I
nte
rna
t
io
na
l J
o
urna
l o
f
Adv
a
nces in Applie
d Science
s
(
I
J
AAS)
Vo
l.
1
3
,
No
.
4
,
Dec
em
b
er
2
0
2
4
,
p
p
.
7
6
3
~
7
72
I
SS
N:
2252
-
8
8
1
4
,
DOI
:
1
0
.
1
1
5
9
1
/ijaas
.
v
1
3
.
i
4
.
pp
763
-
7
72
763
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
a
a
s
.
ia
esco
r
e.
co
m
H
y
brid loa
d
forec
a
sting
cons
iderin
g
energy
ef
fi
cienc
y
and
renewab
le
energy
using
neural
net
wo
rk
Adria
na
H
a
ziqa
h M
o
hd
Aiz
a
m
1
,
No
f
ri
Yenit
a
Da
hla
n
1,
2
,
Sa
ida
t
ul H
a
bs
a
h Asm
a
n
2
, S
i
t
i H
a
j
a
r
Yus
o
f
f
3
1
S
c
h
o
o
l
o
f
E
l
e
c
t
r
i
c
a
l
E
n
g
i
n
e
e
r
i
n
g
,
C
o
l
l
e
g
e
o
f
E
n
g
i
n
e
e
r
i
n
g
,
U
n
i
v
e
r
si
t
i
Te
k
n
o
l
o
g
i
M
A
R
A
,
S
h
a
h
A
l
a
m,
M
a
l
a
y
s
i
a
2
S
o
l
a
r
R
e
se
a
r
c
h
I
n
st
i
t
u
t
e
,
U
n
i
v
e
r
si
t
i
T
e
k
n
o
l
o
g
i
M
A
R
A
,
S
h
a
h
A
l
a
m
,
M
a
l
a
y
s
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
C
o
m
p
u
t
e
r
E
n
g
i
n
e
e
r
i
n
g
,
F
a
c
u
l
t
y
o
f
E
n
g
i
n
e
e
r
i
n
g
,
I
n
t
e
r
n
a
t
i
o
n
a
l
I
sl
a
mi
c
U
n
i
v
e
r
si
t
y
M
a
l
a
y
si
a
,
K
u
a
l
a
Lu
m
p
u
r
,
M
a
l
a
y
si
a
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Feb
7
,
2
0
2
4
R
ev
is
ed
Ma
y
2
0
,
2
0
2
4
Acc
ep
ted
J
u
n
1
9
,
2
0
2
4
In
re
c
e
n
t
y
e
a
rs,
t
h
e
re
latio
n
sh
i
p
b
e
twe
e
n
a
c
o
u
n
try
'
s
g
ro
ss
d
o
m
e
sti
c
p
ro
d
u
c
t
(
G
DP
)
a
n
d
it
s
e
lec
tri
c
it
y
c
o
n
s
u
m
p
ti
o
n
h
a
s
c
h
a
n
g
e
d
si
g
n
ifi
c
a
n
tl
y
d
u
e
to
in
c
re
a
se
d
e
n
e
rg
y
e
fficie
n
c
y
(EE
)
a
n
d
re
n
e
wa
b
le
e
n
e
rg
y
(RE)
a
d
o
p
ti
o
n
.
Th
i
s
d
e
c
o
u
p
li
n
g
d
isru
p
ts
c
o
n
v
e
n
t
io
n
a
l
lo
a
d
fo
re
c
a
stin
g
m
o
d
e
ls,
a
ffe
c
ti
n
g
u
ti
li
t
y
c
o
m
p
a
n
ies
.
T
h
is
stu
d
y
h
a
s
d
e
v
e
lo
p
e
d
a
n
in
n
o
v
a
ti
v
e
s
o
lu
t
io
n
u
sin
g
a
n
a
rti
ficia
l
n
e
u
ra
l
n
e
tw
o
rk
(AN
N)
Hy
b
r
id
m
e
th
o
d
f
o
r
lo
a
d
f
o
re
c
a
stin
g
,
re
su
lt
in
g
in
a
re
m
a
rk
a
b
l
y
a
c
c
u
ra
te
m
o
d
e
l
with
9
9
.
6
8
%
p
re
c
isio
n
.
Ap
p
ly
in
g
th
is mo
d
e
l
to
M
a
lay
sia
'
s e
lec
tri
c
it
y
c
o
n
s
u
m
p
ti
o
n
fr
o
m
2
0
2
0
to
2
0
4
0
re
v
e
a
ls a
sig
n
ifi
c
a
n
t
1
3
%
re
d
u
c
ti
o
n
wh
e
n
a
c
c
o
u
n
ti
n
g
f
o
r
EE
a
n
d
RE
tren
d
s.
T
h
is
m
e
th
o
d
a
i
d
s
ris
k
m
a
n
a
g
e
m
e
n
t,
c
o
n
ti
n
g
e
n
c
y
p
lan
n
in
g
,
a
n
d
d
e
c
isio
n
-
m
a
k
in
g
b
y
a
c
c
u
ra
tely
re
flec
ti
n
g
c
h
a
n
g
i
n
g
e
n
e
rg
y
u
sa
g
e
d
y
n
a
m
ics
in
fl
u
e
n
c
e
d
b
y
EE
a
n
d
RE
s
o
u
rc
e
s.
K
ey
w
o
r
d
s
:
E
lectr
icity
co
n
s
u
m
p
tio
n
E
n
er
g
y
ef
f
icien
cy
L
o
ad
f
o
r
ec
asti
n
g
Neu
r
al
n
etwo
r
k
f
u
n
ctio
n
R
en
ewa
b
le
en
er
g
y
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
:
No
f
r
i Y
en
ita
Dah
lan
Sch
o
o
l o
f
E
lectr
ical
E
n
g
in
ee
r
in
g
,
C
o
lleg
e
o
f
E
n
g
i
n
ee
r
in
g
,
U
n
iv
er
s
iti T
ek
n
o
lo
g
i M
AR
A
4
0
4
5
0
,
Sh
ah
Alam
,
Selan
g
o
r
,
Ma
lay
s
ia
E
m
ail: n
o
f
r
iy
e
n
ita0
1
2
@
u
itm
.
e
d
u
.
m
y
1.
I
NT
RO
D
UCT
I
O
N
Acc
o
r
d
in
g
to
Ma
lay
s
ia’
s
E
co
n
o
m
ic
Plan
n
in
g
Un
it,
th
e
en
e
r
g
y
s
ec
to
r
co
n
tr
i
b
u
tes
2
8
%
o
f
th
e
g
r
o
s
s
d
o
m
esti
c
p
r
o
d
u
ct
(
GDP
)
p
er
an
n
u
m
,
w
h
ich
is
ar
o
u
n
d
R
M
4
0
0
b
illi
o
n
[
1
]
.
On
th
e
d
e
m
a
n
d
s
id
e,
th
e
s
ec
to
r
n
ee
d
s
to
ad
d
r
ess
is
s
u
es
s
u
ch
as
in
cr
ea
s
in
g
en
er
g
y
co
n
s
u
m
p
tio
n
an
d
th
e
b
u
r
d
e
n
o
f
s
u
b
s
id
ies.
On
th
e
s
u
p
p
l
y
s
id
e,
it
f
ac
es
ch
allen
g
es
l
ik
e
th
e
d
ep
letio
n
o
f
n
o
n
-
r
e
n
e
wab
le
r
eso
u
r
ce
s
,
r
is
in
g
s
u
p
p
ly
co
s
ts
,
co
m
p
lex
en
v
ir
o
n
m
en
tal
is
s
u
es,
an
d
f
r
ag
m
en
ted
g
o
v
er
n
an
ce
.
I
n
ter
n
atio
n
ally
,
f
u
tu
r
e
-
p
r
o
o
f
in
g
th
e
in
d
u
s
tr
y
in
v
o
lv
es
tack
li
n
g
m
eg
at
r
en
d
s
s
u
ch
a
s
th
e
en
er
g
y
tr
a
n
s
itio
n
an
d
v
ar
io
u
s
r
eg
io
n
al
an
d
g
lo
b
al
s
o
cio
ec
o
n
o
m
ic
,
tech
n
o
lo
g
ical,
an
d
g
eo
p
o
liti
ca
l
ch
an
g
es
th
at
co
u
l
d
r
esh
a
p
e
th
e
en
e
r
g
y
lan
d
s
ca
p
e
in
i
m
p
lem
en
tin
g
th
e
1
7
s
u
s
tain
ab
le
d
ev
elo
p
m
e
n
t g
o
als
(
SDGs
)
[
2
]
.
A
s
tu
d
y
b
y
C
h
an
d
r
an
et
a
l
.
[
3
]
f
o
u
n
d
a
clea
r
lin
k
b
etwe
en
en
er
g
y
co
n
s
u
m
p
tio
n
a
n
d
ec
o
n
o
m
ic
g
r
o
wth
in
Ma
lay
s
ia.
Oztu
r
k
a
n
d
Aca
r
av
ci
[
4
]
id
en
tifie
d
f
o
u
r
ty
p
es
o
f
ca
u
s
ality
b
etwe
en
th
ese
v
ar
iab
les:
n
o
ca
u
s
ality
,
u
n
id
ir
ec
tio
n
al
ca
u
s
ality
f
r
o
m
en
er
g
y
co
n
s
u
m
p
t
io
n
to
ec
o
n
o
m
ic
g
r
o
wth
an
d
v
ice
v
er
s
a,
an
d
b
id
ir
ec
tio
n
al
ca
u
s
ality
.
L
iao
et
a
l
.
[
5
]
d
is
co
v
er
ed
t
h
at
a
o
n
e
p
er
ce
n
ta
g
e
p
o
in
t
i
n
cr
ea
s
e
in
GDP
p
er
ca
p
ita
g
r
o
wth
lead
s
to
a
0
.
1
5
p
e
r
ce
n
t
ag
e
p
o
in
t
in
c
r
ea
s
e
in
en
er
g
y
u
s
ag
e.
I
n
C
h
in
a,
Gu
an
g
et
a
l
.
[
6
]
o
b
s
er
v
e
d
th
at
th
e
elec
tr
icity
g
r
o
wth
r
ate
av
er
a
g
ed
1
1
.
2
2
%
f
r
o
m
2
0
0
0
to
2
0
1
3
b
u
t
d
r
o
p
p
ed
to
5
.
5
5
%
f
r
o
m
2
0
1
4
to
2
0
1
9
.
R
ah
m
an
et
a
l
.
[
7
]
co
n
f
ir
m
ed
a
b
id
ir
ec
tio
n
al
r
elatio
n
s
h
ip
b
e
twee
n
r
en
ewa
b
le
en
e
r
g
y
(
RE
)
u
s
e
a
n
d
ec
o
n
o
m
ic
g
r
o
wth
in
Ma
lay
s
ia.
Z
h
an
g
et
a
l
.
[
8
]
war
n
ed
th
at
d
if
f
e
r
en
c
es
in
v
ar
iab
les
lik
e
in
d
u
s
tr
ial
g
r
o
wth
an
d
f
o
r
eig
n
d
ir
ec
t
in
v
estme
n
t
ac
r
o
s
s
c
o
u
n
tr
ies
co
u
ld
b
ias
r
esu
lts
u
n
less
p
r
o
p
er
l
y
ac
c
o
u
n
te
d
f
o
r
.
T
r
an
et
a
l
.
[
9
]
s
u
g
g
ested
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
8
1
4
I
n
t J Ad
v
Ap
p
l Sci
,
Vo
l.
1
3
,
No
.
4
,
Dec
em
b
er
2
0
2
4
:
763
-
7
7
2
764
th
at
an
aly
s
es
s
h
o
u
ld
co
n
s
id
er
GDP
th
r
esh
o
ld
ef
f
ec
ts
to
av
o
id
b
iases
.
Ma
u
leó
n
[
1
0
]
u
s
ed
a
s
tatis
tical
ap
p
r
o
ac
h
to
s
h
o
w
th
at
th
e
r
elatio
n
s
h
i
p
b
etwe
en
GDP
,
p
o
p
u
latio
n
g
r
o
w
th
,
an
d
e
n
er
g
y
d
e
m
a
n
d
is
in
cr
ea
s
in
g
ly
n
o
n
-
lin
ea
r
o
v
er
tim
e.
T
h
e
r
esear
ch
id
en
tifie
s
a
g
a
p
in
th
e
ac
cu
r
ac
y
o
f
cu
r
r
en
t
lo
ad
f
o
r
ec
asti
n
g
m
o
d
els,
p
ar
ticu
lar
ly
r
eg
ar
d
in
g
th
e
im
p
ac
t
o
f
en
er
g
y
ef
f
icien
c
y
(
EE
)
an
d
R
E
f
ac
to
r
s
.
