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m
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c
to
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h
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s
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e
tem
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sm
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ta
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e
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o
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h
m
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ly
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il
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ti
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g
a
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c
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ra
te
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d
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ti
o
n
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o
f
tran
sm
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s.
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s
e
d
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m
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d
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l
a
c
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e
d
a
m
e
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n
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th
e
p
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o
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a
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ru
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t
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a
d
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ift
s.
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h
e
se
re
su
lt
s
c
o
n
firm
th
e
m
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d
e
l’s
st
re
n
g
th
in
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e
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ti
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y
in
g
l
o
n
g
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term
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sm
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n
l
o
ss
p
a
tt
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s,
m
a
k
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n
g
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ste
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g
a
n
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o
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ti
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n
a
l
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re
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e
m
o
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e
l
e
x
h
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b
it
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d
h
i
g
h
p
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a
c
c
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ra
c
y
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e
sp
e
c
ially
in
re
c
o
g
n
izin
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lo
n
g
-
term
tren
d
s
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it
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c
e
d
li
m
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o
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i
n
a
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c
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ra
tely
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a
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p
t
c
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a
n
g
e
s
in
tra
n
sm
issio
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l
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ss
e
s.
Th
e
re
fo
re
,
f
u
tu
re
im
p
ro
v
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m
e
n
ts
sh
o
u
l
d
a
im
t
o
e
n
h
a
n
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e
re
sp
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n
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to
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.
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e
re
su
lt
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g
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th
a
t
t
h
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EE
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D
-
S
VR m
o
d
e
l
c
a
n
p
ro
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ly
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ra
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i
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rin
g
a
n
d
m
it
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a
ti
n
g
tran
sm
issio
n
l
o
ss
e
s.
K
ey
w
o
r
d
s
:
Dee
p
lear
n
in
g
EEMD
J
av
a
-
B
ali
s
y
s
tem
SVR
T
r
an
s
m
is
s
io
n
lo
s
s
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T
r
an
s
m
is
s
io
n
s
y
s
tem
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
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-
SA
li
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e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Kar
el
Octa
v
ian
u
s
B
ac
h
r
i
Dep
ar
tm
en
t o
f
E
lectr
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E
n
g
i
n
ee
r
in
g
,
Sch
o
o
l o
f
B
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cien
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T
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o
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y
,
a
n
d
I
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o
v
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Atm
a
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ay
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C
ath
o
lic
Un
iv
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s
ity
o
f
I
n
d
o
n
esia
So
u
th
J
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ar
ta
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I
n
d
o
n
esia
E
m
ail:
k
ar
el.
b
ac
h
r
i@
atm
ajay
a.
ac
.
id
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
lar
g
est
elec
tr
icity
s
y
s
tem
in
I
n
d
o
n
esia
is
J
av
a
–
B
ali
s
y
s
tem
[
1
]
,
wh
ich
is
d
iv
id
e
d
in
to
f
iv
e
ar
ea
s
co
n
s
is
tin
g
o
f
th
e
J
ak
ar
ta
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B
an
ten
ar
ea
,
th
e
W
est
J
av
a
ar
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,
t
h
e
C
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tr
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J
av
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ar
ea
,
th
e
E
ast
J
av
a
ar
ea
,
an
d
th
e
B
ali
ar
ea
.
T
h
e
o
p
er
atio
n
o
f
th
is
s
y
s
tem
co
n
f
o
r
m
s
to
th
e
b
asic
p
r
in
cip
les
o
f
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o
n
o
m
y
,
r
eli
ab
ilit
y
,
q
u
ality
,
an
d
g
r
ee
n
[
2
]
.
T
r
an
s
m
is
s
io
n
-
lo
s
s
es
is
an
ec
o
n
o
m
ic
in
d
icato
r
in
th
e
o
p
er
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n
o
f
p
o
we
r
s
y
s
tem
b
ec
au
s
e
tr
an
s
m
is
s
io
n
lo
s
s
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s
h
o
w
th
e
elec
tr
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en
er
g
y
lo
s
t w
h
en
en
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g
y
is
g
en
er
ated
u
n
til it is tr
an
s
m
itted
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o
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g
h
th
e
tr
an
s
m
is
s
io
n
n
etwo
r
k
[
3
]
-
[
5
]
.
