg
r
ess
o
r
,
lin
ea
r
r
eg
r
ess
io
n
,
L
i
g
h
tGB
M
r
eg
r
ess
o
r
,
an
d
C
atB
o
o
s
t
r
eg
r
ess
o
r
f
o
r
m
u
lti
-
o
u
tp
u
t
f
o
r
ec
asti
n
g
o
f
GHI
an
d
win
d
s
p
ee
d
.
B
y
p
u
ttin
g
t
o
g
eth
er
r
ec
en
t
f
in
d
in
g
s
,
th
is
r
ev
iew
tr
ies
to
clar
if
y
th
e
s
tr
en
g
th
s
an
d
lim
itatio
n
s
o
f
th
ese
m
o
d
els,
p
o
in
t
o
u
t
th
e
g
ap
s
in
h
y
p
er
p
a
r
am
eter
o
p
tim
i
za
tio
n
an
d
f
ea
tu
r
e
s
elec
tio
n
,
an
d
o
f
f
e
r
p
r
ac
tic
al
in
s
ig
h
ts
f
o
r
r
esear
ch
er
s
an
d
p
r
ac
titi
o
n
er
s
in
r
e
n
ewa
b
le
en
er
g
y
f
o
r
ec
asti
n
g
[
1
9
]
.
T
h
is
wo
r
k
will
co
n
tr
ib
u
te
to
im
p
r
o
v
in
g
th
e
ac
cu
r
ac
y
an
d
r
eliab
ilit
y
o
f
r
e
n
ewa
b
le
e
n
er
g
y
in
teg
r
atio
n
in
to
p
o
wer
s
y
s
tem
s
.
Un
lik
e
ea
r
lie
r
en
s
em
b
le
-
b
ased
s
tu
d
ies,
th
is
wo
r
k
f
o
c
u
s
es
o
n
a
s
in
g
le
m
u
l
ti
-
o
u
tp
u
t
m
o
d
el
th
at
p
r
e
d
icts
b
o
th
GHI
an
d
win
d
s
p
ee
d
to
g
eth
er
,
in
s
tead
o
f
b
u
il
d
in
g
s
ep
ar
ate
m
o
d
els
f
o
r
ea
ch
[
2
0
]
.
I
t
also
u
s
es
ex
p
lain
ab
le
AI
to
o
ls
lik
e
SHAP
an
d
L
I
ME
to
s
h
o
w
h
o
w
d
if
f
er
en
t
wea
th
er
f
ea
tu
r
es
in
f
lu
en
ce
th
e
p
r
ed
ictio
n
s
,
wh
ich
is
s
o
m
eth
in
g
m
an
y
ea
r
lier
s
tu
d
ies
d
id
n
o
t
in
cl
u
d
e.
B
y
c
o
m
b
in
in
g
j
o
in
t
p
r
ed
ictio
n
,
i
n
ter
p
r
etab
ilit
y
,
an
d
d
etailed
m
o
d
e
l
co
m
p
ar
is
o
n
,
th
is
r
esear
ch
ad
d
s
a
clea
r
p
r
ac
tical
im
p
r
o
v
em
e
n
t a
n
d
o
r
ig
i
n
ality
to
r
en
ewa
b
le
e
n
er
g
y
f
o
r
ec
asti
n
g
.
2.
M
E
T
H
O
DO
L
O
G
Y
T
h
e
v
ar
io
u
s
s
tep
s
in
v
o
lv
ed
ar
e
s
h
o
wn
in
Fig
u
r
e
1.
T
h
e
d
ata
s
et
f
o
r
th
is
s
tu
d
y
was
o
b
tain
ed
f
r
o
m
t
h
e
NSR
D
B
,
m
an
ag
ed
b
y
th
e
Nat
io
n
al
R
en
ewa
b
le
E
n
er
g
y
L
ab
o
r
ato
r
y
(
NR
E
L
)
,
USA.
Fo
r
th
is
wo
r
k
,
r
ec
o
r
d
s
f
o
r
2
0
2
0
wer
e
ex
tr
ac
te
d
f
o
r
th
e
s
tate
o
f
T
am
il
Nad
u
,
I
n
d
ia,
co
v
er
in
g
g
eo
g
r
ap
h
ic
co
o
r
d
in
ates b
etwe
en
8
°0
4
′N
an
d
1
3
°3
5
′N
latitu
d
e
an
d
7
6
°1
4
′E
an
d
8
0
°2
1
′E
l
o
n
g
itu
d
e.
T
h
e
d
ataset
p
r
o
v
id
ed
h
o
u
r
ly
m
ea
s
u
r
em
en
ts
o
f
GHI
an
d
win
d
s
p
ee
d
,
alo
n
g
with
s
u
p
p
o
r
tin
g
m
ete
o
r
o
lo
g
ical
p
ar
am
e
ter
s
s
u
ch
as
air
tem
p
er
atu
r
e,
r
elativ
e
h
u
m
i
d
ity
,
p
r
ess
u
r
e,
an
d
clo
u
d
c
o
v
er
.
T
h
ese
p
ar
am
eter
s
s
er
v
ed
as
th
e
i
n
p
u
t
s
et
f
o
r
m
o
d
el
tr
ain
in
g
an
d
p
r
e
d
ictio
n
task
s
.
Fig
u
r
e
2
s
h
o
ws
th
e
m
o
n
th
ly
av
er
ag
es
o
f
GHI
an
d
win
d
s
p
ee
d
in
T
am
il
Nad
u
f
o
r
2
0
2
0
.
GHI
p
ea
k
s
d
u
r
in
g
th
e
s
u
m
m
er
m
o
n
th
s
(
Ma
r
ch
–
Ma
y
)
,
wh
ile
win
d
s
p
ee
d
r
is
es
in
th
e
m
o
n
s
o
o
n
s
ea
s
o
n
(
J
u
n
e
–
Sep
te
m
b
er
)
.
I
n
ter
esti
n
g
ly
,
th
e
two
r
eso
u
r
ce
s
f
o
llo
w
o
p
p
o
s
ite
r
h
y
th
m
s
.
Fig
u
r
e
1.
Me
th
o
d
o
lo
g
y
2
.
1
.
Da
t
a
pre
-
pro
ce
s
s
ing
T
o
k
ee
p
t
h
e
d
ataset
co
n
s
is
ten
t
an
d
r
eliab
le,
we
a
p
p
lied
s
ev
er
al
p
r
e
-
p
r
o
ce
s
s
in
g
s
tep
s
.
Sh
o
r
t
g
ap
s
in
th
e
d
ata
wer
e
f
illed
u
s
in
g
lin
ea
r
in
ter
p
o
latio
n
,
wh
ile
lo
n
g
er
m
is
s
in
g
in
ter
v
als
wer
e
lef
t
o
u
t
o
f
th
e
a
n
aly
s
is
.
T
o
ex
p
lo
r
e
th
e
r
elatio
n
s
h
i
p
b
etwe
en
in
p
u
t
f
ea
tu
r
es
an
d
th
e
tar
g
et
v
ar
iab
les
(
GHI
an
d
win
d
s
p
ee
d
)
,
a
co
r
r
elatio
n
h
ea
t
m
ap
was
p
lo
tted
an
d
s
h
o
wn
in
Fig
u
r
e
3
.
T
h
is
f
ig
u
r
e
s
h
o
ws
th
e
lin
ea
r
co
r
r
elatio
n
am
o
n
g
th
e
v
a
r
iab
les
in
th
e
d
ataset.
Mo
s
t
o
f
th
e
in
p
u
t
f
ea
tu
r
es
o
n
l
y
h
a
v
e
wea
k
o
r
m
o
d
er
ate
c
o
n
n
ec
tio
n
s
wi
th
o
n
e
an
o
th
er
.
T
h
e
GHI
r
is
es
with
tem
p
er
atu
r
e
(
0
.
7
3
)
b
u
t
f
alls
with
r
elativ
e
h
u
m
id
ity
an
d
s
o
lar
ze
n
ith
a
n
g
le.
W
in
d
s
p
ee
d
h
as
a
p
o
s
itiv
e
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.
1
7
,
No
.
1
,
Ma
r
c
h
20
2
6
:
786
-
796
788
co
r
r
elatio
n
with
b
o
th
tem
p
er
atu
r
e
an
d
GHI
;
o
n
t
h
e
o
th
e
r
h
an
d
,
it
h
as
a
n
eg
ativ
e
co
r
r
elatio
n
with
r
elativ
e
h
u
m
id
ity
a
n
d
s
o
lar
ze
n
ith
an
g
le.
T
h
e
tim
e
-
b
ased
f
ea
tu
r
es
,
s
u
ch
as
h
o
u
r
an
d
d
ay
o
f
t
h
e
y
ea
r
,
d
em
o
n
s
tr
ate
h
ig
h
co
r
r
elatio
n
with
s
o
lar
ze
n
ith
a
n
g
le,
wh
ich
is
ex
p
ec
ted
s
in
ce
t
h
ey
ca
p
tu
r
e
d
aily
a
n
d
s
ea
s
o
n
al
p
atter
n
s
.
Fig
u
r
e
2.
T
h
e
m
o
n
th
ly
a
v
er
ag
es o
f
GHI
an
d
win
d
s
p
ee
d
Fig
u
r
e
3
.
C
o
r
r
elatio
n
h
ea
tm
a
p
Ov
er
all,
tem
p
er
atu
r
e,
r
elativ
e
h
u
m
id
ity
,
an
d
s
o
lar
ze
n
ith
an
g
le
ar
e
th
e
m
o
s
t
in
f
lu
en
tial
v
ar
iab
les
f
o
r
GHI
,
wh
er
ea
s
tem
p
er
atu
r
e
an
d
h
u
m
id
ity
in
f
l
u
en
ce
win
d
s
p
ee
d
.
Ou
tlier
s
in
GHI
an
d
win
d
s
p
ee
d
wer
e
d
etec
ted
u
s
in
g
b
o
t
h
z
-
s
co
r
e
th
r
esh
o
ld
s
an
d
in
ter
q
u
ar
tile
r
a
n
g
e
(
I
QR
)
an
aly
s
is
,
en
s
u
r
in
g
th
at
ex
tr
em
e
an
o
m
alies
d
id
n
o
t
s
k
ew
th
e
m
o
d
els.