W
h
ile
ex
is
tin
g
s
t
u
d
ies
ex
p
lo
r
e
th
e
r
elatio
n
s
h
ip
b
etwe
en
ec
o
n
o
m
ic
g
r
o
wth
an
d
e
n
er
g
y
co
n
s
u
m
p
tio
n
o
r
in
v
estme
n
t
f
a
cto
r
s
,
th
ey
d
o
n
o
t
s
p
ec
if
ically
ad
d
r
ess
h
o
w
E
E
an
d
R
E
s
ig
n
if
ican
tly
i
n
f
lu
en
c
e
lo
ad
f
o
r
ec
asti
n
g
.
Var
i
o
u
s
m
eth
o
d
s
h
av
e
b
ee
n
u
s
ed
f
o
r
elec
tr
ical
lo
ad
f
o
r
ec
asti
n
g
,
in
clu
d
in
g
tim
e
s
er
ies,
s
tatis
t
ical
an
aly
s
is
,
r
eg
r
ess
io
n
an
aly
s
is
,
m
u
ltip
le
lin
ea
r
r
eg
r
ess
io
n
,
an
d
a
r
tific
ial
in
tellig
en
ce
(
AI
)
.
T
h
e
r
a
p
id
d
ev
elo
p
m
en
t
o
f
AI
an
d
d
ee
p
le
ar
n
in
g
,
p
ar
ticu
lar
ly
n
eu
r
al
n
etwo
r
k
s
(
NN)
,
h
as
s
ig
n
if
ican
tly
im
p
ac
ted
t
h
e
elec
tr
i
city
s
ec
to
r
.
H
o
wev
er
,
tr
ad
itio
n
al
ar
tific
ial
n
eu
r
al
n
etwo
r
k
s
(
ANN)
f
ac
e
c
h
alle
n
g
es
in
m
ed
i
u
m
an
d
lo
n
g
-
te
r
m
lo
ad
f
o
r
ec
asti
n
g
d
u
e
t
o
d
ata
co
llectio
n
an
d
p
r
o
ce
s
s
in
g
d
if
f
ic
u
lties
.
T
o
ad
d
r
ess
th
ese
is
s
u
es,
Ma
u
leó
n
[
1
0
]
p
r
o
p
o
s
ed
u
s
in
g
r
ad
ial
b
a
s
is
f
u
n
ctio
n
n
eu
r
al
n
etwo
r
k
s
(
R
B
FN
N)
.
Ad
d
itio
n
ally
,
Ali
et
a
l
.
[
1
1
]
s
u
g
g
ested
a
f
u
zz
y
lo
g
ic
ap
p
r
o
ac
h
to
ac
co
u
n
t
f
o
r
th
e
n
o
n
-
lin
ea
r
r
elatio
n
s
h
ip
b
etwe
en
w
ea
th
er
an
d
p
ea
k
m
o
n
th
ly
l
o
a
d
s
.
I
n
a
m
o
r
e
r
a
d
ical
ap
p
r
o
ac
h
,
C
h
en
a
n
d
W
an
g
[
1
2
]
f
o
r
m
ed
a
co
m
m
ittee
o
f
ex
p
er
ts
to
f
o
r
ec
ast
a
n
n
u
al
en
er
g
y
co
n
s
u
m
p
tio
n
u
s
in
g
p
r
in
cip
al
co
m
p
o
n
e
n
t
an
aly
s
is
an
d
f
u
zz
y
f
ee
d
-
f
o
r
war
d
n
eu
r
al
n
etwo
r
k
s
(
PC
A
-
FF
NN)
,
co
m
b
in
ed
with
p
ar
t
ial
-
co
n
s
en
s
u
s
f
u
zz
y
in
ter
s
ec
tio
n
an
d
r
ad
ial
b
asis
f
u
n
ctio
n
n
etwo
r
k
(
PC
FI
-
R
B
F
)
m
eth
o
d
s
.
Desp
ite
th
ese
ad
v
an
ce
m
en
ts
,
ANNs
r
em
ain
o
n
e
o
f
th
e
m
o
s
t
wid
ely
u
s
ed
m
eth
o
d
s
,
m
o
d
elin
g
co
m
p
u
tatio
n
ally
th
e
s
tr
u
ctu
r
e
an
d
b
eh
av
io
r
o
f
b
io
lo
g
ical
b
r
ai
n
n
etwo
r
k
s
.
W
h
ile
v
ar
io
u
s
lo
ad
f
o
r
ec
asti
n
g
m
o
d
els
b
ased
o
n
NN
ap
p
r
o
ac
h
es
h
av
e
b
ee
n
p
r
o
p
o
s
ed
in
p
r
ev
io
u
s
s
tu
d
ies,
to
th
e
b
est
o
f
o
u
r
k
n
o
wled
g
e,
n
o
n
e
h
av
e
co
n
s
id
er
e
d
in
clu
d
in
g
th
e
im
p
ac
ts
o
f
R
E
an
d
E
E
,
esp
ec
ially
in
th
e
co
n
tex
t
o
f
Ma
lay
s
ia.
T
h
er
ef
o
r
e,
th
is
s
tu
d
y
aim
s
t
o
ad
d
r
ess
th
is
g
ap
b
y
d
ev
el
o
p
in
g
a
n
ew
lo
ad
f
o
r
ec
asti
n
g
m
et
h
o
d
u
s
in
g
a
h
y
b
r
id
a
p
p
r
o
a
ch
.
I
n
itially
,
a
b
aselin
e
ec
o
n
o
m
etr
ic
f
o
r
ec
ast
was
f
o
r
m
u
lated
to
ca
p
tu
r
e
th
e
r
elatio
n
s
h
ip
b
et
wee
n
p
ar
am
eter
s
.
Su
b
s
eq
u
en
tly
,
th
e
en
d
-
u
s
e
f
o
r
ec
ast
was
ca
lib
r
ated
to
th
e
ec
o
n
o
m
etr
ic
f
o
r
ec
ast
to
in
c
o
r
p
o
r
ate
ad
d
itio
n
al
m
ac
r
o
tr
en
d
s
,
alo
n
g
with
s
ev
er
al
p
o
s
t
-
esti
m
atio
n
ad
ju
s
tm
en
ts
to
im
p
r
o
v
e
ac
c
u
r
ac
y
.
I
n
ess
en
ce
,
th
e
f
o
r
ec
asted
r
esu
lts
o
b
tain
ed
f
r
o
m
th
e
f
ee
d
-
f
o
r
war
d
b
ac
k
-
p
r
o
p
ag
atio
n
ANN
m
o
d
elin
g
u
s
in
g
MA
T
L
AB
d
o
n
o
t
cu
r
r
en
tly
r
ef
lect
th
e
p
r
ev
ailin
g
tr
e
n
d
s
.
T
h
u
s
,
ad
ju
s
tin
g
th
e
p
o
s
t
-
esti
m
atio
n
m
o
d
el
f
o
r
R
E
an
d
E
E
wo
u
ld
en
h
an
ce
th
e
ac
cu
r
ac
y
o
f
th
e
f
o
r
ec
asti
n
g
r
esu
lts
,
th
er
eb
y
d
em
o
n
s
tr
atin
g
th
e
im
p
ac
t
o
f
t
h
e
d
ec
o
u
p
lin
g
b
etwe
en
GDP
an
d
p
o
p
u
latio
n
o
n
elec
tr
icity
co
n
s
u
m
p
tio
n
.
T
h
is
m
eth
o
d
co
u
l
d
en
h
an
ce
r
is
k
m
an
ag
em
en
t,
co
n
tin
g
en
c
y
p
l
an
n
in
g
,
an
d
d
ec
is
io
n
-
m
ak
i
n
g
b
y
m
eticu
lo
u
s
ly
ca
p
tu
r
in
g
th
e
e
v
o
lv
in
g
d
y
n
am
ics
o
f
en
er
g
y
u
s
ag
e,
wh
ic
h
ar
e
in
tr
icate
ly
in
f
l
u
en
ce
d
b
y
b
o
t
h
EE
m
ea
s
u
r
es
an
d
RE
s
o
u
r
ce
s
.
T
h
e
p
ap
e
r
is
s
tr
u
ctu
r
ed
as
f
o
l
lo
ws
:
in
s
ec
tio
n
2
,
th
e
m
eth
o
d
o
lo
g
y
p
r
o
ce
s
s
o
f
th
e
s
tu
d
y
i
s
o
u
tlin
ed
,
em
p
h
asizin
g
th
e
en
tire
p
r
o
ce
s
s
s
tar
tin
g
f
r
o
m
d
ata
c
o
llectio
n
,
m
o
d
elin
g
o
f
ANN
ec
o
n
o
m
etr
ics,
an
d
t
u
n
in
g
m
o
d
elin
g
p
ar
am
ete
r
s
.
Sectio
n
3
ex
p
lain
s
th
e
ANN
co
r
r
elatio
n
an
aly
s
is
an
d
p
r
esen
ts
th
e
r
esu
lts
o
f
th
e
f
o
llo
win
g
p
r
o
ce
s
s
,
wh
er
e
r
eg
r
ess
io
n
an
aly
s
is
is
p
er
f
o
r
m
ed
to
s
h
o
w
th
e
v
ar
iab
les'
co
r
r
el
atio
n
.
Fu
r
th
er
m
o
r
e,
E
E
an
d
R
E
m
ea
s
u
r
es
wer
e
u
s
ed
to
d
esig
n
th
e
en
d
-
u
s
e
m
o
d
el
p
r
io
r
to
th
e
m
o
d
el
ad
ju
s
tm
en
t.
Fin
ally
,
co
n
clu
s
io
n
s
ar
e
d
r
awn
in
s
ec
ti
o
n
4.
2.
RE
S
E
ARCH
M
E
T
H
O
D
T
h
e
r
esear
ch
m
eth
o
d
o
lo
g
y
ca
n
b
e
s
u
m
m
ed
u
p
in
th
r
ee
p
r
o
c
ess
es,
n
am
ely
,
i
)
d
ata
g
at
h
er
in
g
,
ii
)
ANN
m
o
d
elin
g
,
an
d
iii
)
h
y
b
r
id
l
o
a
d
f
o
r
ec
asti
n
g
.
T
o
s
tar
t,
d
ata
g
ath
er
in
g
f
r
o
m
r
eliab
le
s
o
u
r
c
es
wer
e
p
er
f
o
r
m
e
d
.
ANN
ec
o
n
o
m
etr
ic
-
b
ased
m
o
d
elin
g
was
th
en
co
n
s
tr
u
cte
d
,
a
n
d
r
etr
ai
n
in
g
o
f
th
e
n
etwo
r
k
was
d
o
n
e
if
th
e
R
-
s
q
u
ar
ed
v
al
u
e
wa
s
lo
w,
s
ig
n
if
y
in
g
th
at
t
h
e
r
esu
lt
f
r
o
m
t
h
e
m
o
d
el
wa
s
in
ac
cu
r
ate
.
T
h
e
lo
ad
f
o
r
ec
asti
n
g
was
estab
lis
h
ed
f
o
r
t
h
e
y
ea
r
s
2
0
2
0
to
2
0
4
0
.
Fu
r
t
h
er
m
o
r
e,
r
esid
u
al
an
aly
s
is
was
p
er
f
o
r
m
ed
,
a
n
d
th
e
r
esu
lt
s
wer
e
co
m
p
ar
ed
with
th
e
r
e
g
r
ess
io
n
an
aly
s
is
to
f
u
r
th
er
ex
am
i
n
e
th
e
ac
cu
r
ac
y
o
f
t
h
e
r
esu
ltin
g
o
u
tp
u
t.
Af
ter
th
e
in
itial
r
esu
lts
h
a
d
b
ee
n
ac
q
u
ir
e
d
f
r
o
m
th
e
c
o
n
s
tr
u
cted
n
etwo
r
k
,
th
e
EE
en
d
-
u
s
e
m
o
d
el
an
d
RE
p
o
s
t
-
esti
m
atio
n
m
o
d
el
u
s
in
g
th
e
d
ata
f
o
r
ec
ast
ed
f
r
o
m
th
e
y
ea
r
s
2
0
1
9
to
2
0
4
0
w
er
e
u
tili
z
ed
.
Af
ter
b
o
th
m
o
d
els
h
av
e
b
ee
n
co
n
s
tr
u
cted
,
t
h
ey
will
th
e
n
b
e
s
u
b
tr
ac
ted
f
r
o
m
th
e
o
r
i
g
in
al
ANN
-
m
o
d
ellin
g
b
aselin
e
ec
o
n
o
m
etr
ic
m
o
d
el.
Fo
r
EE
,
th
e
ass
u
m
p
tio
n
is
th
at
a
5
%
r
ed
u
ctio
n
in
th
e
to
tal
en
er
g
y
co
n
s
u
m
p
tio
n
will
h
a
p
p
en
an
n
u
ally
wh
ile
in
RE
,
an
ass
u
m
p
tio
n
o
f
1
0
%
in
en
er
g
y
c
o
n
s
u
m
p
tio
n
will
o
cc
u
r
.