T
h
e
s
m
aller
tr
an
s
m
is
s
io
n
l
o
s
s
es
s
h
o
w
a
lar
g
e
lev
el
o
f
en
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g
y
ef
f
icien
cy
.
T
r
an
s
m
is
s
io
n
lo
s
s
es
ty
p
ically
ac
co
u
n
t
ab
o
u
t
3
-
5
%
o
f
t
o
tal
p
o
wer
g
en
er
atio
n
[
6
]
.
Ho
wev
er
,
th
e
s
m
all
p
er
ce
n
tag
e
o
f
tr
an
s
m
is
s
io
n
lo
s
s
es in
ter
m
s
o
f
MW is
ca
u
s
in
g
s
ig
n
if
ican
t c
o
n
ce
r
n
.
I
n
m
o
d
er
n
elec
tr
ic
p
o
wer
s
y
s
tem
s
,
alo
n
g
with
i
n
cr
ea
s
in
g
lo
a
d
s
an
d
t
h
e
ch
a
n
g
es
i
n
n
etwo
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k
co
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f
ig
u
r
atio
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,
it
is
im
p
o
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tan
t
to
m
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tain
a
b
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b
etwe
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g
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r
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an
d
lo
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d
s
o
th
at
o
p
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n
o
f
t
h
e
elec
tr
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p
o
wer
s
y
s
tem
r
u
n
s
r
eliab
ly
,
with
q
u
ality
an
d
ec
o
n
o
m
ically
[
7
]
.
I
n
ad
d
itio
n
to
lo
ad
p
r
e
d
ictio
n
,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Po
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&
Dr
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s
t
I
SS
N:
2088
-
8
6
9
4
P
r
ed
ictin
g
tr
a
n
s
mis
s
io
n
lo
s
s
e
s
u
s
in
g
E
E
MD
–
S
V
R
a
lg
o
r
ith
m
(
Hesti Tr
i
Les
ta
r
i
)
2123
ac
cu
r
ate
tr
an
s
m
is
s
io
n
lo
s
s
e
s
p
r
ed
ictio
n
p
lay
s
a
cr
u
cial
r
o
le
in
th
e
o
p
er
atio
n
al
s
tr
ateg
y
an
d
d
ec
is
io
n
m
ak
in
g
o
f
th
e
s
y
s
tem
o
p
er
ato
r
s
[
8
]
,
[
9
]
.
I
t
en
ab
les
s
y
s
tem
o
p
er
ato
r
s
to
en
h
an
ce
th
e
r
eliab
ilit
y
o
f
elec
tr
ical
en
er
g
y
s
u
p
p
ly
an
d
im
p
lem
e
n
t a
p
p
r
o
p
r
iate
m
i
tig
atio
n
to
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ed
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ce
tr
an
s
m
is
s
io
n
lo
s
s
es.
Pre
d
ictin
g
tr
a
n
s
m
is
s
io
n
lo
s
s
es
p
r
esen
ts
n
u
m
er
o
u
s
ch
allen
g
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d
u
e
to
m
u
ltip
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f
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s
s
u
ch
as
p
ea
k
lo
ad
,
v
o
ltag
e
,
an
d
lo
a
d
f
lo
w
[
1
0
]
.
I
t
is
ess
en
tial
to
id
en
tif
y
an
d
u
n
d
er
s
tan
d
th
e
co
r
r
elat
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s
b
etwe
en
th
ese
f
ac
to
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s
an
d
tr
an
s
m
is
s
io
n
lo
s
s
es
to
en
s
u
r
e
ac
cu
r
ate
p
r
ed
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io
n
s
.
T
h
is
an
aly
s
is
is
im
p
o
r
tan
t
in
o
p
tim
izin
g
s
y
s
tem
o
p
er
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n
s
an
d
im
p
r
o
v
in
g
o
v
e
r
all
en
er
g
y
ef
f
icien
c
y
[
1
1
]
,
[
1
2
]
.