I
n
ad
d
itio
n
,
we
also
cr
ea
ted
tim
e
-
b
ased
f
ea
tu
r
es su
ch
as
h
o
u
r
o
f
d
a
y
,
d
ay
o
f
y
ea
r
,
a
n
d
m
o
n
th
to
ca
p
tu
r
e
d
aily
an
d
s
ea
s
o
n
al
v
ar
iatio
n
s
in
s
o
lar
an
d
win
d
p
atter
n
s
.
Fin
ally
,
all
n
u
m
e
r
ical
f
ea
tu
r
es
wer
e
s
tan
d
ar
d
ized
u
s
in
g
z
-
s
co
r
e
n
o
r
m
aliza
tio
n
,
en
s
u
r
in
g
th
at
d
if
f
er
en
ce
s
in
f
ea
tu
r
e
s
ca
le
d
id
n
o
t
af
f
ec
t
m
o
d
el
p
er
f
o
r
m
an
ce
.
Fig
u
r
e
4
s
h
o
ws
th
e
s
tatis
tical
an
aly
s
is
o
f
th
e
in
p
u
t f
ea
tu
r
es.
T
h
e
f
ea
tu
r
e
d
is
tr
i
b
u
tio
n
p
lo
ts
s
h
o
wn
in
Fig
u
r
e
4
wer
e
a
n
aly
ze
d
t
o
e
x
am
in
e
th
e
s
tatis
tical
p
r
o
p
e
r
ties
o
f
in
p
u
t
v
ar
iab
les.
So
m
e
v
ar
i
ab
les
,
s
u
ch
as
GHI
,
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
C
o
mp
a
r
a
tive
a
n
a
lysi
s
o
f m
u
lti
-
o
u
tp
u
t m
a
ch
in
e
lea
r
n
i
n
g
mo
d
els fo
r
s
o
la
r
ir
r
a
d
ia
n
ce
…
(
S
.
S
elvi
)
789
d
is
p
lay
ed
Sk
ewe
d
d
is
tr
ib
u
tio
n
s
an
d
wer
e
n
o
r
m
alize
d
to
s
ta
b
ilize
v
ar
ian
ce
.
W
h
ile
cy
clica
l
f
ea
tu
r
es
lik
e
h
o
u
r
an
d
m
o
n
t
h
wer
e
tr
an
s
f
o
r
m
ed
i
n
to
s
in
e
an
d
co
s
in
e
co
m
p
o
n
e
n
ts
to
p
r
eser
v
e
p
er
io
d
icity
.
T
h
e
s
e
tr
an
s
f
o
r
m
atio
n
s
allo
wed
th
e
m
o
d
els to
ca
p
tu
r
e
b
o
th
d
i
u
r
n
al
an
d
s
ea
s
o
n
al
v
ar
i
atio
n
s
.
Fig
u
r
e
4
.
Statis
tical
an
aly
s
is
o
f
th
e
in
p
u
t
f
ea
tu
r
es
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.
1
7
,
No
.
1
,
Ma
r
c
h
20
2
6
:
786
-
796
790
2
.
2
.
P
r
o
blem
f
o
rm
ula
t
io
n
a
nd
m
o
del selec
t
io
n
T
h
e
f
o
r
e
c
a
s
t
i
n
g
t
as
k
w
a
s
f
r
a
m
ed
a
s
a
m
u
lt
i
-
o
u
t
p
u
t
r
e
g
r
es
s
i
o
n
p
r
o
b
l
e
m
,
w
h
e
r
e
b
o
t
h
G
H
I
a
n
d
w
i
n
d
s
p
ee
d
w
e
r
e
p
r
e
d
i
c
t
e
d
s
i
m
u
l
t
a
n
e
o
u
s
l
y
.
F
r
a
m
i
n
g
i
t
t
h
is
w
a
y
al
l
o
w
e
d
t
h
e
m
o
d
e
l
s
t
o
c
a
p
t
u
r
e
p
o
t
e
n
ti
a
l
co
r
r
e
l
a
t
i
o
n
s
b
e
tw
e
e
n
s
o
l
a
r
i
r
r
a
d
ia
n
c
e
a
n
d
w
i
n
d
s
p
e
ed
,
w
h
i
c
h
f
r
e
q
u
e
n
t
l
y
c
o
e
x
is
t
i
n
r
e
n
e
w
a
b
l
e
e
n
e
r
g
y
s
y
s
t
e
m
s
.
S
ix
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
m
o
d
e
l
s
we
r
e
e
v
al
u
a
t
e
d
.
T
h
is
s
tu
d
y
c
o
v
e
r
s
b
o
t
h
a
s
i
m
p
l
e
b
as
e
l
in
e
m
o
d
e
l
a
n
d
a
d
v
a
n
c
e
d
e
n
s
e
m
b
l
e
m
e
t
h
o
d
s
.
L
i
n
e
ar
r
e
g
r
e
s
s
i
o
n
(
L
R
)
w
a
s
c
h
o
s
e
n
a
s
a
b
a
s
e
l
i
n
e
m
o
d
e
l
,
a
s
it
a
s
s
u
m
e
s
a
l
i
n
e
a
r
r
e
l
a
t
i
o
n
s
h
i
p
b
e
t
w
e
e
n
i
n
p
u
t
a
n
d
o
u
t
p
u
t
s
a
n
d
t
h
u
s
p
r
o
v
i
d
e
s
a
p
o
i
n
t
o
f
c
o
m
p
a
r
i
s
o
n
f
o
r
m
o
r
e
c
o
m
p
l
e
x
m
o
d
e
l
s
.
T
o
c
a
p
t
u
r
e
n
o
n
-
l
i
n
ea
r
d
e
p
e
n
d
e
n
c
i
e
s
,
e
n
s
e
m
b
l
e
a
p
p
r
o
a
c
h
e
s
w
e
r
e
e
m
p
l
o
y
e
d
.
R
an
d
o
m
f
o
r
e
s
t
(
R
F
)
c
o
n
s
t
r
u
ct
s
m
u
l
t
i
p
l
e
d
e
c
is
i
o
n
t
r
e
es
o
n
r
a
n
d
o
m
s
u
b
s
e
ts
o
f
d
a
t
a
a
n
d
f
e
a
t
u
r
e
s
,
t
h
e
n
a
v
e
r
a
g
es
t
h
ei
r
p
r
e
d
i
c
t
i
o
n
s
,
m
a
k
i
n
g
i
t
r
o
b
u
s
t
t
o
n
o
i
s
e
a
n
d
e
f
f
e
c
ti
v
e
f
o
r
n
o
n
-
l
i
n
e
ar
p
a
t
t
e
r
n
s
.
B
o
o
s
ti
n
g
m
e
t
h
o
d
s
we
r
e
al
s
o
i
n
c
l
u
d
e
d
.
G
r
a
d
i
e
n
t
b
o
o
s
t
i
n
g
r
e
g
r
es
s
o
r
(
G
B
R
)
b
u
il
d
s
m
o
d
el
s
s
te
p
b
y
s
t
e
p
,
w
h
e
r
e
e
a
c
h
n
ew
m
o
d
e
l
f
i
x
e
s
t
h
e
m
i
s
t
a
k
e
s
o
f
t
h
e
e
a
r
l
i
e
r
o
n
e
,
w
h
i
c
h
h
e
l
p
s
i
m
p
r
o
v
e
a
c
c
u
r
a
c
y
.
E
x
t
r
e
m
e
g
r
a
d
i
e
n
t
b
o
o
s
t
i
n
g
(
X
GB
o
o
s
t)
i
s
a
f
a
s
t
e
r
a
n
d
s
t
r
o
n
g
e
r
v
e
r
s
i
o
n
.
I
t
p
r
e
v
e
n
t
s
o
v
e
r
f
i
t
ti
n
g
u
s
i
n
g
r
e
g
u
l
a
r
i
z
a
ti
o
n
,
w
o
r
k
s
w
el
l
w
i
th
m
i
s
s
i
n
g
d
a
ta
,
a
n
d
t
r
a
i
n
s
f
a
s
te
r
b
y
u
s
i
n
g
p
a
r
al
l
el
p
r
o
c
e
s
s
i
n
g
.
L
i
g
h
t
g
r
a
d
i
e
n
t
b
o
o
s
t
i
n
g
m
a
c
h
i
n
e
(
L
i
g
h
tG
B
M
)
u
s
e
s
a
s
p
e
ci
a
l
w
a
y
o
f
g
r
o
w
i
n
g
t
r
e
e
s
t
h
a
t
m
a
k
es
it
b
o
th
f
a
s
t
a
n
d
m
e
m
o
r
y
-
e
f
f
i
c
ie
n
t
,
esp
e
c
i
a
l
l
y
w
h
e
n
t
h
e
d
a
t
a
h
a
s
m
an
y
f
e
a
t
u
r
e
s
.
Fi
n
a
l
l
y
,
c
a
t
e
g
o
r
i
c
a
l
b
o
o
s
t
i
n
g
(
C
a
tB
o
o
s
t)
w
a
s
s
el
e
c
t
e
d
b
ec
a
u
s
e
i
t
c
a
n
h
a
n
d
l
e
c
a
t
e
g
o
r
i
c
al
f
e
at
u
r
e
s
d
i
r
e
c
t
l
y
wi
t
h
o
u
t
h
e
a
v
y
p
r
e
-
p
r
o
c
e
s
s
i
n
g
,
w
h
il
e
a
ls
o
c
o
n
tr
o
l
l
i
n
g
o
v
e
r
f
i
t
t
i
n
g
a
n
d
g
i
v
i
n
g
a
r
e
l
i
a
b
l
e
p
r
e
d
i
ct
i
o
n
.
2
.
3
.
E
v
a
lua
t
i
o
n o
f
m
a
chine le
a
rning
m
o
dels
T
h
e
d
ataset
was
s
p
lit
in
to
two
p
ar
ts
,
wh
er
e
7
0
%
was
u
s
ed
f
o
r
tr
ain
in
g
t
h
e
m
o
d
el
a
n
d
th
e
r
em
ain
in
g
3
0
% f
o
r
v
alid
atio
n
.