Pre
v
io
u
s
ly
,
th
e
r
elatio
n
s
h
ip
b
etwe
en
en
er
g
y
co
n
s
u
m
p
tio
n
a
n
d
GDP
ca
n
b
e
ex
p
lain
ed
b
y
th
e
ec
o
n
o
m
etr
i
c
m
o
d
el.
T
h
e
e
co
n
o
m
etr
ic
m
o
d
el
o
b
ey
s
th
e
n
eo
-
class
ical
th
eo
r
y
o
f
g
r
o
wth
p
r
o
p
o
s
ed
b
y
R
o
b
er
t
So
lo
w
an
d
T
r
ev
o
r
Swan
in
1
9
5
6
[
1
3
]
.
I
n
th
is
th
eo
r
y
,
en
er
g
y
is
r
ec
o
g
n
ized
f
o
r
its
r
o
le
in
r
elatio
n
to
ec
o
n
o
m
ic
g
r
o
wth
,
ac
tin
g
as
b
o
th
an
in
ter
m
ed
iate
in
p
u
t
in
th
e
p
r
o
d
u
ctio
n
p
r
o
ce
s
s
an
d
a
tr
ad
ab
le
co
m
m
o
d
ity
in
th
e
m
ar
k
e
t
.
Ho
wev
e
r
,
t
h
e
th
eo
r
y
m
ain
ly
f
o
c
u
s
es
o
n
h
o
w
en
er
g
y
af
f
ec
t
s
p
r
o
d
u
ctio
n
an
d
ec
o
n
o
m
ic
ef
f
i
cien
cy
b
u
t
f
ails
to
ca
p
tu
r
e
th
e
in
tr
icate
an
d
m
u
ltifa
ce
ted
r
elatio
n
s
h
ip
b
etwe
en
en
er
g
y
a
n
d
th
e
ec
o
n
o
m
y
,
o
r
i
ts
b
r
o
ad
er
im
p
licatio
n
s
f
o
r
s
o
ciety
an
d
th
e
e
n
v
ir
o
n
m
en
t
as
h
as
b
ee
n
s
h
o
wn
b
y
R
o
ch
o
n
an
d
R
o
s
s
i
[
1
4
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ad
v
Ap
p
l Sci
I
SS
N:
2252
-
8
8
1
4
Hyb
r
id
lo
a
d
fo
r
ec
a
s
tin
g
c
o
n
s
id
erin
g
en
erg
y
efficien
cy
a
n
d
r
en
ewa
b
le
…
(
A
d
r
ia
n
a
Ha
z
iq
a
h
Mo
h
d
A
iz
a
m
)
765
2
.
1
.
Da
t
a
g
a
t
hering
I
n
th
is
s
tag
e,
th
e
h
is
to
r
ical
p
atter
n
s
o
f
p
r
o
jecte
d
co
n
s
u
m
e
r
d
em
an
d
th
at
d
o
n
o
t
tak
e
E
E
a
n
d
R
E
in
to
ac
co
u
n
t
ar
e
in
tr
o
d
u
ce
d
f
o
r
t
h
e
b
aselin
e
elec
tr
icity
co
n
s
u
m
p
tio
n
f
o
r
e
ca
s
t.
T
h
e
in
d
ep
e
n
d
en
t
v
ar
iab
le
o
f
t
h
is
m
o
d
el
is
th
e
p
o
p
u
latio
n
a
n
d
G
DP
p
er
ca
p
ita
wh
ile
th
e
d
ep
e
n
d
en
t
v
ar
iab
le
is
th
e
e
n
er
g
y
co
n
s
u
m
p
tio
n
in
k
to
e.
T
h
e
in
d
e
p
en
d
e
n
t
v
ar
iab
les
ac
t
as
th
e
i
n
p
u
t
o
f
th
e
ANN
m
o
d
ellin
g
wh
ile
t
h
e
d
ep
en
d
en
t
v
ar
iab
le
is
th
e
tar
g
et
d
ata.
T
h
e
d
ata
u
s
ed
in
th
e
A
NN
m
o
d
elin
g
ar
e
f
r
o
m
t
h
e
y
ea
r
s
1
9
8
0
to
2
0
4
0
an
d
wer
e
ac
q
u
ir
ed
t
h
r
o
u
g
h
th
e
Natio
n
al
E
n
er
g
y
B
alan
ce
(
NE
B
)
an
d
th
e
Ma
lay
s
ian
E
n
er
g
y
I
n
f
o
r
m
atio
n
H
ub
(
ME
I
H
)
m
an
ag
ed
b
y
th
e
en
er
g
y
co
m
m
is
s
io
n
.
Fo
r
th
e
n
ex
t
p
ar
t
o
f
d
ata
g
a
th
er
in
g
,
co
n
cr
ete
d
ata
t
h
at
m
ea
s
u
r
es
th
e
EE
a
n
d
RE
ar
e
n
ee
d
ed
t
o
co
n
s
tr
u
ct
b
o
th
t
h
e
en
d
-
u
s
e
m
o
d
el
an
d
p
o
s
t
-
esti
m
atio
n
m
o
d
el.
I
n
te
r
m
s
o
f
EE
,
th
e
s
tu
d
y
co
n
s
id
er
s
EE
m
ea
s
u
r
es
f
r
o
m
ap
p
lian
ce
s
s
u
ch
as
air
co
n
d
itio
n
e
r
s
,
r
ef
r
ig
er
ato
r
s
,
an
d
f
a
n
s
,
th
at
ar
e
b
a
s
ed
o
n
Ma
lay
s
ia
’
s
m
in
im
u
m
en
er
g
y
s
tan
d
ar
d
(
M
E
PS
)
[
1
5
]
.
T
h
e
d
ata
u
s
ed
wer
e
f
r
o
m
th
e
y
ea
r
s
2
0
1
3
to
2
0
1
5
.
On
th
e
o
th
er
h
an
d
,
all
ty
p
es o
f
RE
ar
e
co
n
s
id
er
ed
,
an
d
th
e
a
v
ailab
le
d
ata
ar
e
f
r
o
m
th
e
y
ea
r
s
2
0
1
0
to
2
0
1
9
.
2
.
2
.
ANN
-
ba
s
ed
ec
o
no
m
e
t
ric
m
o
del
T
h
e
g
en
e
r
al
eq
u
atio
n
o
f
th
e
ANN
m
o
d
el
is
s
h
o
wn
i
n
(
1
)
.
A
NN
tak
es
f
ewe
r
in
s
tan
ce
s
th
a
n
s
tatis
tical
tech
n
iq
u
es
as
it
is
n
o
n
lin
ea
r
i
n
ch
ar
ac
te
r
is
tic,
m
ak
in
g
it
m
o
r
e
ef
f
icien
t
to
s
o
lv
e
m
o
r
e
c
o
m
p
lex
p
r
o
b
lem
s
t
h
an
m
o
s
t
s
tan
d
ar
d
ap
p
r
o
ac
h
es.
T
h
e
NN
was
c
o
n
s
tr
u
cted
b
y
u
tili
z
in
g
th
e
to
o
ls
a
v
ailab
le
in
MA
T
L
AB
.
T
h
e
n
etwo
r
k
was
a
th
r
ee
-
lay
er
f
ee
d
-
f
o
r
war
d
b
ac
k
-
p
r
o
p
ag
atio
n
wh
er
e
th
e
b
asis
o
f
th
e
n
etwo
r
k
is
a
s
in
g
le
h
id
d
en
lay
er
n
etwo
r
k
with
o
n
e
in
p
u
t
lay
er
an
d
o
n
e
o
u
tp
u
t
lay
er
.
Netwo
r
k
s
ettin
g
s
lik
e
th
e
n
u
m
b
er
o
f
n
eu
r
o
n
s
an
d
tr
an
s
f
er
f
u
n
ctio
n
ar
e
h
eu
r
is
tic
ally
co
m
p
u
te
d
an
d
cr
o
s
s
-
v
alid
atio
n
m
eth
o
d
s
ar
e
u
s
ed
to
f
in
d
th
e
id
ea
l
n
etwo
r
k
ar
ch
itectu
r
e.
Fu
r
th
e
r
m
o
r
e
,
th
e
th
r
ee
-
lay
er
f
ee
d
-
f
o
r
war
d
b
ac
k
-
p
r
o
p
ag
atio
n
w
as
ch
o
s
en
d
u
e
to
its
n
o
n
-
lin
ea
r
ity
h
an
d
lin
g
th
at
en
a
b
les
it
to
ca
p
tu
r
e
co
m
p
lex
r
elatio
n
s
h
ip
s
a
n
d
u
n
d
er
ly
in
g
p
atter
n
s
in
d
ata,
a
cr
u
cial
f
ea
tu
r
e
f
o
r
en
er
g
y
c
o
n
s
u
m
p
ti
o
n
esti
m
atio
n
.
B
ac
k
p
r
o
p
ag
atio
n
also
allo
ws
th
e
m
o
d
el
to
iter
ativ
el
y
ad
ju
s
t
its
weig
h
ts
to
m
in
im
ize
er
r
o
r
.
T
h
is
p
r
o
ce
s
s
h
elp
s
t
h
e
m
o
d
el
to
c
o
n
v
e
r
g
e
to
a
s
o
lu
tio
n
th
at
ca
n
ac
cu
r
ately
r
e
p
r
esen
t
th
e
u
n
d
er
ly
i
n
g
p
atter
n
s
in
th
e
d
ata
.
=
(
ℎ
−
1
×
−
1
)
(
1
)
Fig
u
r
e
1
is
th
e
v
is
u
aliza
tio
n
o
f
th
e
ar
ch
itectu
r
e
o
f
th
e
n
etwo
r
k
.
T
h
e
i
n
p
u
t
d
ata,
x
is
th
e
in
d
ep
en
d
e
n
t
v
ar
iab
les
n
am
ely
th
e
p
o
p
u
latio
n
a
n
d
t
h
e
GDP
p
er
ca
p
ita
d
a
ta
,
wh
ile
th
e
elec
tr
icity
co
n
s
u
m
p
tio
n
w
h
ich
is
th
e
d
ep
en
d
e
n
t
v
ar
ia
b
le
ac
ts
as
th
e
tar
g
et
d
ata,
y
m
ea
n
in
g
th
at
w
e
p
u
t
th
e
y
d
ata
to
a
ct
as
th
e
b
en
ch
m
ar
k
f
o
r
t
h
e
f
o
r
ec
asted
o
u
tp
u
t,
Y
to
m
ea
s
u
r
e
its
elf
ag
ain
s
t.
T
h
e
tar
g
et
f
o
r
th
e
f
o
r
ec
asted
o
u
tp
u
t,
Y
is
th
e
p
er
io
d
f
r
o
m
2
0
2
1
to
2
0
4
0
.
Fig
u
r
e
1
.
ANN
ar
ch
itectu
r
e
2
.
2
.
1
.
T
ra
ini
ng
t
he
m
o
del
Du
r
in
g
th
e
tr
ain
in
g
p
r
o
ce
s
s
,
th
e
n
etwo
r
k
u
n
d
er
wen
t
b
o
th
f
o
r
war
d
an
d
b
ac
k
war
d
p
r
o
p
ag
at
io
n
.
I
n
th
e
f
o
r
war
d
p
r
o
p
a
g
atio
n
p
r
o
ce
s
s
,
th
e
in
p
u
t
d
ata
ar
e
p
ass
ed
th
r
o
u
g
h
th
e
5
0
h
id
d
en
n
etwo
r
k
s
.
T
h
e
in
p
u
t
v
alu
es
p
r
o
p
a
g
ate
in
th
e
d
ir
e
ctio
n
f
r
o
m
lef
t
(
in
p
u
t)
to
r
i
g
h
t
(
o
u
tp
u
t)
.
Du
r
in
g
th
is
p
r
o
ce
s
s
,
th
e
ac
tiv
atio
n
f
u
n
ctio
n
o
cc
u
r
s
wh
en
th
e
p
r
e
-
ac
tiv
atio
n
f
u
n
ctio
n
ca
lcu
lates
th
e
weig
h
ted
s
u
m
s
o
f
th
e
d
ata
,
an
d
wh
en
th
e
ac
tiv
atio
n
f
u
n
ctio
n
is
ev
en
tu
ally
ap
p
lie
d
,
it
wo
u
ld
b
e
b
ased
o
n
th
e
weig
h
ted
s
u
m
to
e
n
s
u
r
e
th
at
th
e
n
etwo
r
k
f
lo
ws
n
o
n
-
lin
ea
r
ly
u
s
in
g
b
ias.