W
ith
th
e
ad
v
a
n
ce
m
en
t
o
f
tech
n
o
lo
g
y
,
th
e
u
s
e
o
f
m
ac
h
in
e
le
ar
n
in
g
alg
o
r
ith
m
s
h
as
b
ec
o
m
e
a
m
et
h
o
d
to
p
r
ed
ict
an
d
o
p
tim
ize
th
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
elec
tr
icity
n
etwo
r
k
[
1
3
]
-
[
1
5
]
.
Ma
ch
in
e
l
ea
r
n
in
g
ca
n
u
tili
ze
lar
g
e
am
o
u
n
ts
o
f
o
p
e
r
atio
n
al
an
d
h
is
to
r
ical
d
ata
to
p
r
ed
ict
tr
an
s
m
is
s
io
n
lo
s
s
e
s
q
u
ick
ly
an
d
ac
cu
r
ately
[
1
6
]
.
T
h
is
wo
r
k
aim
s
to
d
ev
elo
p
a
p
r
ed
ictiv
e
m
o
d
el
u
s
in
g
an
ap
p
r
o
p
r
iate
m
ac
h
in
e
lear
n
i
n
g
alg
o
r
ith
m
,
wh
ich
is
n
o
t
o
n
ly
ac
cu
r
ate
b
u
t a
ls
o
p
r
ac
tica
l to
im
p
lem
en
t in
a
n
elec
tr
ic
p
o
wer
o
p
er
ati
n
g
s
y
s
tem
.
T
h
is
wo
r
k
aim
s
to
d
ev
elo
p
a
p
r
ed
ictiv
e
m
o
d
el
u
s
in
g
an
a
p
p
r
o
p
r
iate
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
,
wh
ich
is
n
o
t
o
n
ly
ac
cu
r
ate
b
u
t
also
p
r
ac
tical
to
im
p
lem
en
t
in
an
elec
tr
ic
p
o
wer
o
p
e
r
atin
g
s
y
s
tem
.
T
o
ac
h
iev
e
th
is
,
we
p
r
o
p
o
s
e
a
n
o
v
el
h
y
b
r
id
p
r
e
d
ictio
n
m
o
d
el
t
h
at
co
m
b
in
es
en
s
em
b
le
em
p
ir
ical
m
o
d
e
d
ec
o
m
p
o
s
itio
n
(
E
E
MD
)
with
s
u
p
p
o
r
t
v
ec
to
r
r
eg
r
ess
io
n
(
SVR
)
to
f
o
r
ec
ast
t
r
an
s
m
is
s
io
n
lo
s
s
e
s
.
Un
lik
e
co
n
v
en
tio
n
al
m
o
d
els,
th
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
d
ec
o
m
p
o
s
es
th
e
lo
s
s
s
ig
n
al
in
to
f
r
eq
u
en
cy
co
m
p
o
n
en
ts
,
en
ab
lin
g
th
e
SVR
to
m
o
r
e
ef
f
ec
tiv
ely
ca
p
tu
r
e
b
o
th
s
h
o
r
t
-
ter
m
f
l
u
ctu
atio
n
s
a
n
d
l
o
n
g
-
ter
m
tr
en
d
s
.
T
o
th
e
b
est
o
f
o
u
r
k
n
o
wled
g
e,
th
is
is
th
e
f
ir
s
t
ap
p
licatio
n
o
f
th
e
E
E
MD
-
SVR
f
r
am
ewo
r
k
f
o
r
p
r
ed
ictin
g
tr
a
n
s
m
is
s
io
n
lo
s
s
es
in
th
e
J
av
a
-
B
ali
elec
tr
icity
s
y
s
tem
,
o
f
f
er
in
g
b
o
t
h
m
eth
o
d
o
lo
g
ical
a
n
d
r
e
g
io
n
al
n
o
v
elty
.
2.
M
E
T
H
O
D
2
.
1
.