T
h
e
v
alid
a
tio
n
p
ar
t h
el
p
s
th
e
m
o
d
el
test
d
if
f
er
en
t
h
y
p
er
p
ar
am
eter
s
etti
n
g
s
f
r
o
m
t
h
e
Scik
it
-
lear
n
lib
r
ar
y
b
e
f
o
r
e
f
in
alizin
g
th
e
b
est
co
n
f
ig
u
r
a
tio
n
.
T
e
m
p
o
r
al
h
o
l
d
o
u
t
v
alid
atio
n
was
p
er
f
o
r
m
ed
,
wh
e
r
e
tr
ain
in
g
u
s
ed
J
an
–
Oct
d
ata
an
d
test
in
g
co
v
e
r
ed
N
o
v
–
Dec
2
0
2
0
t
o
en
s
u
r
e
tem
p
o
r
al
g
e
n
er
aliza
tio
n
.
So
m
e
o
f
th
ese
m
o
d
els
ca
n
n
o
t
d
ir
ec
tly
p
r
ed
ict
m
u
ltip
le
o
u
tp
u
ts
,
s
o
we
u
s
ed
th
e
m
u
lti
-
o
u
tp
u
t
r
eg
r
e
s
s
o
r
wr
ap
p
er
wh
en
n
ec
ess
ar
y
.
T
o
im
p
r
o
v
e
g
e
n
er
aliza
tio
n
an
d
f
air
co
m
p
ar
is
o
n
,
h
y
p
er
p
a
r
am
eter
tu
n
in
g
was
p
er
f
o
r
m
ed
u
s
in
g
a
co
m
b
in
atio
n
o
f
G
r
id
Sear
ch
C
V
an
d
B
ay
esian
o
p
tim
izatio
n
,
with
cr
o
s
s
-
v
alid
atio
n
.
Hy
p
er
p
ar
am
eter
tu
n
in
g
was
p
er
f
o
r
m
ed
u
s
in
g
1
0
-
f
o
ld
c
r
o
s
s
-
v
alid
atio
n
with
b
o
th
Gr
id
Sear
ch
C
V
an
d
B
ay
esian
o
p
tim
izatio
n
.
2
.
4
.
P
er
f
o
r
m
a
nce
m
e
t
rics
T
h
e
p
r
ed
ictiv
e
ac
cu
r
ac
y
o
f
th
e
m
ac
h
in
e
lear
n
in
g
m
o
d
els
w
as
ev
alu
ated
u
s
in
g
v
ar
io
u
s
p
er
f
o
r
m
a
n
ce
m
etr
ics
[
2
1
]
,
wh
ich
in
d
icate
h
o
w
well
th
e
p
r
ed
icted
o
u
t
p
u
ts
co
r
r
esp
o
n
d
to
t
h
e
o
b
s
er
v
ed
v
alu
es
d
er
iv
ed
f
r
o
m
th
e
in
p
u
t
f
ea
tu
r
es
[
2
2
]
.
T
h
e
co
ef
f
icien
t
o
f
d
eter
m
in
atio
n
(
R
²)
r
ep
r
esen
ts
th
e
p
r
o
p
o
r
tio
n
o
f
v
ar
ian
c
e
in
th
e
d
ep
en
d
e
n
t
v
ar
ia
b
le
ex
p
lain
e
d
b
y
th
e
i
n
d
ep
e
n
d
en
t
v
ar
iab
les
[
2
3
]
.
Valu
es
a
p
p
r
o
a
ch
in
g
1
s
ig
n
if
y
a
h
ig
h
er
lev
el
o
f
ag
r
ee
m
en
t
b
etwe
en
th
e
p
r
e
d
icted
an
d
ac
tu
al
r
esu
lts
.
In
(
1
)
g
iv
es
th
e
f
o
r
m
u
la
f
o
r
ca
lcu
latin
g
th
e
R
²
v
alu
e
[
2
4
]
.
T
h
e
MA
E
ev
alu
ates
th
e
av
er
ag
e
d
ev
iatio
n
o
f
p
r
ed
ictio
n
s
f
r
o
m
ac
tu
al
v
alu
es
[
2
4
]
.
T
h
e
f
o
r
m
u
la
f
o
r
MA
E
is
g
iv
en
b
y
(
2
)
.
T
h
e
n
ex
t
m
etr
i
c
,
R
MSE
,
i
s
g
iv
en
in
(
3
)
[
2
5
]
.
I
t
s
q
u
ar
es
th
e
d
if
f
er
en
ce
s
b
ef
o
r
e
av
er
ag
in
g
,
wh
ich
m
o
r
e
s
ev
er
ely
p
en
alize
s
h
ig
h
er
er
r
o
r
s
[
2
5
]
.
B
ec
au
s
e
o
f
th
i
s
,
R
MSE
is
m
o
r
e
s
en
s
itiv
e
to
o
u
tlier
s
.
A
s
m
aller
R
MSE
r
ef
lects
b
etter
p
r
ed
ictio
n
p
e
r
f
o
r
m
an
ce
[
2
5
]
.
C
o
llectiv
ely
,
th
ese
m
etr
ics
o
f
f
er
a
d
et
ailed
ass
ess
m
en
t
o
f
m
o
d
el
ef
f
ec
tiv
e
n
ess
an
d
allo
w
f
o
r
m
ea
n
in
g
f
u
l c
o
m
p
ar
is
o
n
s
a
cr
o
s
s
d
if
f
er
en
t a
lg
o
r
ith
m
s
an
d
d
atasets
[
2
5
]
.
2
=
1
−
∑
(
−
(
̂
)
=
1
∑
(
−
̅
)
=
1
(
1
)
=
1
∑
⌈
−
̂
⌉
=
1
(
2
)
=
1
√
∑
(
−
̂
)
2
(
3
)
3.
SH
AP
B
AS
E
D
E
VAL
UAT
I
O
N
T
h
e
Sh
ap
ley
ad
d
itiv
e
ex
p
lan
at
io
n
s
(
SHAP)
f
r
am
ewo
r
k
,
p
r
o
p
o
s
ed
b
y
L
u
n
d
b
er
g
an
d
L
e
e
[
2
0
]
,
ad
ap
ts
co
o
p
er
ativ
e
g
am
e
th
e
o
r
y
an
d
i
s
n
o
w
wid
el
y
u
s
ed
to
in
ter
p
r
et
m
ac
h
in
e
lear
n
i
n
g
m
o
d
els
[
2
1
]
.
I
n
th
is
p
lo
t,
t
h
e
x
-
ax
is
r
ep
r
esen
ts
SHAP
v
alu
es,
th
e
y
-
a
x
is
lis
ts
f
ea
tu
r
es
in
o
r
d
e
r
o
f
im
p
o
r
tan
ce
,
an
d
a
c
o
lo
r
g
r
a
d
ien
t
in
d
icate
s
th
e
u
n
d
er
ly
i
n
g
f
ea
tu
r
e
v
alu
es.
T
o
g
ain
in
s
ig
h
ts
in
to
h
o
w
d
if
f
e
r
en
t
f
ea
tu
r
es
co
n
tr
ib
u
te
to
th
e
m
o
d
el,
SHAP
an
aly
s
i
s
was a
p
p
lied
to
th
e
b
est
-
p
e
r
f
o
r
m
in
g
m
o
d
el
f
o
r
GHI
an
d
win
d
s
p
ee
d
p
r
ed
ictio
n
.
Fo
r
th
e
GHI
p
r
ed
ictio
n
,
th
e
C
atB
o
o
s
t
m
o
d
el
g
av
e
th
e
b
est
ac
cu
r
ac
y
.
T
h
e
SHAP
r
esu
lt
s
s
h
o
wn
in
Fig
u
r
es
5
(
a)
an
d
5
(
b
)
s
h
o
w
th
at
s
o
lar
ze
n
ith
an
g
le
is
th
e
m
o
s
t
im
p
o
r
tan
t
f
ea
tu
r
e,
f
o
llo
wed
b
y
d
ew
p
o
in
t
a
n
d
r
elativ
e
h
u
m
id
ity
.
W
in
d
d
ir
ec
tio
n
,
p
r
ess
u
r
e
,
a
n
d
tem
p
er
at
u
r
e
h
ad
m
u
ch
less
co
n
tr
ib
u
tio
n
i
n
co
m
p
ar
is
o
n
.
T
h
e
b
est
p
er
f
o
r
m
in
g
m
o
d
el
f
o
r
win
d
s
p
ee
d
p
r
e
d
ictio
n
is
r
an
d
o
m
f
o
r
est
,
an
d
th
e
SHAP
s
u
m
m
ar
y
p
lo
t
is
s
h
o
wn
i
n
Fig
u
r
e
s
6
(
a)
an
d
6
(
b
)
.
T
h
e
r
esu
lt
s
h
o
ws
th
at
s
o
lar
ze
n
ith
an
g
le
an
d
r
elativ
e
h
u
m
id
ity
ar
e
th
e
m
o
s
t
i
n
f
lu
en
tial
p
r
ed
icto
r
s
,
f
o
llo
we
d
b
y
win
d
d
ir
ec
tio
n
an
d
d
ew
p
o
in
t
.
Pre
s
s
u
r
e
an
d
tem
p
er
at
u
r
e
h
a
d
a
r
elativ
ely
s
m
aller
ef
f
ec
t.
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
C
o
mp
a
r
a
tive
a
n
a
lysi
s
o
f m
u
lti
-
o
u
tp
u
t m
a
ch
in
e
lea
r
n
i
n
g
mo
d
els fo
r
s
o
la
r
ir
r
a
d
ia
n
ce
…
(
S
.
S
elvi
)
791
3
.
1
.
L
o
ca
l int
er
pret
a
ble
m
o
del
-
a
g
no
s
t
ic
ex
pla
na
t
io
ns
(
L
I
M
E
)
Fig
u
r
es
7
(
a)
an
d
7
(
b
)
s
h
o
w
th
e
L
I
ME
l
f
o
r
GHI
an
d
win
d
s
p
ee
d
p
r
ed
ictio
n
s
.