Fu
r
th
e
r
m
o
r
e,
ac
co
r
d
i
n
g
to
th
e
s
tu
d
y
co
n
d
u
cted
in
[
1
6
]
,
th
e
ex
is
ten
ce
o
f
th
e
h
id
d
e
n
lay
er
s
en
ab
les
th
e
ex
tr
ac
tio
n
o
f
h
ig
h
er
-
o
r
d
e
r
s
tatis
tics
f
o
r
th
e
n
etwo
r
k
.
T
h
e
b
ac
k
p
r
o
p
ag
atio
n
m
e
an
wh
ile
p
r
o
p
a
g
ates
th
e
d
ata
i
n
th
e
o
p
p
o
s
ite
d
i
r
ec
tio
n
wh
ile
co
m
p
a
r
in
g
th
e
ac
tu
al
o
u
tp
u
t
to
th
e
d
esire
d
o
u
tp
u
t
a
n
d
th
e
n
ea
c
h
n
o
d
e
is
ad
ju
s
ted
b
y
ch
a
n
g
in
g
th
e
w
eig
h
ted
s
u
m
s
to
r
e
d
u
ce
th
e
e
r
r
o
r
in
p
r
ed
ictin
g
t
h
e
o
u
t
p
u
t.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
8
1
4
I
n
t J Ad
v
Ap
p
l Sci
,
Vo
l.
1
3
,
No
.
4
,
Dec
em
b
er
2
0
2
4
:
763
-
7
7
2
766
As
h
as
b
ee
n
co
n
cu
r
r
ed
,
f
o
r
th
is
n
et
wo
r
k
th
er
e
ar
e
two
in
p
u
t
d
ata,
wh
ich
ar
e
th
e
p
o
p
u
lati
o
n
an
d
th
e
Ma
lay
s
ian
GDP
p
er
ca
p
ita
f
r
o
m
th
e
y
ea
r
1
9
8
0
to
2
0
4
0
.
Me
an
wh
ile,
th
e
tar
g
et
d
ata
ar
e
also
co
n
s
id
er
ed
as
in
p
u
t
in
th
e
in
itial
p
h
ase
as
th
e
m
o
d
el
lear
n
ed
t
o
ap
p
r
o
x
im
a
te
th
e
u
n
d
er
ly
i
n
g
m
ap
p
in
g
b
et
wee
n
th
e
in
p
u
t
an
d
th
e
tar
g
et
d
ata
b
y
a
d
ju
s
tin
g
its
in
ter
n
al
weig
h
ts
an
d
b
iases
.
Nex
t,
th
e
o
u
tp
u
t o
f
th
e
n
etwo
r
k
is
co
m
p
ar
e
d
to
th
e
co
r
r
esp
o
n
d
in
g
tar
g
et
d
ata
t
o
co
m
p
u
te
th
e
e
r
r
o
r
o
r
lo
s
s
.
B
y
r
ep
ea
ted
l
y
p
r
esen
tin
g
in
p
u
t
d
ata
an
d
tar
g
et
d
ata
p
air
s
to
th
e
n
et
wo
r
k
a
n
d
u
p
d
atin
g
its
p
ar
am
eter
s
,
th
e
m
o
d
el
aim
s
to
m
in
im
i
z
e
th
e
d
is
cr
ep
an
cy
b
etwe
en
its
p
r
ed
ictio
n
s
an
d
th
e
ac
tu
al
v
alu
e
as seen
in
th
e
tar
g
et
v
al
u
e.
Fig
u
r
e
2
s
h
o
ws
th
e
NN
tr
ain
i
n
g
in
f
o
r
m
atio
n
.
I
n
th
is
tr
ain
i
n
g
,
th
e
L
ev
en
b
er
g
-
Ma
r
q
u
a
r
d
t
m
eth
o
d
is
u
s
ed
,
an
d
th
e
p
e
r
f
o
r
m
an
ce
is
e
v
alu
ated
u
s
in
g
th
e
m
ea
n
s
q
u
ar
ed
er
r
o
r
(
MSE
)
.
T
h
e
tr
ain
i
n
g
e
p
o
ch
tak
es
ar
o
u
n
d
1
2
iter
atio
n
s
f
o
r
th
e
d
ataset
in
v
o
lv
e
d
.
T
h
e
L
ev
en
b
er
g
-
Ma
r
q
u
ar
d
t
al
g
o
r
ith
m
ad
ju
s
ts
th
e
m
o
d
el’
s
p
ar
a
m
eter
s
d
u
r
in
g
tr
ain
i
n
g
to
o
p
tim
ize
th
e
f
it
b
etwe
en
th
e
p
r
ed
icted
en
er
g
y
co
n
s
u
m
p
tio
n
an
d
th
e
tar
g
et
d
ata.
I
n
th
e
1
2
iter
atio
n
s
d
o
n
e,
m
in
im
izatio
n
o
f
er
r
o
r
was
d
o
n
e
to
h
elp
th
e
ANN
m
o
d
el
lear
n
th
e
u
n
d
e
r
ly
in
g
r
elatio
n
s
h
ip
b
etwe
en
th
e
i
n
p
u
t
v
ar
iab
les,
th
at
is
th
e
GDP
p
er
ca
p
ita
a
n
d
p
o
p
u
latio
n
,
an
d
th
e
o
u
tp
u
t
v
ar
iab
le,
e
n
er
g
y
co
n
s
u
m
p
tio
n
.
T
h
e
ANN’
s
p
er
f
o
r
m
a
n
ce
v
alu
e
o
f
1
.
1
7
e
-
2
3
also
s
u
g
g
ested
an
e
x
tr
em
ely
lo
w
er
r
o
r
o
r
lo
s
s
in
d
icatin
g
th
at
th
e
ANN
m
o
d
el
h
as
ac
h
iev
ed
a
h
ig
h
lev
el
o
f
ac
cu
r
ac
y
an
d
p
r
ec
is
io
n
in
p
r
ed
ictin
g
th
e
tar
g
et
v
ar
iab
le.
T
h
e
g
r
a
d
ien
t o
f
th
e
ANN
m
o
d
el
b
ein
g
4
.
0
5
e
-
0
8
m
ea
n
t th
at
th
e
lo
s
s
f
u
n
ctio
n
is
c
h
an
g
in
g
s
lo
wly
with
r
esp
ec
t
to
th
e
m
o
d
el
p
a
r
am
et
er
s
,
in
d
icatin
g
th
at
th
e
m
o
d
el
is
ap
p
r
o
ac
h
in
g
a
l
o
ca
l
m
in
i
m
u
m
o
r
is
clo
s
e
to
co
n
v
er
g
en
ce
.
Fig
u
r
e
2
.
Netwo
r
k
tr
ain
in
g
in
f
o
r
m
atio
n
2
.
2
.
2
.
T
esting
t
he
m
o
del
T
h
e
n
ex
t
s
tep
in
th
is
p
r
o
ce
s
s
is
test
in
g
th
e
m
o
d
el.
T
esti
n
g
th
e
m
o
d
el
is
cr
u
cial
in
ass
ess
in
g
th
e
tr
ain
ed
n
etwo
r
k
’
s
p
er
f
o
r
m
a
n
c
e
b
y
way
o
f
r
e
g
r
ess
io
n
an
aly
s
is
th
at
co
m
p
ar
es
th
e
n
etwo
r
k
’
s
o
u
tp
u
t
an
d
t
h
e
tar
g
et
d
ata.
R
eg
r
ess
io
n
an
aly
s
is
ca
n
en
s
u
r
e
th
at
th
e
r
esu
ltin
g
n
etwo
r
k
o
u
tp
u
t
is
co
h
esiv
e
an
d
r
ef
lectiv
e
o
f
th
e
tar
g
et
d
ata.
A
s
ig
n
if
ican
t
ass
o
ciatio
n
ca
n
b
e
co
n
clu
d
e
d
wh
en
th
e
r
esu
ltin
g
R
-
v
alu
e
b
etwe
en
th
e
o
u
tp
u
t
d
ata
an
d
th
e
tar
g
et
d
ata
is
n
ea
r
1
.
Ho
wev
er
,
it
m
u
s
t
b
e
n
o
te
d
th
at
s
o
m
etim
es
th
e
m
o
d
el
m
ig
h
t
n
o
t
b
e
ac
cu
r
ate
o
n
th
e
f
ir
s
t
tr
y
th
er
ef
o
r
e,
r
et
r
ain
in
g
s
h
o
u
ld
b
e
d
o
n
e
to
m
ak
e
s
u
r
e
th
at
th
e
o
u
tp
u
t
alig
n
s
with
th
e
tar
g
et
d
ata
th
at
is
s
o
u
g
h
t.
2
.
3
.
H
y
brid
lo
a
d f
o
re
ca
s
t
ing
wit
h E
E
a
nd
RE
m
o
del
2
.
3
.
1
.
E
nerg
y
ef
f
iciency
end
-
us
e
m
o
del
Acc
o
r
d
in
g
t
o
B
h
attac
h
ar
y
y
a
an
d
T
im
ils
in
a
[
1
7
]
,
th
e
e
n
d
-
u
s
e
m
o
d
el
in
ter
m
s
o
f
th
e
e
n
g
in
ee
r
in
g
-
ec
o
n
o
m
y
ap
p
r
o
ac
h
r
e
f
er
s
to
th
e
d
is
ag
g
r
e
g
atio
n
o
f
t
h
e
f
i
n
al
co
n
s
u
m
p
tio
n
o
r
th
e
en
d
-
u
s
e
o
f
co
n
s
u
m
er
s
at
ce
r
tain
lev
els.
I
n
th
is
m
o
d
el,
t
h
e
en
d
-
u
s
e
ap
p
r
o
ac
h
is
s
y
m
b
o
li
z
ed
in
th
e
E
E
m
ea
s
u
r
es
th
a
t
ca
n
b
e
co
u
n
te
d
in
ev
er
y
d
a
y
ap
p
lian
ce
s
as
m
o
r
e
ap
p
lian
ce
s
co
m
p
ly
with
th
e
ME
PS
.
T
h
e
s
tan
d
ar
d
ec
o
n
o
m
etr
ic
m
o
d
el
d
o
es
n
o
t
ac
cu
r
ately
ex
p
lain
c
h
an
g
es
th
at
ar
e
d
r
iv
en
b
y
th
e
i
n
ter
co
n
n
ec
tin
g
ad
d
itio
n
al
f
ac
to
r
s
s
u
c
h
as
th
e
ef
f
ec
t
th
at
ME
PS
ap
p
lian
ce
s
ca
n
h
av
e
o
n
f
u
tu
r
e
en
er
g
y
co
n
s
u
m
p
tio
n
.
T
h
e
en
d
-
u
s
e
m
o
d
el
m
ea
n
wh
ile
allo
ws
th
ese
f
ac
to
r
s
to
b
e
co
n
s
id
er
e
d
an
d
ar
e
d
eter
m
in
is
tic
in
n
atu
r
e.
B
y
e
x
tr
ap
o
latin
g
th
e
g
ath
e
r
ed
E
E
m
ea
s
u
r
es d
ata
f
r
o
m
th
e
y
ea
r
s
2
0
1
3
to
2
0
1
5
,
(
2
)
was
ac
q
u
ir
ed
,
wh
er
e
y
is
th
e
en
er
g
y
s
av
in
g
s
f
r
o
m
th
e
E
E
m
e
asu
r
es
an
d
x
is
th
e
y
ea
r
wh
e
r
e
it
p
r
o
v
id
ed
a
f
o
r
ec
asted
r
esu
lt
u
p
to
2
0
4
0
.
T
h
e
r
e
g
r
ess
io
n
h
as
an
R
-
s
q
u
a
r
ed
v
alu
e
o
f
0
.
9
8
4
7
,
wh
ich
in
d
icate
s
a
g
o
o
d
co
r
r
el
atio
n
.
T
o
ad
d
,
th
e
f
o
r
ec
asted
tr
en
d
is
a
s
tead
y
in
clin
e
in
th
e
im
m
ed
iate
f
u
tu
r
e
[
1
8
]
–
[
2
0
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ad
v
Ap
p
l Sci
I
SS
N:
2252
-
8
8
1
4
Hyb
r
id
lo
a
d
fo
r
ec
a
s
tin
g
c
o
n
s
id
erin
g
en
erg
y
efficien
cy
a
n
d
r
en
ewa
b
le
…
(
A
d
r
ia
n
a
Ha
z
iq
a
h
Mo
h
d
A
iz
a
m
)
767
=
253
.
23
+
1966
.
4
(
2
)
2.
3
.
2
.
Renew
a
ble
ener
g
y
po
s
t
-
estim
a
t
io
n
m
o
del
Po
s
t
-
esti
m
atio
n
m
o
d
el
is
t
h
en
im
p
lem
en
ted
to
e
n
s
u
r
e
f
o
r
ec
as
ted
r
esu
lt
wo
u
ld
m
o
r
e
ac
cu
r
ately
r
ef
lect
th
e
cu
r
r
en
t
s
itu
atio
n
.