D
a
t
a
c
o
llect
ing
T
h
is
wo
r
k
s
elec
ts
J
av
a
–
B
ali
tr
an
s
m
is
s
io
n
s
y
s
tem
as
th
e
r
esear
ch
o
b
ject.
T
h
e
h
is
to
r
ical
d
ata
was
co
llected
f
o
r
th
r
ee
y
ea
r
s
.
T
h
e
d
ata
co
n
s
is
ts
o
f
elec
tr
icity
d
a
ta
in
clu
d
in
g
d
aily
p
ea
k
l
o
ad
,
p
r
o
d
u
ctio
n
e
n
er
g
y
,
en
er
g
y
co
m
p
o
s
itio
n
d
ata
f
o
r
g
en
er
atin
g
p
la
n
ts
in
ea
ch
ar
ea
,
an
d
lo
ad
f
lo
w
b
etwe
en
ar
e
as
.
Me
teo
r
o
lo
g
ical
f
ac
to
r
s
s
u
ch
as
tr
an
s
m
is
s
io
n
li
n
e
tem
p
er
atu
r
e
ar
e
s
elec
ted
[
1
7
]
.
C
alen
d
ar
d
ata
was
ch
o
s
en
co
n
s
is
ts
o
f
b
in
ar
y
wee
k
en
d
-
wee
k
d
ay
in
d
icato
r
.
His
to
r
ical
d
ata
is
d
iv
id
ed
in
to
two
s
u
b
s
ets:
tr
ain
in
g
d
ata
an
d
test
d
ata.
T
h
e
tr
ain
in
g
d
ata
is
u
s
ed
to
tr
ain
th
e
m
o
d
el,
wh
ile
th
e
test
d
ata
is
u
s
ed
to
ev
alu
ate
t
h
e
m
o
d
el’
s
p
er
f
o
r
m
an
ce
.
I
n
th
is
wo
r
k
,
d
ata
s
ep
ar
atio
n
was c
ar
r
ied
o
u
t a
s
: tr
ain
in
g
d
a
ta
was 8
0
% wh
ile
test
d
ata
was 2
0
%.
2
.
2
.
Da
t
a
prepro
ce
s
s
ing
Data
p
r
e
-
p
r
o
ce
s
s
in
g
is
an
im
p
o
r
tan
t
s
tep
in
th
e
p
r
ep
ar
atio
n
o
f
a
d
ataset
p
r
io
r
to
its
u
tili
za
tio
n
in
a
m
ac
h
in
e
lear
n
in
g
m
o
d
el.
T
h
e
m
o
d
u
le
in
p
u
t
in
th
is
wo
r
k
wer
e
p
r
e
-
p
r
o
ce
s
s
with
d
ata
clea
n
s
in
g
with
in
ter
q
u
ar
tile
r
a
n
g
e
(
I
QR
)
an
d
d
ata
n
o
r
m
aliza
tio
n
with
Stan
d
ar
d
Scaler
.
T
h
e
I
QR
tech
n
iq
u
e
f
o
r
d
ata
clea
n
in
g
is
d
esig
n
ed
to
id
en
tify
an
d
r
e
m
o
v
e
o
u
tlier
s
f
r
o
m
th
e
d
ataset.
I
t
is
r
o
b
u
s
t
e
s
tim
ato
r
f
o
r
a
d
ata
s
et
u
p
to
2
5
%
o
u
tlier
s
[
1
8
]
,
[
1
9
]
.
T
h
e
in
ter
q
u
ar
tile
r
an
g
e
(
I
QR
)
q
u
an
tifi
es
d
ata
d
is
tr
ib
u
tio
n
b
y
d
eter
m
in
in
g
th
e
in
ter
v
al
b
etwe
en
th
e
f
ir
s
t q
u
a
r
tile (
Q1
)
an
d
th
e
t
h
ir
d
q
u
ar
tile (
Q3
)
.
Af
ter
clea
n
in
g
th
e
d
ata,
we
p
er
f
o
r
m
d
ata
n
o
r
m
aliza
tio
n
u
s
in
g
Stan
d
ar
d
Scaler
.