Fo
r
th
e
GHI
m
o
d
el
u
s
in
g
C
atB
o
o
s
t,
th
e
p
r
ed
icted
v
alu
e
was
3
.
9
2
with
in
a
p
o
s
s
ib
le
r
an
g
e
o
f
0
.
2
6
to
5
.
8
6
.
So
lar
ze
n
it
h
an
g
le
with
a
v
alu
e
o
f
1
1
5
.
2
4
h
a
d
th
e
s
tr
o
n
g
est n
e
g
ativ
e
im
p
ac
t o
n
th
e
p
r
e
d
ictio
n
.
Fo
r
th
e
win
d
s
p
ee
d
m
o
d
el
u
s
in
g
r
an
d
o
m
f
o
r
est,
th
e
p
r
e
d
icted
v
al
u
e
was
1
.
1
5
in
a
r
a
n
g
e
f
r
o
m
0
.
5
6
to
5
.
1
1
.
T
h
ese
r
esu
lts
ex
p
lain
h
o
w
d
if
f
er
en
t
atm
o
s
p
h
er
ic
v
ar
iab
les af
f
ec
t th
e
lo
ca
l
p
r
ed
i
ctio
n
s
o
f
b
o
t
h
GHI
an
d
win
d
s
p
ee
d
m
o
d
els.
(
a)
(
b
)
Fig
u
r
e
5
.
SHAP
-
b
ased
f
ea
tu
r
e
im
p
o
r
tan
ce
a
n
aly
s
is
f
o
r
g
l
o
b
a
l h
o
r
izo
n
tal
ir
r
ad
ian
ce
(
GHI
)
:
(
a)
ab
s
o
lu
te
m
ea
n
SHAP v
alu
es
an
d
(
b
)
g
lo
b
al
SHAP v
alu
e
s
(
a)
(
b
)
Fig
u
r
e
6
.
SHAP
-
b
ased
f
ea
tu
r
e
im
p
o
r
tan
ce
a
n
aly
s
is
f
o
r
win
d
s
p
ee
d
:
(
a)
ab
s
o
lu
te
m
ea
n
an
d
(
b
)
g
lo
b
al
SHAP v
alu
es
(
a)
(
b
)
Fig
u
r
e
7
.
L
I
ME
ex
p
la
n
atio
n
:
(
a)
f
o
r
GHI
(
C
atB
o
o
s
t)
an
d
(
b
)
f
o
r
win
d
s
p
ee
d
(
r
a
n
d
o
m
f
o
r
est
)
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.
1
7
,
No
.
1
,
Ma
r
c
h
20
2
6
:
786
-
796
792
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
4
.
1
.
P
er
f
o
r
m
a
nce
T
h
is
s
tu
d
y
e
v
alu
ated
s
ix
m
ac
h
in
e
lear
n
in
g
m
o
d
els
u
n
d
er
a
m
u
lti
-
o
u
tp
u
t
r
eg
r
ess
io
n
f
r
a
m
ewo
r
k
,
wh
er
e
b
o
th
GHI
an
d
win
d
s
p
ee
d
wer
e
f
o
r
ec
asted
s
im
u
ltan
eo
u
s
ly
.
T
h
e
r
esu
lts
s
h
o
wn
in
T
ab
le
1
r
ev
ea
l
th
at
en
s
em
b
le
tr
ee
-
b
ased
m
et
h
o
d
s
c
o
n
s
is
ten
tly
o
u
t
p
er
f
o
r
m
ed
th
e
b
aselin
e
l
in
ea
r
m
o
d
el.
Fo
r
GHI
f
o
r
ec
asti
n
g
,
C
atB
o
o
s
t
g
av
e
th
e
s
tr
o
n
g
est
p
er
f
o
r
m
a
n
ce
,
ac
h
iev
in
g
th
e
lo
west
n
o
r
m
alize
d
R
MSE
(
3
1
.
5
%)
an
d
n
MA
E
(
1
6
.
1
%),
alo
n
g
with
th
e
h
ig
h
est R
²
(
0
.
9
4
6
)
.
L
ig
h
tG
B
M
an
d
XGBo
o
s
t a
l
s
o
p
er
f
o
r
m
ed
well,
b
o
th
with
R
²
v
alu
es a
b
o
v
e
0
.
9
4
,
th
o
u
g
h
s
lig
h
tly
b
eh
in
d
C
atB
o
o
s
t.
B
y
co
n
tr
ast,
lin
ea
r
r
e
g
r
ess
io
n
s
h
o
w
ed
p
o
o
r
p
e
r
f
o
r
m
an
ce
,
with
a
n
n
R
MSE
ex
ce
ed
in
g
6
3
%,
co
n
f
ir
m
in
g
its
in
ab
ilit
y
to
ca
p
tu
r
e
th
e
n
o
n
lin
ea
r
n
atu
r
e
o
f
GHI
d
ata.
Fo
r
win
d
s
p
ee
d
p
r
ed
ictio
n
,
r
an
d
o
m
f
o
r
est
d
eliv
er
e
d
th
e
m
o
s
t
r
eliab
le
ac
cu
r
ac
y
,
with
an
n
R
MSE
o
f
2
9
.
2
%
a
n
d
R
²
o
f
0
.
6
7
.
B
o
o
s
tin
g
m
o
d
els
s
u
ch
as
XGBo
o
s
t
an
d
L
ig
h
tGB
M
ac
h
iev
ed
co
m
p
ar
a
b
le
r
esu
lts
,
b
u
t
th
e
r
an
d
o
m
f
o
r
est
p
r
o
v
ed
m
o
r
e
s
tab
le
f
o
r
th
is
v
ar
iab
le.
T
h
is
in
d
icate
s
th
at
wh
ile
a
s
in
g
le
m
u
lti
-
o
u
tp
u
t
f
r
am
ewo
r
k
was
u
s
ed
,
d
if
f
e
r
en
t
m
o
d
els
ca
p
tu
r
ed
p
atter
n
s
b
etter
f
o
r
d
if
f
er
en
t
tar
g
ets:
C
atB
o
o
s
t
ex
ce
lled
f
o
r
G
HI
,
wh
er
ea
s
r
an
d
o
m
f
o
r
est
wa
s
m
o
r
e
s
u
itab
le
f
o
r
win
d
s
p
ee
d
.
T
ab
le
1
.
Per
f
o
r
m
an
ce
m
etr
ics o
f
th
e
ML
m
o
d
el
M
o
d
e
l
G
H
I
m
e
t
r
i
c
s
W
i
n
d
s
p
e
e
d
me
t
r
i
c
s
n
R
M
S
E
(
%)
n
M
A
E
(
%)
R²
n
R
M
S
E
(
%)
n
M
A
E
(
%)
R²
Li
n
e
a
r
r
e
g
r
e
s
si
o
n
6
3
.
8
3
5
2
.
6
3
0
.
7
7
9
4
1
.
0
9
3
2
.
4
3
0
.
3
5
5
R
a
n
d
o
m
f
o
r
e
s
t
3
2
.
4
7
1
5
.
8
7
0
.
9
4
3
2
9
.
2
3
2
1
.
2
4
0
.
6
7
3
G
r
a
d
i
e
n
t
b
o
o
s
t
i
n
g
3
2
.
4
4
1
6
.
1
3
0
.
9
4
3
3
0
.
7
8
2
3
.
2
4
0
.
6
3
8
X
G
B
o
o
st
3
2
.
4
3
1
6
.
5
8
0
.
9
4
3
3
0
.
6
2
2
2
.
9
7
0
.
6
4
2
Li
g
h
t
G
B
M
3
1
.
9
3
1
6
.
3
2
0
.
9
4
5
3
0
.
1
4
2
2
.
5
6
0
.
6
5
3
C
a
t
B
o
o
s
t
3
1
.
5
8
1
6
.
1
6
0
.
9
4
6
3
1
.
3
2
2
2
.
6
3
0
.
6
2
5
4
.
2
.
T
em
po
r
a
l
v
a
lid
a
t
io
n
a
nd
m
o
del st
a
bil
it
y
T
o
v
e
r
if
y
t
h
e
tem
p
o
r
al
r
o
b
u
s
tn
ess
o
f
m
o
d
els,
a
tim
e
-
awa
r
e
v
alid
atio
n
s
tr
ateg
y
u
s
in
g
tim
e
s
er
ies
s
p
lit
was
ad
o
p
ted
.
Fig
u
r
e
8
p
r
esen
ts
th
e
R
MSE
o
b
tain
ed
ac
r
o
s
s
f
iv
e
r
o
llin
g
tim
e
-
b
ased
f
o
ld
s
f
o
r
b
o
th
GHI
an
d
win
d
s
p
ee
d
.
Ho
u
r
ly
er
r
o
r
an
aly
s
is
is
s
h
o
wn
in
Fig
u
r
e
9
.
R
ev
ea
led
h
ig
h
er
p
r
e
d
ictio
n
er
r
o
r
s
d
u
r
in
g
ea
r
ly
m
o
r
n
in
g
an
d
late
ev
en
in
g
,
an
d
m
in
im
al
d
e
v
i
atio
n
s
ar
o
u
n
d
m
id
d
ay
.
W
in
d
s
p
ee
d
s
h
o
wed
s
im
ilar
d
iu
r
n
al
tr
en
d
s
,
with
m
o
d
er
ate
er
r
o
r
s
d
u
r
in
g
tr
an
s
itio
n
p
er
io
d
s
.
Ov
er
all,
th
e
m
o
d
els
p
er
f
o
r
m
ed
m
o
s
t
co
n
s
is
ten
tly
d
u
r
in
g
s
tab
le
m
id
d
a
y
co
n
d
itio
n
s
.
Fig
u
r
e
8
.
T
e
m
p
o
r
al
v
alid
atio
n
r
esu
lts
s
h
o
win
g
R
MSE
ac
r
o
s
s
f
iv
e
r
o
llin
g
tim
e
-
b
ased
f
o
ld
s
4
.
3
.