I
n
th
is
ca
s
e,
th
e
p
o
s
t
-
esti
m
atio
n
p
r
o
ce
s
s
co
n
s
is
t
s
o
f
all
R
E
p
r
o
jects
im
p
lem
en
ted
.
An
ag
g
r
eg
atio
n
o
f
all
s
o
u
r
ce
s
o
f
RE
d
ata
th
at
was
m
ad
e
av
ailab
le
v
ia
th
e
NE
B
an
d
th
e
M
E
I
H
w
as
tak
en
in
to
co
n
s
id
er
atio
n
i
n
co
n
s
tr
u
ct
in
g
th
e
p
o
s
t
-
esti
m
atio
n
m
o
d
el.
Fro
m
th
ese
d
ata
,
we
ca
n
also
s
u
r
m
is
e
th
at
th
e
p
o
licy
an
d
r
eg
u
latio
n
s
s
u
ch
as
th
e
n
et
en
er
g
y
m
ete
r
in
g
(
NE
M)
,
f
ee
d
-
in
tar
if
f
,
an
d
th
e
s
elf
-
co
n
s
u
m
p
tio
n
(
SelC
o
)
s
ch
em
e
ar
e
i
n
clu
d
ed
[
2
1
]
–
[
2
3
]
.
Usi
n
g
th
e
g
ath
er
ed
d
ata
f
r
o
m
2
0
1
0
t
o
2
0
1
9
,
an
ex
p
o
n
en
tial
tr
en
d
was
o
b
s
er
v
ed
as
s
h
o
w
n
in
(
3
)
,
wh
er
e
y
is
th
e
to
tal
o
f
th
e
R
E
g
e
n
er
atio
n
in
Ma
lay
s
ia
an
d
x
is
th
e
y
ea
r
.
Fr
o
m
th
is
,
th
e
ap
p
r
o
x
im
atio
n
o
f
th
e
s
u
b
s
eq
u
en
t
d
ata
f
r
o
m
2
0
2
0
to
2
0
4
0
w
as
ac
q
u
ir
ed
u
s
in
g
th
e
eq
u
a
tio
n
.
T
h
e
f
itted
lin
e
also
u
n
d
e
r
wen
t
an
R
-
s
q
u
ar
e
d
test
,
wh
er
e
it
was
s
ee
n
th
at
th
e
r
esu
lts
wer
e
0
.
9
7
4
6
in
d
icatin
g
th
at
t
h
e
ap
p
r
o
x
im
atio
n
w
as
h
ig
h
ly
r
eli
ab
le.
=
2
×
10
7
0
.
0567
(
3
)
2.
3
.
3
.
Adj
us
t
ed
lo
a
d
-
f
o
re
ca
s
t
ing
m
o
del
T
h
e
eq
u
atio
n
u
s
ed
f
o
r
th
e
ad
j
u
s
ted
lo
ad
-
f
o
r
ec
asti
n
g
m
o
d
el
i
s
s
h
o
wn
in
(
4
)
.
T
h
e
d
ata
in
v
o
l
v
ed
in
th
is
p
r
o
ce
s
s
ar
e
th
e
ANN
f
o
r
ec
a
s
ted
o
u
tp
u
t,
EE
,
a
n
d
RE
m
ea
s
u
r
es
f
r
o
m
th
e
y
ea
r
s
2
0
1
9
to
2
0
4
0
.
Fu
r
th
e
r
ad
ju
s
tm
en
ts
ar
e
m
ad
e
to
b
o
th
t
h
e
E
E
an
d
R
E
m
ea
s
u
r
es
,
wh
er
e
ea
ch
d
ata
f
r
o
m
ea
ch
y
ea
r
is
m
u
ltip
lied
with
th
e
f
ac
to
r
0
.
1
an
d
0
.
0
5
r
esp
ec
t
iv
ely
.
T
h
ese
m
u
ltip
licatio
n
f
a
cto
r
s
s
y
m
b
o
lize
t
h
e
p
er
ce
n
ta
g
e
o
f
r
e
d
u
ctio
n
in
elec
tr
icity
co
n
s
u
m
p
tio
n
.
I
n
th
is
ca
s
e,
an
ass
u
m
p
tio
n
was
m
ad
e
th
at
EE
m
ea
s
u
r
es
b
ein
g
i
m
p
lem
en
ted
wo
u
ld
r
ed
u
ce
th
e
o
v
e
r
all
elec
tr
icity
co
n
s
u
m
p
tio
n
b
y
1
0
%
wh
ile
RE
m
ea
s
u
r
es
wo
u
ld
y
i
eld
a
5
%
r
ed
u
ctio
n
in
o
v
e
r
all
elec
tr
icity
co
n
s
u
m
p
tio
n
.
Stu
d
ies
in
[
2
4
]
,
an
d
[
2
5
]
h
av
e
s
h
o
wn
th
at
b
o
t
h
m
ea
s
u
r
es
ca
n
y
ield
a
h
i
g
h
er
p
er
ce
n
tag
e
o
f
r
ed
u
ctio
n
in
en
er
g
y
co
n
s
u
m
p
tio
n
,
b
u
t
th
i
s
s
tu
d
y
ch
o
s
e
to
em
p
lo
y
a
m
o
r
e
co
n
s
er
v
ativ
e
esti
m
atio
n
o
f
5
a
n
d
1
0
% r
ed
u
c
tio
n
b
y
RE
an
d
EE
m
ea
s
u
r
es r
esp
ec
tiv
ely
.
=
−
(
×
0
.
1
)
−
(
×
0
.
05
)
(
4
)
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
3
.
1
.
Art
if
ici
a
l neura
l net
wo
rk
-
ba
s
ed
ec
o
no
m
et
ric
m
o
del
As
h
as
b
ee
n
s
h
o
wn
i
n
th
e
p
r
e
v
io
u
s
s
ec
tio
n
,
th
e
test
s
h
av
e
s
h
o
wn
th
at
all
t
h
e
v
ar
ia
b
les
h
a
v
e
s
tr
o
n
g
co
r
r
elatio
n
s
with
ea
ch
o
th
er
.
T
h
er
ef
o
r
e
,
in
co
n
s
tr
u
ctin
g
th
e
m
o
d
el,
th
e
d
ata
wer
e
s
ep
ar
ated
in
to
two
ca
teg
o
r
ies,
test
in
g
d
ata
an
d
tr
ain
in
g
d
ata.
Data
f
r
o
m
1
9
8
0
t
o
2
0
2
0
ac
te
d
as
th
e
tr
ain
in
g
d
ata
wh
ile
th
e
d
ata
f
r
o
m
2
0
2
1
to
2
0
4
0
ac
te
d
as
th
e
test
in
g
d
ata.
B
y
r
ef
er
r
in
g
t
o
Fig
u
r
e
3
,
it
was
s
ee
n
th
at
th
e
o
v
er
all
v
alu
e
o
f
R
f
o
r
tr
ain
in
g
,
test
in
g
,
an
d
v
alid
atio
n
test
s
i
s
0
.
9
9
9
6
8
.
T
h
e
co
r
r
elatio
n
co
ef
f
icie
n
t,
R
is
v
er
y
clo
s
e
to
1
th
er
ef
o
r
e
we
ca
n
s
u
r
m
is
e
a
s
ig
n
if
ican
t d
eg
r
ee
o
f
co
r
r
elatio
n
b
etw
ee
n
th
e
i
n
d
ep
en
d
en
t
v
ar
iab
les
an
d
th
e
d
ep
en
d
en
t
v
ar
iab
les.
T
h
e
te
s
tin
g
d
ata
ac
t
as
a
b
en
ch
m
ar
k
to
e
v
alu
ate
th
e
n
et
wo
r
k
’
s
p
er
f
o
r
m
a
n
ce
.
T
h
e
R
-
s
q
u
ar
ed
v
alu
e
o
f
th
e
ANN
m
o
d
el
is
th
en
ev
alu
ated
b
y
co
m
p
ar
in
g
it
with
th
e
R
-
s
q
u
ar
ed
v
alu
e
o
f
th
e
r
eg
r
ess
io
n
an
aly
s
is
d
o
n
e
o
n
th
e
E
x
ce
l
T
o
o
lPak
ap
p
licatio
n
.
T
h
e
R
-
s
q
u
ar
ed
v
alu
e
o
f
th
e
ANN
m
o
d
el
is
0
.
9
9
9
6
8
w
h
ile
th
e
R
-
s
q
u
ar
ed
v
alu
e
o
f
th
e
E
x
ce
l
T
o
o
lPak
r
eg
r
ess
io
n
an
aly
s
is
is
0
.
9
9
6
2
.
B
y
th
ese,
it
is
in
d
icate
d
th
at
th
e
ANN’
s
f
o
r
ec
asted
o
u
tp
u
t
s
h
o
ws
a
s
tr
o
n
g
e
r
co
r
r
elatio
n
b
e
twee
n
th
e
v
ar
ia
b
les
b
ein
g
an
al
y
z
ed
,
s
u
g
g
esti
n
g
th
at
p
er
h
ap
s
it
h
as
a
clo
s
er
f
it
to
th
e
d
ata
p
o
in
ts
u
s
ed
f
o
r
tr
ain
in
g
an
d
test
in
g
.
I
t
m
ay
also
s
u
g
g
est
th
at
th
e
A
NN
m
o
d
el
m
ay
h
av
e
u
tili
z
ed
ad
d
itio
n
al
v
ar
iab
les
o
r
em
p
l
o
y
ed
a
m
o
r
e
s
o
p
h
is
ticated
alg
o
r
ith
m
th
at
en
a
b
les
it
to
u
n
co
v
er
h
id
d
e
n
p
at
ter
n
s
o
r
n
o
n
lin
ea
r
r
elatio
n
s
h
ip
s
th
at
t
h
e
E
x
ce
l
T
o
o
lPak
ap
p
licatio
n
’
s
r
e
g
r
ess
io
n
an
aly
s
is
o
r
th
e
ANO
VA
test
co
u
ld
n
o
t
ac
cu
r
ately
ca
p
tu
r
e.
Nex
t,
Fig
u
r
e
4
d
ep
icts
th
e
g
r
a
p
h
o
f
th
e
ac
tu
al
v
e
r
s
u
s
th
e
f
o
r
ec
asted
v
alu
e
o
f
th
e
m
o
d
el.
Fr
o
m
it
,
o
n
e
c
an
s
ee
th
at
th
e
f
o
r
ec
asted
v
al
u
e
f
o
llo
ws
alo
n
g
m
o
r
e
o
r
less
a
s
im
ilar
s
lo
p
e
o
f
th
e
g
r
a
p
h
a
s
th
e
ac
tu
al
v
alu
e
.
Me
an
wh
ile,
af
ter
th
e
tr
ai
n
in
g
an
d
test
in
g
o
f
t
h
e
d
ata
h
a
d
b
e
en
d
o
n
e,
r
esid
u
al
an
aly
s
is
was
ca
r
r
ied
o
u
t
to
test
th
e
v
alid
ity
o
f
t
h
e
r
esu
lts
.
I
n
Fig
u
r
e
5
,
wh
ich
d
ep
icts
th
e
s
ca
tter
p
lo
t
o
f
th
e
r
esu
lts
.
F
it
ca
n
b
e
s
ee
n
th
at
th
e
s
m
all
d
is
p
er
s
io
n
tr
en
d
o
f
th
e
g
r
ap
h
m
ea
n
s
th
at
th
e
d
ata
p
o
i
n
ts
h
av
e
a
b
etter
f
it
to
th
e
h
o
r
izo
n
tal
s
tr
aig
h
t
lin
e.
T
h
is
r
ein
f
o
r
ce
s
th
e
h
y
p
o
th
esis
th
at
v
ar
iab
les
h
av
e
a
s
tr
o
n
g
lin
ea
r
r
elatio
n
s
h
i
p
.
T
h
e
f
a
ct
th
at
th
er
e
is
n
o
s
ig
n
if
ican
t d
ev
iatio
n
t
o
th
e
p
l
o
t a
ls
o
m
ea
n
s
th
at
th
e
d
ata
ca
n
b
e
ass
u
m
ed
to
b
e
ac
cu
r
ate.
Nex
t,
Fig
u
r
e
6
s
h
o
ws
th
e
h
is
to
g
r
am
p
lo
t
o
f
t
h
e
r
esu
lt.
B
y
i
n
ter
p
r
etin
g
th
is
d
ata,
th
e
d
is
tr
ib
u
tio
n
is
s
k
ewe
d
to
th
e
lef
t
wh
ich
in
d
i
ca
tes
th
at
m
o
s
t
o
f
th
e
f
o
r
ec
asted
d
ata
ar
e
co
n
ce
n
tr
ated
to
w
ar
d
s
h
ig
h
er
lev
el
s
with
a
lo
n
g
er
tail
to
war
d
s
lo
wer
v
alu
es.