T
h
is
is
im
p
o
r
tan
t
b
ec
au
s
e
s
o
m
e
alg
o
r
ith
m
s
,
lik
e
SVR
,
ar
e
s
en
s
itiv
e
to
d
if
f
er
e
n
ce
s
in
f
ea
tu
r
e
s
ca
lin
g
.
Stan
d
ar
d
Scaler
tr
an
s
f
o
r
m
s
th
e
d
ata
f
ea
tu
r
es
to
h
av
e
a
m
e
an
o
f
0
an
d
a
s
tan
d
ar
d
d
ev
iati
o
n
o
f
1
.
T
h
is
en
s
u
r
es
th
at
all
f
ea
tu
r
es
ar
e
o
n
th
e
s
am
e
s
ca
le,
p
r
ev
en
tin
g
f
ea
tu
r
es
with
lar
g
e
s
ca
les
f
r
o
m
o
v
e
r
p
o
wer
in
g
t
h
e
m
o
d
el
an
d
im
p
r
o
v
in
g
t
h
e
lear
n
in
g
p
r
o
ce
s
s
ef
f
icien
cy
.
2
.
3
.
Da
t
a
EEMD
d
ec
o
m
po
s
i
t
io
n
E
n
s
em
b
le
em
p
ir
ical
m
o
d
e
d
ec
o
m
p
o
s
itio
n
(
E
E
MD
)
is
th
e
im
p
r
o
v
em
e
n
t
o
f
E
MD
alg
o
r
ith
m
s
.
I
t
aim
s
to
o
v
er
co
m
e
th
e
d
ef
icien
cies
o
f
m
o
d
al
aliasin
g
in
E
MD
m
eth
o
d
o
lo
g
ies
[
2
0
]
.
T
h
e
d
ec
o
m
p
o
s
itio
n
s
tag
es
th
a
t
ar
e
s
p
ec
if
ic
to
E
E
MD
ar
e
as:
Allo
w
wh
ite
n
o
is
e
(
)
to
th
e
o
r
ig
in
al
s
ig
n
al
(
)
.
All
wh
ite
n
o
is
e
(
)
r
eq
u
ir
e
th
at
th
e
a
m
p
litu
d
e
v
alu
e
is
ze
r
o
an
d
th
at
th
e
s
tan
d
ar
d
d
e
v
iatio
n
r
em
ain
s
co
n
s
tan
t,
s
p
ec
if
ically
b
etwe
en
0
.
1
to
0
.
4
tim
es
th
e
o
r
ig
in
al
s
tan
d
ar
d
d
ev
iatio
n
,
th
e
eq
u
atio
n
o
f
(
)
is
as (
1
)
.
(
)
=
(
)
+
(
)
(1
)
I
n
wh
ich
th
e
s
ig
n
al
is
r
ep
r
esen
ted
b
y
(
)
wh
ich
is
th
e
ad
d
itio
n
o
f
wh
ite
n
o
is
e
o
v
e
r
tim
e.
T
h
e
i
n
t
r
i
n
s
i
c
m
o
d
a
l
f
u
n
c
t
i
o
n
(
I
M
F
)
c
o
m
p
o
n
e
n
t
(
)
a
n
d
t
h
e
r
e
m
a
i
n
d
e
r
(
)
∙
(
)
c
a
n
b
e
o
b
t
a
i
n
e
d
b
y
d
e
c
o
m
p
o
s
i
n
g
(
)
w
i
t
h
E
E
MD
.
T
h
e
ℎ
I
M
F a
n
d
t
h
e
G
a
u
s
s
i
a
n
w
h
i
t
e
n
o
is
e
h
a
v
e
b
ee
n
a
d
d
e
d
i t
i
m
e
s
.
R
ep
ea
t
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
6
9
4
I
n
t J Po
w
E
lec
&
Dr
i Sy
s
t
,
Vo
l.
16
,
No
.
3
,
Sep
tem
b
er
20
25
:
212
2
-
21
29
2124
th
e
in
itial two
s
tag
es N
tim
es.