Ro
llin
g
-
o
rig
in f
o
re
ca
s
t
a
nd
un
ce
rt
a
inty
beha
v
io
r
A
r
o
llin
g
-
o
r
ig
in
v
alid
atio
n
te
s
t
was
co
n
d
u
cted
to
ass
ess
m
o
d
el
ad
ap
ta
b
ilit
y
to
s
eq
u
e
n
tial
d
ata
an
d
f
o
r
ec
ast
u
n
ce
r
tain
ty
.
As
s
h
o
wn
in
Fig
u
r
e
1
0
,
th
e
ac
tu
al
a
n
d
p
r
ed
icted
GHI
v
alu
e
f
o
llo
ws
s
im
ilar
tem
p
o
r
al
tr
en
d
s
with
o
n
ly
m
in
im
al
lag
.
T
h
e
s
h
ad
ed
r
eg
io
n
s
(
±
1
0
%)
r
ep
r
esen
t
an
ap
p
r
o
x
im
ate
u
n
ce
r
tain
ty
b
an
d
,
in
d
icatin
g
th
e
m
o
d
el’
s
ex
p
ec
te
d
p
r
e
d
ictio
n
v
ar
iab
ilit
y
.
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
C
o
mp
a
r
a
tive
a
n
a
lysi
s
o
f m
u
lti
-
o
u
tp
u
t m
a
ch
in
e
lea
r
n
i
n
g
mo
d
els fo
r
s
o
la
r
ir
r
a
d
ia
n
ce
…
(
S
.
S
elvi
)
793
Fig
u
r
e
9
.
Ho
u
r
ly
a
b
s
o
lu
te
er
r
o
r
d
is
tr
ib
u
tio
n
f
o
r
GHI
a
n
d
win
d
s
p
ee
d
Fig
u
r
e
1
0
.
R
o
llin
g
-
o
r
i
g
in
GHI
f
o
r
ec
ast co
m
p
a
r
in
g
ac
t
u
al
an
d
p
r
ed
icted
v
alu
es with
±
1
0
% u
n
ce
r
tain
ty
b
an
d
5.
CO
NCLU
SI
O
N
I
n
s
u
m
m
a
r
y
,
t
h
e
s
tu
d
y
d
em
o
n
s
tr
ated
th
at
C
atB
o
o
s
t
is
th
e
m
o
s
t
ef
f
ec
tiv
e
m
o
d
el
f
o
r
GHI
p
r
ed
ictio
n
,
wh
ile
r
an
d
o
m
f
o
r
est
wo
r
k
s
b
est
f
o
r
win
d
s
p
ee
d
f
o
r
ec
asts
.
T
h
ese
f
in
d
in
g
s
h
ig
h
lig
h
t
th
e
v
alu
e
o
f
en
s
em
b
le
ap
p
r
o
ac
h
es
f
o
r
r
en
ewa
b
le
en
e
r
g
y
f
o
r
ec
asti
n
g
,
esp
ec
ially
wh
en
co
m
p
ar
e
d
to
s
im
p
le
lin
ea
r
m
o
d
els.
T
h
e
u
s
e
o
f
SHAP
an
d
L
I
ME
p
r
o
v
i
d
ed
ex
t
r
a
tr
an
s
p
ar
en
c
y
,
s
h
o
win
g
w
h
ich
f
ea
tu
r
es
in
f
lu
e
n
ce
p
r
e
d
ictio
n
s
th
e
m
o
s
t.
Desp
ite
a
f
ew
lim
itatio
n
s
,
th
e
wo
r
k
co
n
f
ir
m
s
th
at
m
ac
h
in
e
lear
n
i
n
g
m
o
d
els ca
n
p
r
o
v
id
e
ac
cu
r
ate
f
o
r
ec
asts
o
f
s
o
lar
an
d
win
d
en
er
g
y
,
o
f
f
er
i
n
g
u
s
ef
u
l
i
n
s
ig
h
ts
f
o
r
g
r
id
o
p
er
ato
r
s
an
d
p
lan
n
er
s
to
m
an
ag
e
r
en
ewa
b
le
en
er
g
y
in
teg
r
atio
n
m
o
r
e
e
f
f
ec
tiv
ely
.
Alth
o
u
g
h
th
e
m
o
d
els
p
er
f
o
r
m
ed
well
o
v
er
all,
s
o
m
e
lim
itatio
n
s
s
h
o
u
ld
b
e
n
o
ted
.
T
h
e
d
ataset
was
r
estricte
d
to
a
s
in
g
le
y
ea
r
(
2
0
2
0
)
f
r
o
m
T
am
il
Nad
u
,
w
h
ich
m
ay
lim
it
g
e
n
er
aliza
tio
n
to
o
th
e
r
r
eg
io
n
s
o
r
clim
atic
co
n
d
itio
n
s
.
On
ly
s
tan
d
ar
d
m
eteo
r
o
lo
g
ical
v
ar
iab
les
wer
e
co
n
s
id
er
ed
;
in
cl
u
d
in
g
ad
d
itio
n
al
p
r
ed
icto
r
s
s
u
ch
as c
lo
u
d
c
o
v
er
o
r
ae
r
o
s
o
l
d
ata
c
o
u
ld
en
h
a
n
ce
ac
c
u
r
ac
y
.
T
h
e
d
ata
s
et
was a
ls
o
lim
ited
t
o
h
o
u
r
ly
r
eso
lu
tio
n
,
wh
ile
g
r
id
o
p
er
ato
r
s
o
f
te
n
r
eq
u
ir
e
f
in
er
in
ter
v
als
(
e.
g
.
,
1
0
–
1
5
m
in
u
tes).
Fin
ally
,
s
in
ce
th
e
m
o
d
els
wer
e
v
alid
ated
o
n
o
n
e
d
ataset,
th
eir
tr
an
s
f
e
r
a
b
ilit
y
to
o
th
er
clim
ates sti
ll n
ee
d
s
f
u
r
th
er
test
in
g
.
Fo
r
f
u
tu
r
e
wo
r
k
,
th
is
f
r
am
ew
o
r
k
ca
n
b
e
e
x
ten
d
ed
t
o
in
co
r
p
o
r
ate
d
ee
p
lear
n
i
n
g
ar
ch
itectu
r
es
s
u
ch
as
L
STM
,
C
NN,
o
r
h
y
b
r
id
p
h
y
s
ic
s
ML
m
o
d
els
m
ay
ca
p
tu
r
e
lo
n
g
er
tem
p
o
r
al
d
ep
e
n
d
en
cies
an
d
s
p
atial
co
r
r
elatio
n
s
m
o
r
e
ef
f
ec
tiv
ely
.
A
d
d
itio
n
all
y
,
p
r
o
b
ab
ilis
tic
o
r
q
u
a
n
tile
r
e
g
r
ess
io
n
m
eth
o
d
s
co
u
ld
h
elp
q
u
an
tify
p
r
e
d
ictio
n
u
n
ce
r
tain
ty
an
d
im
p
r
o
v
e
f
o
r
e
ca
s
t
r
eliab
ilit
y
u
n
d
er
f
lu
ctu
atin
g
m
eteo
r
o
lo
g
ical
co
n
d
itio
n
s
.
Fin
ally
,
alig
n
in
g
f
o
r
ec
asti
n
g
p
r
ac
tices
with
in
ter
n
atio
n
al
s
tan
d
ar
d
s
,
s
u
ch
as
I
E
C
6
1
7
2
4
(
PV
p
er
f
o
r
m
an
c
e
m
o
n
ito
r
in
g
)
,
I
SO
1
4
0
6
4
(
GHG
r
ep
o
r
tin
g
)
,
an
d
I
E
E
E
2
0
3
0
.
9
(
r
en
ewa
b
le
f
o
r
ec
asti
n
g
g
u
id
elin
es)
will
en
s
u
r
e
th
at
th
e
p
r
o
p
o
s
ed
m
eth
o
d
o
l
o
g
y
r
em
ain
s
co
m
p
atib
le
with
g
lo
b
al
e
n
er
g
y
f
o
r
ec
as
tin
g
an
d
g
r
id
c
o
m
p
lian
ce
f
r
am
ewo
r
k
s
.
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.
1
7
,
No
.
1
,
Ma
r
c
h
20
2
6
:
786
-
796
794
F
UNDING
I
NF
O
R
M
A
T
I
O
N
No
f
u
n
d
in
g
r
ec
ei
v
ed
.
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
t
r
ib
u
to
r
R
o
les
T
a
x
o
n
o
m
y
(
C
R
ed
iT)
to
r
ec
o
g
n
ize
in
d
iv
i
d
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
S
.
Selv
i
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
N
.
Sh
an
t
h
i
✓
✓
✓
✓
✓
✓
✓
L
ak
s
h
m
i D
h
an
d
a
p
an
i
✓
✓
✓
✓
✓
✓
✓
✓
M.
B
h
o
o
p
ath
i
✓
✓
✓
✓
✓
✓
T.
Satis
h
Ku
m
ar
✓
✓
✓
✓
✓
✓
✓
✓
P
.
Kav
ith
a
✓
✓
✓
✓
✓
✓
✓
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
T
h
e
au
th
o
r
s
d
ec
lar
e
th
at
th
e
y
h
av
e
n
o
co
n
f
licts
o
f
in
ter
est.
I
NF
O
RM
E
D
CO
NS
E
N
T
T
h
e
au
th
o
r
s
co
n
f
ir
m
th
at
in
f
o
r
m
ed
co
n
s
en
t w
as o
b
tai
n
ed
f
r
o
m
all
in
d
iv
id
u
als in
clu
d
ed
in
th
is
wo
r
k
.
DATA AV
AI
L
AB
I
L
I
T
Y
T
h
e
au
th
o
r
s
co
n
f
ir
m
th
at
th
e
d
ata
s
u
p
p
o
r
tin
g
th
is
s
tu
d
y
'
s
f
in
d
in
g
s
ar
e
av
ailab
le
with
in
th
e
a
r
ticle.
RF
E
RE
NCE
S
[
1
]
T
.
C
h
a
u
d
h
a
r
i
,
A
.
Jo
sh
i
,
D
.
B
h
a
r
t
i
,
P
.
C
h
a
v
a
n
,
M
.
K
.
D
e
s
h
mu
k
h
,
a
n
d
P
.