Fro
m
th
is
,
an
in
ter
p
r
etatio
n
was
m
ad
e
th
at
th
e
r
e
ar
e
r
elativ
ely
f
ew
in
s
tan
ce
s
o
f
lo
wer
elec
tr
icity
alth
o
u
g
h
t
h
er
e
is
o
n
e
o
u
tlier
in
th
e
r
ig
h
t th
at
s
u
g
g
est
s
th
e
ex
is
ten
ce
o
f
a
f
ew
d
at
a
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
8
1
4
I
n
t J Ad
v
Ap
p
l Sci
,
Vo
l.
1
3
,
No
.
4
,
Dec
em
b
er
2
0
2
4
:
763
-
7
7
2
768
p
o
in
ts
with
ex
ce
p
tio
n
ally
h
ig
h
elec
tr
icity
co
n
s
u
m
p
tio
n
v
al
u
es
th
at
f
all
f
ar
o
u
ts
id
e
th
e
ty
p
ical
p
atter
n
o
f
th
e
d
is
tr
ib
u
tio
n
.
T
h
is
p
h
en
o
m
e
n
o
n
m
ig
h
t
b
e
attr
ib
u
te
d
to
ex
t
r
em
e
o
r
o
u
tl
ier
o
b
s
er
v
atio
n
s
.
Ov
er
all,
t
h
e
lef
t
-
s
k
ewe
d
d
is
tr
ib
u
tio
n
in
d
icate
s
th
at
th
er
e
is
a
ten
d
en
cy
f
o
r
a
h
ig
h
er
co
n
s
u
m
p
tio
n
lev
el
with
a
lim
ited
n
u
m
b
er
o
f
in
s
tan
ce
s
o
f
lo
wer
co
n
s
u
m
p
tio
n
v
alu
es
with
f
ew
e
x
ce
p
t
io
n
al
in
s
tan
ce
s
o
f
a
b
n
o
r
m
ally
h
ig
h
elec
tr
icity
co
n
s
u
m
p
ti
o
n
as r
ep
r
esen
ted
b
y
th
e
o
u
tlier
th
at
s
k
ewe
d
to
th
e
r
ig
h
t.
Fig
u
r
e
3
.
Netwo
r
k
r
esu
lts
Fig
u
r
e
4
.
Actu
al
v
er
s
u
s
f
o
r
ec
a
s
ted
v
alu
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ad
v
Ap
p
l Sci
I
SS
N:
2252
-
8
8
1
4
Hyb
r
id
lo
a
d
fo
r
ec
a
s
tin
g
c
o
n
s
id
erin
g
en
erg
y
efficien
cy
a
n
d
r
en
ewa
b
le
…
(
A
d
r
ia
n
a
Ha
z
iq
a
h
Mo
h
d
A
iz
a
m
)
769
Fig
u
r
e
5
.
Scatter
p
l
o
t
Fig
u
r
e
6
.
His
to
g
r
am
p
lo
t
3.
2
.
H
y
brid
lo
a
d f
o
re
ca
s
t
ing
m
o
del
Fig
u
r
e
7
s
h
o
ws
th
e
ad
ju
s
ted
co
n
s
u
m
p
tio
n
b
ased
o
n
th
e
h
y
b
r
id
lo
ad
f
o
r
ec
asti
n
g
s
y
s
tem
in
MA
T
L
AB
.
Fro
m
th
e
f
ig
u
r
e,
a
r
ed
u
ctio
n
o
f
2
,
0
0
0
k
to
e
in
e
n
er
g
y
co
n
s
u
m
p
tio
n
ca
n
b
e
o
b
s
er
v
ed
.
T
h
e
s
u
m
m
atio
n
o
f
f
o
r
ec
asts
f
r
o
m
th
e
ec
o
n
o
m
etr
i
c
m
o
d
els
h
a
s
cr
ea
ted
th
e
ag
g
r
eg
atio
n
o
f
th
e
b
aselin
e
f
o
r
ec
a
s
t
o
f
th
e
elec
tr
icity
co
n
s
u
m
p
tio
n
(
Fig
u
r
e
7
(
a)
)
.
T
h
e
lin
e
ch
a
r
t
s
h
o
ws
th
at
a
d
ec
lin
e
in
elec
tr
icity
co
n
s
u
m
p
tio
n
s
tar
ted
ar
o
u
n
d
2
0
1
9
an
d
will
co
n
tin
u
e
t
o
d
ec
lin
e
u
n
til
2
0
4
0
b
ased
o
n
o
u
r
h
y
b
r
id
lo
ad
f
o
r
ec
asti
n
g
tec
h
n
iq
u
e
(
Fig
u
r
e
7
(
b
)
)
.
Fu
r
th
e
r
ca
lcu
l
atio
n
s
wer
e
d
o
n
e
an
d
th
e
av
er
ag
e
f
o
r
th
e
ad
j
u
s
ted
co
n
s
u
m
p
tio
n
an
d
ac
tu
al
co
n
s
u
m
p
t
io
n
ar
e
2
8
,
5
7
9
6
.
4
6
an
d
2
4
,
6
6
7
1
.
3
9
W
r
esp
ec
tiv
el
y
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
8
1
4
I
n
t J Ad
v
Ap
p
l Sci
,
Vo
l.
1
3
,
No
.
4
,
Dec
em
b
er
2
0
2
4
:
763
-
7
7
2
770
(
a)
(
b
)
Fig
u
r
e
7
.
E
lectr
icity
c
o
n
s
u
m
p
t
io
n
b
ased
o
n
a
h
y
b
r
id
lo
ad
f
o
r
ec
asti
n
g
s
y
s
tem
in
(
a)
ac
tu
al
v
s
f
o
r
ec
asted
an
d
(b
)
o
v
er
all
ad
ju
s
ted
elec
tr
icity
co
n
s
u
m
p
tio
n
4.
CO
NCLU
SI
O
N
T
h
e
two
m
ain
o
b
jectiv
es
o
f
t
h
is
s
tu
d
y
ar
e
to
d
ev
el
o
p
a
m
o
d
el
th
at
p
r
ed
icts
elec
tr
icity
co
n
s
u
m
p
tio
n
th
at
co
n
s
id
er
s
th
e
ef
f
ec
t
th
at
EE
an
d
RE
m
ea
s
u
r
e
wh
ile
an
aly
z
in
g
th
e
ef
f
ec
t
th
at
b
o
th
m
ea
s
u
r
es
ca
n
h
a
v
e
o
n
elec
tr
icity
co
n
s
u
m
p
tio
n
.
B
y
an
aly
z
in
g
th
e
r
esu
lt
o
f
th
is
s
tu
d
y
,
it
ca
n
b
e
co
n
clu
d
ed
th
at
b
o
th
m
ea
s
u
r
es
in
f
lu
en
ce
elec
tr
icity
co
n
s
u
m
p
t
io
n
b
y
r
e
d
u
cin
g
th
e
d
em
an
d
an
d
th
u
s
p
r
o
v
in
g
th
at
in
th
e
f
u
t
u
r
e
th
er
e
will
b
e
a
wid
er
tr
en
d
o
f
d
ec
o
u
p
lin
g
b
etwe
en
ec
o
n
o
m
ic
g
r
o
wth
th
a
t
is
s
y
m
b
o
lized
b
y
th
e
GDP
p
er
ca
p
ita
an
d
th
e
o
v
er
all
elec
tr
icity
c
o
n
s
u
m
p
ti
o
n
.
A
f
u
r
th
e
r
co
n
clu
s
io
n
ca
n
b
e
m
a
d
e
th
at
t
h
e
d
e
p
en
d
e
n
t
v
ar
iab
le
h
as
s
o
m
e
co
r
r
elatio
n
with
th
e
i
n
d
ep
e
n
d
en
t
v
ar
iab
le
b
y
lo
o
k
in
g
at
th
e
ANN
m
o
d
el
an
d
th
e
h
y
b
r
id
lo
ad
f
o
r
ec
asti
n
g
m
o
d
el
th
at
p
r
ec
ed
es
it.
Ho
wev
er
,
s
o
m
e
r
ec
o
m
m
e
n
d
ati
o
n
s
ca
n
also
b
e
m
ad
e.
Sin
ce
th
is
s
tu
d
y
in
v
o
lv
es
v
ar
iab
les
s
u
ch
as
p
o
p
u
latio
n
a
n
d
GDP
p
er
ca
p
ita,
i
n
th
e
f
u
t
u
r
e
m
o
r
e
v
ar
iab
les
s
h
o
u
l
d
b
e
co
n
s
id
er
ed
as
b
o
th
v
ar
iab
les
h
av
e
a
wid
e
b
r
ea
d
th
o
f
f
ac
to
r
s
th
at
ca
n
in
f
lu
en
ce
t
h
em
s
u
ch
as
th
e
f
lo
w
o
f
ca
p
it
al
in
to
th
e
co
u
n
tr
y
,
h
u
m
an
ca
p
ital,
in
f
latio
n
o
r
to
g
iv
e
a
r
ec
en
t
ex
am
p
le,
t
h
e
p
a
n
d
em
ic
C
OVI
D
-
19
.
E
x
ter
n
al
an
d
u
n
co
n
tr
o
llab
le
f
ac
to
r
s
lik
e
th
ese
ca
n
s
o
m
etim
es
h
av
e
a
d
o
m
in
o
ef
f
ec
t
o
n
h
o
w
we
b
eh
av
e
wh
ich
ca
n
in
d
ir
e
ctly
af
f
ec
t
h
o
w
we
u
s
e
an
d
co
n
s
u
m
e
en
er
g
y
.
ACK
NO
WL
E
D
G
E
M
E
NT
W
e
wo
u
ld
lik
e
to
ex
ten
d
o
u
r
s
in
ce
r
e
ap
p
r
ec
iatio
n
to
Un
iv
er
s
iti
T
ek
n
o
lo
g
i
MA
R
A
(
UiT
M)
f
o
r
p
r
o
v
id
i
n
g
u
s
with
th
eir
s
ite
as a
s
tu
d
y
lo
ca
tio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ad
v
Ap
p
l Sci
I
SS
N:
2252
-
8
8
1
4
Hyb
r
id
lo
a
d
fo
r
ec
a
s
tin
g
c
o
n
s
id
erin
g
en
erg
y
efficien
cy
a
n
d
r
en
ewa
b
le
…
(
A
d
r
ia
n
a
Ha
z
iq
a
h
Mo
h
d
A
iz
a
m
)
771
RE
F
E
R
E
NC
E
S
[
1
]
N
.
A
.
L
u
d
i
n
,
H
.
P
h
o
u
mi
n
,
F
.
S
.
M
.
C
h
a
c
h
u
l
i
,
a
n
d
N
.
H
.
H
a
m
i
d
,
“
S
u
st
a
i
n
a
b
l
e
e
n
e
r
g
y
p
o
l
i
c
y
r
e
f
o
r
m
i
n
M
a
l
a
y
s
i
a
,
”
i
n
R
e
v
i
s
i
t
i
n
g
El
e
c
t
r
i
c
i
t
y
M
a
rk
e
t
Re
f
o
rm
s
,
S
i
n
g
a
p
o
r
e
:
S
p
r
i
n
g
e
r
N
a
t
u
r
e
S
i
n
g
a
p
o
r
e
,
2
0
2
2
,
p
p
.
2
5
1
–
2
8
1
.
[
2
]
D
e
p
a
r
t
me
n
t
o
f
Ec
o
n
o
m
i
c
a
n
d
S
o
c
i
a
l
A
f
f
a
i
r
s,
“
T
h
e
1
7
g
o
a
l
s
,
”
U
n
i
t
e
d
N
a
t
i
o
n
,
2
0
1
5
.
[
O
n
l
i
n
e
]
,
A
v
a
i
l
a
b
l
e
a
t
h
t
t
p
s:
/
/
sd
g
s
.
u
n
.
o
r
g
/
g
o
a
l
s
(
a
c
c
e
s
se
d
M
a
r
.
1
8
,
2
0
2
4
)
.
[
3
]
V
.
G
.
R
.
C
h
a
n
d
r
a
n
,
S
.
S
h
a
r
ma
,
a
n
d
K
.
M
a
d
h
a
v
a
n
,
“
El
e
c
t
r
i
c
i
t
y
c
o
n
s
u
m
p
t
i
o
n
–
g
r
o
w
t
h
n
e
x
u
s:
t
h
e
c
a
se
o
f
M
a
l
a
y
s
i
a
,
”
E
n
e
r
g
y
P
o
l
i
c
y
,
v
o
l
.
3
8
,
n
o
.
1
,
p
p
.
6
0
6
–
6
1
2
,
J
a
n
.
2
0
1
0
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
n
p
o
l
.
2
0
0
9
.
1
0
.
0
1
3
.
[
4
]
I
.
O
z
t
u
r
k
a
n
d
A
.
A
c
a
r
a
v
c
i
,
“
C
O
2
e
m
i
ssi
o
n
s,
e
n
e
r
g
y
c
o
n
s
u
m
p
t
i
o
n
a
n
d
e
c
o
n
o
mi
c
g
r
o
w
t
h
i
n
Tu
r
k
e
y
,
”
Re
n
e
w
a
b
l
e
a
n
d
S
u
s
t
a
i
n
a
b
l
e
En
e
r
g
y
Re
v
i
e
w
s
,
v
o
l
.
1
4
,
n
o
.
9
,
p
p
.
3
2
2
0
–
3
2
2
5
,
D
e
c
.
2
0
1
0
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
r
s
e
r
.
2
0
1
0
.
0
7
.
0
0
5
.
[
5
]
H
.
Li
a
o
,
Y
.
P
e
n
g
,
F
.
-
Z
.
W
a
n
g
,
a
n
d
T
.
Z
h
a
n
g
,
“
U
n
d
e
r
st
a
n
d
i
n
g
e
n
e
r
g
y
u
s
e
g
r
o
w
t
h
:
t
h
e
r
o
l
e
o
f
i
n
v
e
st
m
e
n
t
-
G
D
P
r
a
t
i
o
,
”
S
t
r
u
c
t
u
r
a
l
C
h
a
n
g
e
a
n
d
Ec
o
n
o
m
i
c
D
y
n
a
m
i
c
s
,
v
o
l
.
6
3
,
p
p
.
1
5
–
2
4
,
D
e
c
.
2
0
2
2
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
st
r
u
e
c
o
.
2
0
2
2
.
0
8
.
0
0
7
.
[
6
]
F
.
G
u
a
n
g
,
L.
W
e
n
,
a
n
d
B
.
S
h
a
r
p
,
“
En
e
r
g
y
e
f
f
i
c
i
e
n
c
y
i
m
p
r
o
v
e
me
n
t
s
a
n
d
i
n
d
u
s
t
r
y
t
r
a
n
si
t
i
o
n
:
a
n
a
n
a
l
y
s
i
s
o
f
C
h
i
n
a
’
s
e
l
e
c
t
r
i
c
i
t
y
c
o
n
su
mp
t
i
o
n
,
”
E
n
e
r
g
y
,
v
o
l
.
2
4
4
,
p
.
1
2
2
6
2
5
,
A
p
r
.
2
0
2
2
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
n
e
r
g
y
.
2
0
2
1
.
1
2
2
6
2
5
.
[
7
]
N
.
A
.
A
b
d
R
a
h
ma
n
,
S
.
N
.
K
a
mar
u
z
z
a
ma
n
,
a
n
d
F
.
W
.
A
k
a
s
h
a
h
,
“
S
c
e
n
a
r
i
o
a
n
d
s
t
r
a
t
e
g
y
t
o
w
a
r
d
s
e
n
e
r
g
y
e
f
f
i
c
i
e
n
c
y
i
n
M
a
l
a
y
s
i
a
:
a
r
e
v
i
e
w
,
”
MA
T
EC
We
b
o
f
C
o
n
f
e
re
n
c
e
s
,
v
o
l
.
2
6
6
,
p
.
0
2
0
1
2
,
F
e
b
.
2
0
1
9
,
d
o
i
:
1
0
.
1
0
5
1
/
m
a
t
e
c
c
o
n
f
/
2
0
1
9
2
6
6
0
2
0
1
2
.
[
8
]
C
.
Z
h
a
n
g
,
K
.
Zh
o
u
,
S
.
Y
a
n
g
,
a
n
d
Z.
S
h
a
o
,
“
O
n
e
l
e
c
t
r
i
c
i
t
y
c
o
n
s
u
m
p
t
i
o
n
a
n
d
e
c
o
n
o
mi
c
g
r
o
w
t
h
i
n
C
h
i
n
a
,
”
R
e
n
e
w
a
b
l
e
a
n
d
S
u
s
t
a
i
n
a
b
l
e
E
n
e
rg
y
R
e
v
i
e
w
s
,
v
o
l
.
7
6
,
p
p
.
3
5
3
–
3
6
8
,
S
e
p
.
2
0
1
7
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
r
ser.
2
0
1
7
.
0
3
.
0
7
1
.
[
9
]
B.
-
L.
Tr
a
n
,
C
.
-
C
.
C
h
e
n
,
a
n
d
W
.
-
C
.
T
s
e
n
g
,
“
C
a
u
sa
l
i
t
y
b
e
t
w
e
e
n
e
n
e
r
g
y
c
o
n
s
u
mp
t
i
o
n
a
n
d
e
c
o
n
o
mi
c
g
r
o
w
t
h
i
n
t
h
e
p
r
e
se
n
c
e
o
f
G
D
P
t
h
r
e
s
h
o
l
d
e
f
f
e
c
t
:
e
v
i
d
e
n
c
e
f
r
o
m O
EC
D
c
o
u
n
t
r
i
e
s,
”
E
n
e
r
g
y
,
v
o
l
.
2
5
1
,
p
.
1
2
3
9
0
2
,
Ju
l
.
2
0
2
2
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
n
e
r
g
y
.
2
0
2
2
.
1
2
3
9
0
2
.
[
1
0
]
I
.
M
a
u
l
e
ó
n
,
“
A
s
t
a
t
i
s
t
i
c
a
l
m
o
d
e
l
t
o
f
o
r
e
c
a
s
t
a
n
d
s
i
m
u
l
a
t
e
e
n
e
r
g
y
d
e
m
a
n
d
i
n
t
h
e
l
o
n
g
-
r
u
n
,
”
S
m
a
r
t
En
e
rg
y
,
v
o
l
.
7
,
p
.
1
0
0
0
8
4
,
A
u
g
.
2
0
2
2
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
se
g
y
.
2
0
2
2
.
1
0
0
0
8
4
.
[
1
1
]
D
.
A
l
i
,
M
.
Y
o
h
a
n
n
a
,
M
.
I
.
P
u
w
u
,
a
n
d
B
.
M
.
G
a
r
k
i
d
a
,
“
L
o
n
g
-
t
e
r
m
l
o
a
d
f
o
r
e
c
a
st
m
o
d
e
l
l
i
n
g
u
si
n
g
a
f
u
z
z
y
l
o
g
i
c
a
p
p
r
o
a
c
h
,
”
P
a
c
i
f
i
c
S
c
i
e
n
c
e
Re
v
i
e
w
A:
N
a
t
u
r
a
l
S
c
i
e
n
c
e
a
n
d
E
n
g
i
n
e
e
ri
n
g
,
v
o
l
.
1
8
,
n
o
.
2
,
p
p
.
1
2
3
–
1
2
7
,
J
u
l
.
2
0
1
6
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
p
sr
a
.
2
0
1
6
.
0
9
.
0
1
1
.
[
1
2
]
T.
C
h
e
n
a
n
d
Y
.
-
C
.
W
a
n
g
,
“
Lo
n
g
-
t
e
r
m
l
o
a
d
f
o
r
e
c
a
st
i
n
g
b
y
a
c
o
l
l
a
b
o
r
a
t
i
v
e
f
u
z
z
y
-
n
e
u
r
a
l
a
p
p
r
o
a
c
h
,
”
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
Po
w
e
r
& E
n
e
rg
y
S
y
s
t
e
m
s
,
v
o
l
.
4
3
,
n
o
.
1
,
p
p
.
4
5
4
–
4
6
4
,
D
e
c
.
2
0
1
2
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
i
j
e
p
e
s.
2
0
1
2
.
0
5
.
0
7
2
.
[
1
3
]
R
.
W
.
D
i
m
a
n
d
a
n
d
B
.
J.
S
p
e
n
c
e
r
,
“
T
r
e
v
o
r
sw
a
n
a
n
d
t
h
e
n
e
o
c
l
a
ss
i
c
a
l
g
r
o
w
t
h
mo
d
e
l
,
”
H
i
s
t
o
r
y
o
f
P
o
l
i
t
i
c
a
l
Ec
o
n
o
m
y
,
v
o
l
.
4
1
,
n
o
.
S
u
p
p
l
_
1
,
p
p
.
1
0
7
–
1
2
6
,
D
e
c
.
2
0
0
9
,
d
o
i
:
1
0
.
1
2
1
5
/
0
0
1
8
2
7
0
2
-
2
0
0
9
-
0
1
9
.
[
14]
L.
-
P
.
R
o
c
h
o
n
a
n
d
S
.
R
o
ssi
,
A
m
o
d
e
r
n
g
u
i
d
e
t
o
re
t
h
i
n
k
i
n
g
e
c
o
n
o
m
i
c
s
.
Ed
w
a
r
d
E
l
g
a
r
P
u
b
l
i
s
h
i
n
g
,
2
0
1
7
.
[
1
5
]
A
.
M
.
I
sa,
S
.
F
.
S
a
l
l
e
h
,
a
n
d
M
.
E.
B
.
M
.
R
o
sl
a
n
,
“
Ev
a
l
u
a
t
i
n
g
t
h
e
i
m
p
a
c
t
o
f
i
mp
l
e
m
e
n
t
i
n
g
m
i
n
i
m
u
m
e
n
e
r
g
y
p
e
r
f
o
r
man
c
e
s
t
a
n
d
a
r
d
s
a
p
p
l
i
a
n
c
e
r
e
g
u
l
a
t
i
o
n
i
n
M
a
l
a
y
si
a
,
”
I
n
t
e
rn
a
t
i
o
n
a
l
J
o
u
rn
a
l
o
f
En
v
i
r
o
n
m
e
n
t
a
l
T
e
c
h
n
o
l
o
g
y
a
n
d
M
a
n
a
g
e
m
e
n
t
,
v
o
l
.
2
2
,
n
o
.
4
/
5
,
p
.
2
5
7
,
2
0
1
9
,
d
o
i
:
1
0
.
1
5
0
4
/
I
JETM
.
2
0
1
9
.
1
0
0
2
6
4
1
7
.
[
1
6
]
M
.
H
.
S
a
z
l
i
,
“
A
b
r
i
e
f
r
e
v
i
e
w
o
f
f
e
e
d
-
f
o
r
w
a
r
d
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
a
c
u
l
t
y
of
S
c
i
e
n
c
e
,
U
n
i
v
e
rsi
t
y
o
f
A
n
k
a
r
a
,
v
o
l
.
5
0
,
n
o
.
1
,
p
p
.
1
1
–
1
7
,
2
0
0
6
,
d
o
i
:
1
0
.
1
5
0
1
/
0
0
0
3
1
6
8
.
[
1
7
]
S
.
C
.
B
h
a
t
t
a
c
h
a
r
y
y
a
a
n
d
G
.
R
.
T
i
mi
l
s
i
n
a
,
“
E
n
e
r
g
y
d
e
ma
n
d
m
o
d
e
l
s
F
o
r
p
o
l
i
c
y
f
o
r
m
u
l
a
t
i
o
n
:
a
c
o
mp
a
r
a
t
i
v
e
s
t
u
d
y
o
f
e
n
e
r
g
y
d
e
m
a
n
d
mo
d
e
l
s
,
”
W
o
rl
d
B
a
n
k
Po
l
i
c
y
Re
s
e
a
r
c
h
W
o
r
k
i
n
g
P
a
p
e
r
,
n
o
.
4
8
6
6
,
M
a
r
.
2
0
0
9
,
d
o
i
:
1
0
.
1
5
9
6
/
1
8
1
3
-
9
4
5
0
-
4
8
6
6
.
[
1
8
]
P
.
E.
B
r
o
c
k
w
a
y
,
S
.
S
o
r
r
e
l
l
,
G
.
S
e
m
i
e
n
i
u
k
,
M
.
K
.
H
e
u
n
,
a
n
d
V
.
C
o
u
r
t
,
“
E
n
e
r
g
y
e
f
f
i
c
i
e
n
c
y
a
n
d
e
c
o
n
o
m
y
-
w
i
d
e
r
e
b
o
u
n
d
e
f
f
e
c
t
s
:
a
r
e
v
i
e
w
o
f
t
h
e
e
v
i
d
e
n
c
e
a
n
d
i
t
s
i
m
p
l
i
c
a
t
i
o
n
s,”
R
e
n
e
w
a
b
l
e
a
n
d
S
u
s
t
a
i
n
a
b
l
e
En
e
r
g
y
R
e
v
i
e
w
s
,
v
o
l
.
1
4
1
,
p
.
1
1
0
7
8
1
,
M
a
y
2
0
2
1
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
r
ser.
2
0
2
1
.
1
1
0
7
8
1
.
[
1
9
]
E.
W
o
r
r
e
l
l
,
L.
B
e
r
n
s
t
e
i
n
,
J
.
R
o
y
,
L.
P
r
i
c
e
,
a
n
d
J
.