B
h
a
l
g
a
t
,
“
A
c
o
mp
a
r
a
t
i
v
e
s
t
u
d
y
o
f
e
n
se
mb
l
e
l
e
a
r
n
i
n
g
m
o
d
e
l
s
f
o
r
a
c
c
u
r
a
t
e
s
o
l
a
r
i
r
r
a
d
i
a
n
c
e
f
o
r
e
c
a
st
i
n
g
,
”
i
n
8
t
h
I
EE
E
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
C
o
m
p
u
t
a
t
i
o
n
a
l
S
y
st
e
m
a
n
d
I
n
f
o
rm
a
t
i
o
n
T
e
c
h
n
o
l
o
g
y
f
o
r
S
u
st
a
i
n
a
b
l
e
S
o
l
u
t
i
o
n
s
,
C
S
I
T
S
S
2
0
2
4
,
2
0
2
4
d
o
i
:
1
0
.
1
1
0
9
/
C
S
I
T
S
S
6
4
0
4
2
.
2
0
2
4
.
1
0
8
1
6
8
1
7
.
[
2
]
M
.
S
.
A
l
a
m
,
F
.
S
.
A
l
-
I
s
m
a
i
l
,
M
.
S
.
H
o
s
sa
i
n
,
a
n
d
S
.
M
.
R
a
h
m
a
n
,
“
En
s
e
mb
l
e
ma
c
h
i
n
e
-
l
e
a
r
n
i
n
g
mo
d
e
l
s
f
o
r
a
c
c
u
r
a
t
e
p
r
e
d
i
c
t
i
o
n
o
f
s
o
l
a
r
i
r
r
a
d
i
a
t
i
o
n
i
n
B
a
n
g
l
a
d
e
sh
,
”
P
ro
c
e
ss
e
s
,
v
o
l
.
1
1
,
n
o
.
3
,
2
0
2
3
,
d
o
i
:
1
0
.
3
3
9
0
/
p
r
1
1
0
3
0
9
0
8
.
[
3
]
M
.
A
b
u
mo
h
sen
,
A
.
Y
.
O
w
d
a
,
M
.
O
w
d
a
,
A
.
A
b
u
mi
h
s
a
n
,
a
n
d
L.
S
t
e
r
g
i
o
u
l
a
s
,
“
F
o
r
e
c
a
st
i
n
g
so
l
a
r
p
o
w
e
r
g
e
n
e
r
a
t
i
o
n
u
s
i
n
g
e
x
t
r
e
m
e
g
r
a
d
i
e
n
t
b
o
o
st
i
n
g
:
A
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
a
p
p
r
o
a
c
h
,
”
i
n
I
C
S
I
N
T
ES
A
2
0
2
4
-
2
0
2
4
4
t
h
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
f
S
c
i
e
n
c
e
a
n
d
I
n
f
o
rm
a
t
i
o
n
T
e
c
h
n
o
l
o
g
y
i
n
S
m
a
rt
A
d
m
i
n
i
s
t
ra
t
i
o
n
:
T
h
e
C
o
l
l
a
b
o
ra
t
i
o
n
o
f
S
m
a
rt
T
e
c
h
n
o
l
o
g
y
a
n
d
G
o
o
d
G
o
v
e
rn
a
n
c
e
f
o
r
S
u
st
a
i
n
a
b
l
e
D
e
v
e
l
o
p
m
e
n
t
G
o
a
l
s
,
2
0
2
4
,
p
p
.
5
0
5
–
5
1
0
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
S
I
N
T
E
S
A
6
2
4
5
5
.
2
0
2
4
.
1
0
7
4
8
0
3
0
.
[
4
]
X
.
K
.
A
b
b
e
y
a
n
d
A
.
A
.
A
d
e
b
i
y
i
,
“
P
r
e
d
i
c
t
i
v
e
mo
d
e
l
l
i
n
g
o
f
r
e
n
e
w
a
b
l
e
e
n
e
r
g
y
so
u
r
c
e
s
:
A
c
o
m
p
r
e
h
e
n
si
v
e
a
n
a
l
y
s
i
s
u
si
n
g
g
r
a
d
i
e
n
t
b
o
o
s
t
e
d
d
e
c
i
si
o
n
t
r
e
e
s
,
”
i
n
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
ren
c
e
o
n
S
c
i
e
n
c
e
,
En
g
i
n
e
e
ri
n
g
a
n
d
Bu
si
n
e
s
s
f
o
r
D
ri
v
i
n
g
S
u
st
a
i
n
a
b
l
e
D
e
v
e
l
o
p
m
e
n
t
G
o
a
l
s,
S
EB4
S
D
G
2
0
2
4
,
2
0
2
4
d
o
i
:
1
0
.
1
1
0
9
/
S
EB
4
S
D
G
6
0
8
7
1
.
2
0
2
4
.
1
0
6
2
9
7
4
3
.
[
5
]
K
.
B
l
a
z
a
k
i
s
,
Y
.
K
a
t
si
g
i
a
n
n
i
s,
a
n
d
G
.
S
t
a
v
r
a
k
a
k
i
s,
“
O
n
e
-
d
a
y
-
a
h
e
a
d
so
l
a
r
i
r
r
a
d
i
a
t
i
o
n
a
n
d
w
i
n
d
sp
e
e
d
f
o
r
e
c
a
st
i
n
g
w
i
t
h
a
d
v
a
n
c
e
d
d
e
e
p
l
e
a
r
n
i
n
g
t
e
c
h
n
i
q
u
e
s
,
”
En
e
rg
i
e
s
,
v
o
l
.
1
5
,
n
o
.
1
2
,
2
0
2
2
,
d
o
i
:
1
0
.
3
3
9
0
/
e
n
1
5
1
2
4
3
6
1
.
[
6
]
L
.
Zj
a
v
k
a
,
“
W
i
n
d
sp
e
e
d
a
n
d
g
l
o
b
a
l
r
a
d
i
a
t
i
o
n
f
o
r
e
c
a
st
i
n
g
b
a
sed
o
n
d
i
f
f
e
r
e
n
t
i
a
l
,
d
e
e
p
,
a
n
d
s
t
o
c
h
a
st
i
c
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
o
f
p
a
t
t
e
r
n
s
i
n
2
-
l
e
v
e
l
h
i
s
t
o
r
i
c
a
l
me
t
e
o
-
q
u
a
n
t
i
t
y
set
s
,
”
C
o
m
p
l
e
x
a
n
d
I
n
t
e
l
l
i
g
e
n
t
S
y
s
t
e
m
s
,
v
o
l
.
9
,
n
o
.
4
,
p
p
.
3
8
7
1
–
3
8
8
5
,
2
0
2
3
,
d
o
i
:
1
0
.
1
0
0
7
/
s4
0
7
4
7
-
022
-
0
0
8
7
9
-
3.
[
7
]
Y
.
A
mo
u
r
a
,
S
.
To
r
r
e
s
,
J.
Li
ma
,
a
n
d
A
.
I
.
P
e
r
e
i
r
a
,
“
S
o
l
a
r
i
r
r
a
d
i
a
t
i
o
n
a
n
d
w
i
n
d
sp
e
e
d
f
o
r
e
c
a
s
t
i
n
g
b
a
sed
o
n
r
e
g
r
e
s
si
o
n
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
mo
d
e
l
s
,
”
L
e
c
t
u
re
N
o
t
e
s
i
n
N
e
t
w
o
r
k
s
a
n
d
S
y
st
e
m
s
,
v
o
l
.
6
4
9
L
N
N
S
,
p
p
.
3
1
–
5
1
,
2
0
2
3
,
d
o
i
:
1
0
.
1
0
0
7
/
9
7
8
-
3
-
031
-
2
7
4
9
9
-
2
_
4
.
[
8
]
Y
.
A
mo
u
r
a
,
S
.
T
o
r
r
e
s,
J.
L
i
m
a
,
a
n
d
A
.
I
.
P
e
r
e
i
r
a
,
“
C
o
m
b
i
n
e
d
O
p
t
i
mi
z
a
t
i
o
n
a
n
d
R
e
g
r
e
s
si
o
n
M
a
c
h
i
n
e
L
e
a
r
n
i
n
g
f
o
r
S
o
l
a
r
I
r
r
a
d
i
a
t
i
o
n
a
n
d
W
i
n
d
S
p
e
e
d
F
o
r
e
c
a
st
i
n
g
,
”
C
o
m
m
u
n
i
c
a
t
i
o
n
s
i
n
C
o
m
p
u
t
e
r
a
n
d
I
n
f
o
r
m
a
t
i
o
n
S
c
i
e
n
c
e
,
v
o
l
.
1
7
5
4
C
C
I
S
,
p
p
.
2
1
5
–
2
2
8
,
2
0
2
2
,
d
o
i
:
1
0
.
1
0
0
7
/
9
7
8
-
3
-
031
-
2
3
2
3
6
-
7
_
1
6
.
[
9
]
F
.
D
.
V
i
d
a
l
B
e
z
e
r
r
a
,
F
.
P
i
n
t
o
M
a
r
i
n
h
o
,
P
.
A
.
C
o
s
t
a
R
o
c
h
a
,
V
.
O
l
i
v
e
i
r
a
S
a
n
t
o
s,
J
.
V
a
n
G
r
i
e
n
s
v
e
n
Th
é
,
a
n
d
B
.
G
h
a
r
a
b
a
g
h
i
,
“
M
a
c
h
i
n
e
l
e
a
r
n
i
n
g
d
y
n
a
mi
c
e
n
s
e
mb
l
e
m
e
t
h
o
d
s
f
o
r
so
l
a
r
i
r
r
a
d
i
a
n
c
e
a
n
d
w
i
n
d
sp
e
e
d
p
r
e
d
i
c
t
i
o
n
s
,
”
A
t
m
o
sp
h
e
r
e
,
v
o
l
.
1
4
,
n
o
.
1
1
,
2
0
2
3
,
d
o
i
:
1
0
.
3
3
9
0
/
a
t
mo
s1
4
1
1
1
6
3
5
.
[
1
0
]
U
.
A
h
med
,
A
.
M
a
h
m
o
o
d
,
M
.
A
.
Tu
n
i
o
,
G
.
H
a
f
e
e
z
,
A
.