H
a
r
n
i
s
c
h
,
“
I
n
d
u
st
r
i
a
l
e
n
e
r
g
y
e
f
f
i
c
i
e
n
c
y
a
n
d
c
l
i
ma
t
e
c
h
a
n
g
e
m
i
t
i
g
a
t
i
o
n
,
”
E
n
e
r
g
y
Ef
f
i
c
i
e
n
c
y
,
v
o
l
.
2
,
n
o
.
2
,
p
p
.
1
0
9
–
1
2
3
,
M
a
y
2
0
0
9
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
2
0
5
3
-
0
0
8
-
9
0
3
2
-
8.
[
2
0
]
A
.
Ü
n
l
e
r
,
“
I
mp
r
o
v
e
m
e
n
t
o
f
e
n
e
r
g
y
d
e
ma
n
d
f
o
r
e
c
a
s
t
s
u
si
n
g
sw
a
r
m
i
n
t
e
l
l
i
g
e
n
c
e
:
t
h
e
c
a
s
e
o
f
T
u
r
k
e
y
w
i
t
h
p
r
o
j
e
c
t
i
o
n
s
t
o
2
0
2
5
,
”
En
e
r
g
y
P
o
l
i
c
y
,
v
o
l
.
3
6
,
n
o
.
6
,
p
p
.
1
9
3
7
–
1
9
4
4
,
Ju
n
.
2
0
0
8
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
n
p
o
l
.
2
0
0
8
.
0
2
.
0
1
8
.
[
2
1
]
P
.
M
o
r
i
a
r
t
y
a
n
d
D
.
H
o
n
n
e
r
y
,
“
W
h
a
t
i
s
t
h
e
g
l
o
b
a
l
p
o
t
e
n
t
i
a
l
f
o
r
r
e
n
e
w
a
b
l
e
e
n
e
r
g
y
?
,”
R
e
n
e
w
a
b
l
e
a
n
d
S
u
st
a
i
n
a
b
l
e
En
e
r
g
y
Re
v
i
e
w
s
,
v
o
l
.
1
6
,
n
o
.
1
,
p
p
.
2
4
4
–
2
5
2
,
J
a
n
.
2
0
1
2
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
r
ser.
2
0
1
1
.
0
7
.
1
5
1
.
[
2
2
]
K
.
S
o
l
a
u
n
a
n
d
E.
C
e
r
d
á
,
“
C
l
i
m
a
t
e
c
h
a
n
g
e
i
m
p
a
c
t
s
o
n
r
e
n
e
w
a
b
l
e
e
n
e
r
g
y
g
e
n
e
r
a
t
i
o
n
.
A
r
e
v
i
e
w
o
f
q
u
a
n
t
i
t
a
t
i
v
e
p
r
o
j
e
c
t
i
o
n
s,”
Re
n
e
w
a
b
l
e
a
n
d
S
u
st
a
i
n
a
b
l
e
En
e
rg
y
R
e
v
i
e
w
s
,
v
o
l
.
1
1
6
,
p
.
1
0
9
4
1
5
,
D
e
c
.
2
0
1
9
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
r
ser.
2
0
1
9
.
1
0
9
4
1
5
.
[
2
3
]
M
.
M
e
t
a
y
e
r
,
C
.
B
r
e
y
e
r
,
a
n
d
H
.
-
J.
F
e
l
l
,
“
T
h
e
p
r
o
j
e
c
t
i
o
n
s
f
o
r
t
h
e
f
u
t
u
r
e
a
n
d
q
u
a
l
i
t
y
i
n
t
h
e
p
a
st
o
f
t
h
e
w
o
r
l
d
e
n
e
r
g
y
o
u
t
l
o
o
k
f
o
r
s
o
l
a
r
P
V
a
n
d
o
t
h
e
r
R
E
t
e
c
h
n
o
l
o
g
i
e
s,”
i
n
3
1
st
E
u
r
o
p
e
a
n
PV
S
o
l
a
r E
n
e
r
g
y
C
o
n
f
e
r
e
n
c
e
a
n
d
Ex
h
i
b
i
t
i
o
n
,
2
0
1
5
,
v
o
l
.
5
.
[
2
4
]
K
.
La
n
e
,
“
A
c
h
i
e
v
e
me
n
t
s
o
f
e
n
e
r
g
y
e
f
f
i
c
i
e
n
c
y
a
p
p
l
i
a
n
c
e
a
n
d
e
q
u
i
p
m
e
n
t
st
a
n
d
a
r
d
s
a
n
d
l
a
b
e
l
l
i
n
g
p
r
o
g
r
a
mm
e
s,”
I
n
t
e
rn
a
t
i
o
n
a
l
En
e
r
g
y
A
g
e
n
c
y
,
2
0
2
1
,
[
O
n
l
i
n
e
]
.
A
v
a
i
l
a
b
l
e
:
h
t
t
p
s:
/
/
i
e
a
.
b
l
o
b
.
c
o
r
e
.
w
i
n
d
o
w
s.
n
e
t
/
a
sse
t
s/
9
9
6
e
a
4
0
e
-
e
0
1
0
-
4
8
c
3
-
a
b
5
3
-
9
b
4
f
7
2
d
d
c
8
1
5
/
A
c
h
i
e
v
e
m
e
n
t
s
o
f
En
e
r
g
y
Ef
f
i
c
i
e
n
c
y
A
p
p
l
i
a
n
c
e
a
n
d
E
q
u
i
p
me
n
t
S
t
a
n
d
a
r
d
s
a
n
d
L
a
b
e
l
l
i
n
g
P
r
o
g
r
a
mm
e
s
.
p
d
f
.
[
2
5
]
I
n
t
e
r
n
a
t
i
o
n
a
l
E
n
e
r
g
y
A
g
e
n
c
y
,
“
A
ss
e
si
n
g
t
h
e
e
f
f
e
c
t
s
o
f
e
c
o
n
o
m
i
c
r
e
c
o
v
e
r
i
e
s
o
n
g
l
o
b
a
l
e
n
e
r
g
y
d
e
ma
n
d
a
n
d
C
O
2
e
m
i
ssi
o
n
s
i
n
2
0
2
1
,
”
G
l
o
b
a
l
E
n
e
r
g
y
Re
v
o
l
u
t
i
o
n
,
p
p
.
1
–
3
2
,
2
0
2
1
.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
Adria
n
a
H
a
z
iq
a
h
Mo
h
d
Aiz
a
m
is
a
re
c
e
n
t
g
ra
d
u
a
te
o
f
B.
En
g
.
(H
o
n
s)
i
n
El
e
c
tri
c
a
l
En
g
in
e
e
ri
n
g
fr
o
m
Un
iv
e
rsiti
Tek
n
o
lo
g
i
M
a
ra
(UiTM
)
S
h
a
h
Ala
m
.
Cu
rre
n
tl
y
,
sh
e
is
wo
rk
i
n
g
a
s
a
n
Ex
e
c
u
ti
v
e
a
t
M
a
lay
sia
G
re
e
n
Tec
h
n
o
lo
g
y
C
o
rp
o
ra
ti
o
n
(M
G
TC)
o
n
G
re
e
n
In
c
e
n
ti
v
e
s.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
sa
d
rian
a
.
h
z
@g
m
a
il
.
c
o
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
8
1
4
I
n
t J Ad
v
Ap
p
l Sci
,
Vo
l.
1
3
,
No
.
4
,
Dec
em
b
er
2
0
2
4
:
763
-
7
7
2
772
No
fr
i
Ye
n
ita
Da
h
l
a
n
is
a
n
As
so
c
iate
P
ro
fe
ss
o
r
in
th
e
S
c
h
o
o
l
o
f
El
e
c
tri
c
a
l
En
g
i
n
e
e
rin
g
,
Co
l
leg
e
o
f
En
g
in
e
e
rin
g
,
U
n
iv
e
rsiti
Tek
n
o
l
o
g
i
M
ARA
(UiTM
)
S
h
a
h
Ala
m
,
M
a
lay
sia
.
Cu
rre
n
tl
y
,
s
h
e
se
rv
e
s
a
s
th
e
Dire
c
to
r
o
f
UiT
M
S
o
lar
Re
se
a
rc
h
In
stit
u
te
(S
RI).
S
h
e
re
c
e
iv
e
d
a
n
El
e
c
tri
c
a
l
En
g
i
n
e
e
rin
g
De
g
re
e
,
B.
E
n
g
.
(H
o
n
s)
fro
m
Un
iv
e
rsiti
Ten
a
g
a
Na
ti
o
n
a
l
(UN
ITE
N)
M
a
lay
sia
,
a
n
M
.
S
c
.
d
e
g
re
e
fro
m
t
h
e
Un
iv
e
rsity
o
f
M
a
n
c
h
e
ste
r
In
sti
tu
te
o
f
S
c
ien
c
e
a
n
d
Tec
h
n
o
l
o
g
y
(UM
IS
T)
,
a
n
d
a
P
h
.
D.
d
e
g
re
e
i
n
th
e
fiel
d
o
f
e
n
e
rg
y
e
c
o
n
o
m
ics
fro
m
t
h
e
Un
iv
e
rsity
o
f
M
a
n
c
h
e
s
ter,
UK
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
n
o
fr
iy
e
n
it
a
0
1
2
@
u
it
m
.
e
d
u
.
m
y.
S
a
id
a
tu
l
H
a
b
s
a
h
As
m
a
n
re
c
e
iv
e
d
a
B.
En
g
.
(H
o
n
s)
a
n
d
,
M
.
S
c
.
i
n
El
e
c
tri
c
a
l
En
g
i
n
e
e
rin
g
fro
m
U
n
iv
e
rsiti
Tek
n
o
lo
g
i
M
ARA
(Ui
TM
).
S
h
e
e
a
rn
e
d
a
P
h
.
D.
d
e
g
re
e
in
th
e
field
o
f
El
e
c
tri
c
a
l
En
g
i
n
e
e
rin
g
f
ro
m
Un
iv
e
rsit
i
Ten
a
g
a
Na
sio
n
a
l
(UN
ITE
N)
M
a
lay
sia
.
He
r
re
se
a
rc
h
in
tere
sts
c
o
v
e
r
RE
re
so
u
rc
e
s,
e
n
e
rg
y
m
a
n
a
g
e
m
e
n
t,
p
ro
tec
t
io
n
sy
ste
m
s
,
fa
u
lt
a
n
a
l
y
sis,
p
o
we
r
q
u
a
li
ty
,
sig
n
a
l
p
r
o
c
e
ss
in
g
,
a
n
d
m
a
c
h
in
e
lea
rn
in
g
a
p
p
li
c
a
ti
o
n
s
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
sa
id
a
tu
lh
a
b
sa
h
9
3
@
g
m
a
il
.
c
o
m
.
S
iti
H
a
ja
r
Yuso
ff
re
c
e
iv
e
d
th
e
M
.
En
g
.
d
e
g
re
e
(Ho
n
s.)
i
n
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
a
n
d
th
e
P
h
.
D.
d
e
g
re
e
in
El
e
c
tri
c
a
l
En
g
i
n
e
e
rin
g
fro
m
t
h
e
Un
iv
e
rs
it
y
o
f
No
tt
i
n
g
h
a
m
,
U.K.,
i
n
2
0
0
9
a
n
d
2
0
1
4
,
re
sp
e
c
ti
v
e
l
y
.
I
n
2
0
1
5
,
sh
e
b
e
c
a
m
e
a
n
As
sista
n
t
P
ro
fe
ss
o
r
at
th
e
De
p
a
rtme
n
t
o
f
El
e
c
tri
c
a
l
a
n
d
C
o
m
p
u
ter
En
g
in
e
e
rin
g
,
In
tern
a
ti
o
n
a
l
Isla
m
ic
Un
i
v
e
rsity
M
a
lay
sia
,
G
o
m
b
a
k
.
S
h
e
is
c
u
rre
n
t
ly
a
lec
tu
re
r
in
c
o
n
tro
l
o
f
p
o
we
r
e
lec
tro
n
ics
a
n
d
e
le
c
tri
c
a
l
p
o
we
r
sy
ste
m
s.
He
r
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
c
o
n
tro
ll
in
g
p
o
we
r
c
o
n
v
e
rters
a
n
d
d
riv
e
s,
m
a
tri
x
a
n
d
m
u
lt
i
-
lev
e
l
c
o
n
v
e
rters
,
th
e
Io
T,
sm
a
rt
m
e
ter
s
,
wire
les
s
p
o
we
r
tran
sfe
r
f
o
r
d
y
n
a
m
ic
c
h
a
rg
i
n
g
i
n
e
lec
tri
c
v
e
h
icle
s (
EVs),
a
n
d
RE
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
sit
iy
u
so
ff
@iiu
m
.
e
d
u
.
m
y
.
Evaluation Warning : The document was created with Spire.PDF for Python.