R
.
K
h
a
n
,
a
n
d
S
.
R
a
z
z
a
q
,
“
I
n
v
e
s
t
i
g
a
t
i
n
g
b
o
o
s
t
i
n
g
t
e
c
h
n
i
q
u
e
s
’
e
f
f
i
c
a
c
y
i
n
f
e
a
t
u
r
e
s
e
l
e
c
t
i
o
n
:
A
c
o
m
p
a
r
a
t
i
v
e
a
n
a
l
y
s
i
s
,
”
E
n
e
rg
y
R
e
p
o
rt
s
,
v
o
l
.
1
1
,
p
p
.
3
5
2
1
–
3
5
3
2
,
2
0
2
4
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
g
y
r
.
2
0
2
4
.
0
3
.
0
2
0
.
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
C
o
mp
a
r
a
tive
a
n
a
lysi
s
o
f m
u
lti
-
o
u
tp
u
t m
a
ch
in
e
lea
r
n
i
n
g
mo
d
els fo
r
s
o
la
r
ir
r
a
d
ia
n
ce
…
(
S
.
S
elvi
)
795
[
1
1
]
K
.
N
a
m
r
a
t
a
,
M
.
K
u
ma
r
,
a
n
d
N
.
K
u
m
a
r
,
“
D
a
t
a
-
d
r
i
v
e
n
h
y
p
e
r
p
a
r
a
m
e
t
e
r
o
p
t
i
mi
z
e
d
e
x
t
r
e
me
g
r
a
d
i
e
n
t
b
o
o
st
i
n
g
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
m
o
d
e
l
f
o
r
so
l
a
r
r
a
d
i
a
t
i
o
n
f
o
r
e
c
a
st
i
n
g
,”
Ad
v
a
n
c
e
s
i
n
El
e
c
t
ri
c
a
l
a
n
d
E
l
e
c
t
ro
n
i
c
En
g
i
n
e
e
ri
n
g
,
v
o
l
.
2
0
,
n
o
.
4
,
F
e
b
.
2
0
2
3
,
d
o
i
:
1
0
.
1
5
5
9
8
/
a
e
e
e
.
v
2
0
i
4
.
4
6
5
0
.
[
1
2
]
J
.
R
.
A
n
d
r
a
d
e
a
n
d
R
.
J
.
B
e
s
sa
,
“
I
m
p
r
o
v
i
n
g
r
e
n
e
w
a
b
l
e
e
n
e
r
g
y
f
o
r
e
c
a
s
t
i
n
g
w
i
t
h
a
g
r
i
d
o
f
n
u
me
r
i
c
a
l
w
e
a
t
h
e
r
p
r
e
d
i
c
t
i
o
n
s
,
”
I
E
E
E
T
r
a
n
sa
c
t
i
o
n
s
o
n
S
u
st
a
i
n
a
b
l
e
En
e
r
g
y
,
v
o
l
.
8
,
n
o
.
4
,
p
p
.
1
5
7
1
–
1
5
8
0
,
2
0
1
7
,
d
o
i
:
1
0
.
1
1
0
9
/
T
S
T
E
.
2
0
1
7
.
2
6
9
4
3
4
0
.
[
1
3
]
H
.
N
.
N
g
u
y
e
n
,
Q
.
T
.
T
r
a
n
,
C
.
T.
N
g
o
,
D
.
D
.
N
g
u
y
e
n
,
a
n
d
V
.
Q
.
T
r
a
n
,
“
S
o
l
a
r
e
n
e
r
g
y
p
r
e
d
i
c
t
i
o
n
t
h
r
o
u
g
h
mac
h
i
n
e
l
e
a
r
n
i
n
g
mo
d
e
l
s:
A
c
o
mp
a
r
a
t
i
v
e
a
n
a
l
y
si
s
o
f
r
e
g
r
e
s
so
r
a
l
g
o
r
i
t
h
m
s,”
PL
o
S
O
N
E
,
v
o
l
.
2
0
,
n
o
.
1
,
2
0
2
5
,
d
o
i
:
1
0
.
1
3
7
1
/
j
o
u
r
n
a
l
.
p
o
n
e
.
0
3
1
5
9
5
5
.
[
1
4
]
S
.
S
o
l
e
y
m
a
n
i
a
n
d
S
.
M
o
h
a
mm
a
d
z
a
d
e
h
,
“
C
o
mp
a
r
a
t
i
v
e
a
n
a
l
y
si
s
o
f
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
a
l
g
o
r
i
t
h
ms
f
o
r
so
l
a
r
i
r
r
a
d
i
a
n
c
e
f
o
r
e
c
a
st
i
n
g
i
n
s
ma
r
t
g
r
i
d
s
,
”
a
rX
i
v
p
rep
ri
n
t
,
2
0
2
3
,
d
o
i
:
1
0
.
4
8
5
5
0
/
a
r
X
i
v
.
2
3
1
0
.
1
3
7
9
1
.
[
1
5
]
H
.
K
i
m,
S
.
P
a
r
k
,
a
n
d
S
.
K
i
m
,
“
S
o
l
a
r
r
a
d
i
a
t
i
o
n
f
o
r
e
c
a
st
i
n
g
u
si
n
g
b
o
o
s
t
i
n
g
d
e
c
i
si
o
n
t
r
e
e
a
n
d
r
e
c
u
r
r
e
n
t
n
e
u
r
a
l
n
e
t
w
o
r
k
s,
”
C
o
m
m
u
n
i
c
a
t
i
o
n
s
f
o
r
S
t
a
t
i
st
i
c
a
l
Ap
p
l
i
c
a
t
i
o
n
s
a
n
d
Me
t
h
o
d
s
,
v
o
l
.
2
9
,
n
o
.
6
,
p
p
.
7
0
9
–
7
1
9
,
2
0
2
2
,
d
o
i
:
1
0
.
2
9
2
2
0
/
C
S
A
M
.
2
0
2
2
.
2
9
.
6
.
7
0
9
.
[
1
6
]
R
.
G
u
p
t
a
,
A
.
K
.
Y
a
d
a
v
,
S
.
K
.
Jh
a
,
a
n
d
P
.
K
.
P
a
t
h
a
k
,
“
A
r
o
b
u
s
t
r
e
g
r
e
s
so
r
m
o
d
e
l
f
o
r
e
s
t
i
mat
i
n
g
s
o
l
a
r
r
a
d
i
a
t
i
o
n
u
si
n
g
a
n
e
n
se
mb
l
e
s
t
a
c
k
i
n
g
a
p
p
r
o
a
c
h
b
a
s
e
d
o
n
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
,
”
I
n
t
e
rn
a
t
i
o
n
a
l
J
o
u
rn
a
l
o
f
G
r
e
e
n
En
e
rg
y
,
v
o
l
.
2
1
,
n
o
.
8
,
p
p
.
1
8
5
3
–
1
8
7
3
,
2
0
2
4
,
d
o
i
:
1
0
.
1
0
8
0
/
1
5
4
3
5
0
7
5
.
2
0
2
3
.
2
2
7
6
1
5
2
.
[
1
7
]
E
.
S
.
S
o
l
a
n
o
,
P
.
D
e
h
g
h
a
n
i
a
n
,
a
n
d
C
.
M
.
A
f
f
o
n
so
,
“
S
o
l
a
r
r
a
d
i
a
t
i
o
n
f
o
r
e
c
a
s
t
i
n
g
u
si
n
g
mac
h
i
n
e
l
e
a
r
n
i
n
g
a
n
d
e
n
se
mb
l
e
f
e
a
t
u
r
e
s
e
l
e
c
t
i
o
n
,
”
E
n
e
rg
i
e
s
,
v
o
l
.
1
5
,
n
o
.
1
9
,
2
0
2
2
,
d
o
i
:
1
0
.
3
3
9
0
/
e
n
1
5
1
9
7
0
4
9
.
[
1
8
]
S
.
S
y
a
ma
,
J.
R
a
mp
r
a
b
h
a
k
a
r
,
R
.
A
n
a
n
d
,
V
.
P
.
M
e
e
n
a
,
a
n
d
J.
M
.
G
u
e
r
r
e
r
o
,
“
A
n
o
v
e
l
h
y
b
r
i
d
m
e
t
h
o
d
o
l
o
g
y
f
o
r
w
i
n
d
sp
e
e
d
a
n
d
s
o
l
a
r
i
r
r
a
d
i
a
n
c
e
f
o
r
e
c
a
s
t
i
n
g
b
a
s
e
d
o
n
i
mp
r
o
v
e
d
w
h
a
l
e
o
p
t
i
mi
z
e
d
r
e
g
u
l
a
r
i
z
e
d
e
x
t
r
e
me
l
e
a
r
n
i
n
g
mac
h
i
n
e
,
”
S
c
i
e
n
t
i
f
i
c
R
e
p
o
rt
s
,
v
o
l
.
1
4
,
n
o
.
1
,
2
0
2
4
,
d
o
i
:
1
0
.
1
0
3
8
/
s4
1
5
9
8
-
0
2
4
-
83836
-
z.
[
1
9
]
X
.
D
a
i
a
n
d
Y
.
Y
a
n
g
,
“
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
a
n
d
S
H
A
P
i
n
t
e
r
p
r
e
t
a
b
i
l
i
t
y
a
n
a
l
y
si
s
b
a
s
e
d
o
n
mu
l
t
i
-
m
o
d
e
l
c
o
mp
a
r
a
t
i
v
e
l
e
a
r
n
i
n
g
,
”
H
i
g
h
l
i
g
h
t
s
i
n
S
c
i
e
n
c
e
,
En
g
i
n
e
e
r
i
n
g
a
n
d
T
e
c
h
n
o
l
o
g
y
,
v
o
l
.
1
4
2
,
p
p
.
4
3
9
–
4
5
0
,
M
a
y
2
0
2
5
,
d
o
i
:
1
0
.
5
4
0
9
7
/
k
t
r
7
g
n
5
3
.
[
2
0
]
S
.
M
.
L
u
n
d
b
e
r
g
a
n
d
S
.
-
I
.
L
e
e
,
“
A
u
n
i
f
i
e
d
a
p
p
r
o
a
c
h
t
o
i
n
t
e
r
p
r
e
t
i
n
g
mo
d
e
l
p
r
e
d
i
c
t
i
o
n
s
,
”
i
n
P
ro
c
e
e
d
i
n
g
s
o
f
t
h
e
3
1
st
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
N
e
u
ra
l
I
n
f
o
r
m
a
t
i
o
n
Pr
o
c
e
ss
i
n
g
S
y
st
e
m
s
(
N
I
PS
)
,
2
0
1
7
,
p
p
.
4
7
6
8
–
4
7
7
7
.
[
2
1
]
D
.
El
-
S
h
a
h
a
t
,
A
.
To
l
b
a
,
M
.
A
b
o
u
h
a
w
w
a
sh
,
a
n
d
M
.
A
b
d
e
l
-
B
a
s
s
e
t
,
“
M
a
c
h
i
n
e
l
e
a
r
n
i
n
g
a
n
d
d
e
e
p
l
e
a
r
n
i
n
g
mo
d
e
l
s
b
a
s
e
d
g
r
i
d
se
a
r
c
h
c
r
o
s
s
-
v
a
l
i
d
a
t
i
o
n
f
o
r
sh
o
r
t
-
t
e
r
m
so
l
a
r
i
r
r
a
d
i
a
n
c
e
f
o
r
e
c
a
st
i
n
g
,
”
J
o
u
rn
a
l
o
f
B
i
g
D
a
t
a
,
v
o
l
.
1
1
,
n
o
.
1
,
2
0
2
4
,
d
o
i
:
1
0
.
1
1
8
6
/
s4
0
5
3
7
-
0
2
4
-
00991
-
w.
[
2
2
]
A
.
V
e
r
d
o
n
e
,
M
.
P
a
n
e
l
l
a
,
E.
D
e
S
a
n
t
i
s
,
a
n
d
A
.
R
i
z
z
i
,
“
A
r
e
v
i
e
w
o
f
so
l
a
r
a
n
d
w
i
n
d
e
n
e
r
g
y
f
o
r
e
c
a
s
t
i
n
g
:
F
r
o
m
si
n
g
l
e
-
si
t
e
t
o
mu
l
t
i
-
s
i
t
e
p
a
r
a
d
i
g
m,
”
Ap
p
l
i
e
d
En
e
rg
y
,
v
o
l
.
3
9
2
,
2
0
2
5
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
a
p
e
n
e
r
g
y
.
2
0
2
5
.
1
2
6
0
1
6
.
[
2
3
]
R
.
M
.
R
i
z
k
-
A
l
l
a
h
,
L
.
M
.
A
b
o
u
e
l
mag
d
,
A
.
D
a
r
w
i
sh
,
V
.
S
n
a
s
e
l
,
a
n
d
A
.
E.
H
a
s
sa
n
i
e
n
,
“
Ex
p
l
a
i
n
a
b
l
e
A
I
a
n
d
o
p
t
i
m
i
z
e
d
so
l
a
r
p
o
w
e
r
g
e
n
e
r
a
t
i
o
n
f
o
r
e
c
a
st
i
n
g
mo
d
e
l
b
a
s
e
d
o
n
e
n
v
i
r
o
n
men
t
a
l
c
o
n
d
i
t
i
o
n
s,
”
P
L
o
S
O
N
E
,
v
o
l
.
1
9
,
n
o
.
1
0
,
2
0
2
4
,
d
o
i
:
1
0
.
1
3
7
1
/
j
o
u
r
n
a
l
.
p
o
n
e
.
0
3
0
8
0
0
2
.
[
2
4
]
E
.
S
.
S
o
l
a
n
o
a
n
d
C
.
M
.
A
f
f
o
n
so
,
“
S
o
l
a
r
i
r
r
a
d
i
a
t
i
o
n
f
o
r
e
c
a
s
t
i
n
g
u
si
n
g
e
n
sem
b
l
e
v
o
t
i
n
g
b
a
sed
o
n
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
a
l
g
o
r
i
t
h
m
s
,
”
S
u
st
a
i
n
a
b
i
l
i
t
y
(
S
w
i
t
ze
rl
a
n
d
)
,
v
o
l
.
1
5
,
n
o
.
1
0
,
2
0
2
3
,
d
o
i
:
1
0
.
3
3
9
0
/
su
1
5
1
0
7
9
4
3
.
[
2
5
]
S
.
S
e
l
v
i
,
M
.
S
.
V
e
e
r
r
a
j
u
,
K
.
V
.
S
a
n
d
e
e
p
,
B
.
K
o
n
d
u
r
i
,
a
n
d
D
.
V
e
t
r
i
t
h
a
n
g
a
m
,
“
E
n
h
a
n
c
i
n
g
s
h
o
r
t
-
t
e
r
m
P
V
p
o
w
e
r
f
o
r
e
c
a
s
t
i
n
g
u
s
i
n
g
d
e
e
p
l
e
a
r
n
i
n
g
mo
d
e
l
s
:
A
c
o
mp
a
r
a
t
i
v
e
s
t
u
d
y
o
f
D
N
N
a
n
d
C
N
N
a
p
p
r
o
a
c
h
e
s
,”
S
S
R
G
I
n
t
e
rn
a
t
i
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
t
r
i
c
a
l
a
n
d
E
l
e
c
t
ro
n
i
c
s
En
g
i
n
e
e
r
i
n
g
,
v
o
l
.
1
1
,
n
o
.
8
,
p
p
.
7
2
–
8
0
,
2
0
2
4
,
d
o
i
:
1
0
.
1
4
4
4
5
/
2
3
4
8
8
3
7
9
/
I
J
EE
E
-
V
1
1
I
8
P
1
0
7
.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
S
.
S
e
l
v
i
is
a
p
ro
fe
ss
o
r
a
n
d
h
e
a
d
in
De
p
a
rtme
n
t
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
,
P
a
n
ima
lar
E
n
g
i
n
e
e
rin
g
Co
l
leg
e
.
S
h
e
re
c
e
iv
e
d
b
a
c
h
e
lo
r
in
E
n
g
i
n
e
e
rin
g
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
fro
m
t
h
e
Co
ll
e
g
e
o
f
E
n
g
i
n
e
e
rin
g
,
G
u
in
d
y
,
An
n
a
Un
iv
e
rsity
.
S
h
e
o
b
tai
n
e
d
h
e
r
m
a
ste
r
o
f
En
g
in
e
e
rin
g
in
P
o
we
r
El
e
c
tro
n
ics
a
n
d
Dri
v
e
s
a
n
d
a
d
o
c
to
ra
te
d
e
g
re
e
fro
m
S
a
th
y
a
b
a
m
a
Un
iv
e
rsity
,
C
h
e
n
n
a
i
.
S
h
e
h
a
s
a
ro
u
n
d
3
0
y
e
a
rs
o
f
tea
c
h
i
n
g
e
x
p
e
rien
c
e
.
He
r
a
re
a
o
f
in
tere
st
is
li
g
h
t
n
i
n
g
a
n
d
i
n
d
u
strial
d
ri
v
e
s
.
S
h
e
h
a
s
p
u
b
li
s
h
e
d
se
v
e
ra
l
re
se
a
rc
h
a
rti
c
le
s
in
n
a
ti
o
n
a
l/
in
ter
n
a
ti
o
n
a
l
c
o
n
fe
re
n
c
e
s
a
n
d
jo
u
rn
a
l
s
.
S
h
e
h
a
s
se
c
u
re
d
Rs.
1
5
lak
h
s
in
re
se
a
rc
h
fu
n
d
i
n
g
,
i
n
c
l
u
d
i
n
g
a
g
ra
n
t
fro
m
th
e
All
-
In
d
ia
Co
u
n
c
il
fo
r
Tec
h
n
ica
l
Ed
u
c
a
ti
o
n
(AICTE
)
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
se
lv
ise
l
v
a
ra
j@g
m
a
il
.
c
o
m
.
N.
S
h
a
n
th
i
is an
a
ss
istan
t
p
r
o
fe
ss
o
r
i
n
t
h
e
El
e
c
tri
c
a
l
a
n
d
E
lec
tro
n
ics
En
g
in
e
e
rin
g
De
p
a
rtme
n
t,
S
ri
S
a
iram
En
g
i
n
e
e
rin
g
Co
ll
e
g
e
,
Ch
e
n
n
a
i,
In
d
ia
,
sin
c
e
2
0
1
0
.
S
h
e
re
c
e
iv
e
d
a
B.
E
.
d
e
g
re
e
in
El
e
c
tri
c
a
l
a
n
d
El
e
c
tro
n
ic
s
En
g
in
e
e
rin
g
fr
o
m
M
a
d
ra
s
Un
i
v
e
rsity
,
Tam
il
Na
d
u
,
i
n
1
9
9
8
a
n
d
an
M
.
E
.
d
e
g
re
e
in
P
o
we
r
El
e
c
tro
n
ics
a
n
d
Dri
v
e
s fro
m
M
a
d
h
a
En
g
i
n
e
e
rin
g
C
o
ll
e
g
e
,
A
n
n
a
Un
iv
e
rsity
,
In
d
ia
,
in
2
0
0
8
;
a
n
d
c
u
rre
n
tl
y
p
u
rs
u
in
g
a
Ph
.
D
.
i
n
P
o
w
e
r
El
e
c
tro
n
ics
fro
m
An
n
a
Un
iv
e
rsity
,
Tam
il
Na
d
u
,
I
n
d
ia
.
S
h
e
is
a
lso
a
m
e
m
b
e
r
o
f
IEI
a
n
d
I
EE
E
p
r
o
fe
ss
io
n
a
l
so
c
ieties
.
He
r
re
se
a
r
c
h
in
tere
sts
in
c
lu
d
e
th
e
field
o
f
p
o
we
r
e
lec
tro
n
ics
,
m
o
t
o
r
d
riv
e
s,
a
n
d
p
h
o
to
v
o
l
taic
p
o
we
r
sy
ste
m
s.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
sh
a
n
t
h
i.
e
e
e
@s
a
iram
i
t.
e
d
u
.
in
.
Evaluation Warning : The document was created with Spire.PDF for Python.