Sin
ce
th
e
s
tatis
tical
av
er
ag
e
v
alu
e
o
f
an
u
n
r
elate
d
r
an
d
o
m
s
e
q
u
en
ce
is
ze
r
o
,
th
is
wo
r
k
will
co
m
p
u
te
th
e
av
er
a
g
e
v
alu
e
o
f
th
e
I
MF
th
at
was
o
b
tain
ed
f
r
o
m
th
e
f
ir
s
t
two
p
r
o
ce
d
u
r
es.
T
h
e
p
u
r
p
o
s
e
is
to
elim
in
ate
t
h
e
i
m
p
ac
t
r
ep
ea
te
d
ly
b
y
a
d
d
in
g
wh
ite
n
o
is
e
o
n
th
e
r
ea
l
I
MF.
T
h
en
I
MF
f
in
ally
o
b
tain
ed
b
y
E
E
MD
d
ec
o
m
p
o
s
itio
n
as
(
2
)
.
(
)
=
1
∑
=
1
(
2
)
I
n
wh
ich
,
(
)
ar
e
ℎ
I
MF
c
o
m
p
o
n
en
t
wh
ich
o
r
ig
i
n
al
s
ig
n
al
h
av
e
E
E
MD
d
ec
o
m
p
o
s
ed
.
T
h
e
r
e
s
u
lts
o
f
th
e
E
E
MD
d
ec
o
m
p
o
s
itio
n
ar
e
as (
3
)
.
(
)
=
∑
(
)
+
(
)
(
3
)
I
n
th
is
wo
r
k
,
t
h
e
tar
g
et
tr
an
s
m
is
s
io
n
lo
s
s
e
s
v
ar
iab
le
is
d
ec
o
m
p
o
s
ed
u
s
in
g
E
E
MD
.
E
E
MD
b
r
ea
k
s
th
e
tar
g
et
in
to
s
ev
er
al
I
MFs,
ea
ch
o
f
w
h
ich
ca
p
tu
r
es
o
s
cillatio
n
s
at
d
if
f
er
e
n
t
f
r
e
q
u
en
cies.
T
h
i
s
tech
n
iq
u
e
r
ed
u
ce
s
th
e
p
r
o
b
lem
o
f
m
o
d
e
m
ix
in
g
b
y
ad
d
in
g
r
an
d
o
m
n
o
is
e
to
th
e
d
ata
b
ef
o
r
e
d
ec
o
m
p
o
s
itio
n
,
wh
ich
r
esu
lts
in
a
clea
r
er
s
ep
ar
atio
n
o
f
co
m
p
o
n
en
ts
.
Af
ter
th
e
d
ec
o
m
p
o
s
itio
n
p
r
o
ce
s
s
,
I
MF
d
iv
id
ed
in
to
t
wo
g
r
o
u
p
s
b
ased
o
n
th
eir
f
r
eq
u
en
cies.
Fig
u
r
e
s
1
a
n
d
2
s
h
o
w
tr
a
n
s
m
is
s
io
n
lo
s
s
es
d
ec
o
m
p
o
s
e
i
n
to
I
MFs
h
ig
h
an
d
I
MFs
lo
w,
with
I
MFs
h
ig
h
r
ep
r
esen
ts
h
ig
h
f
r
eq
u
en
cies,
ca
p
tu
r
in
g
r
ap
id
v
a
r
iatio
n
s
o
r
lo
ca
l
n
o
is
e
in
th
e
d
ata
,
an
d
I
MFs
lo
w
r
ep
r
esen
ts
lo
w
f
r
eq
u
en
cies,
ca
p
tu
r
in
g
l
o
n
g
-
ter
m
tr
en
d
s
o
r
s
te
ad
y
ch
a
n
g
es in
th
e
d
ata.
Fig
u
r
e
1
.
I
MFs h
ig
h
co
m
p
o
n
e
n
t a
f
ter
E
E
MD
d
ec
o
m
p
o
s
itio
n
2
.
4
.
P
r
o
po
s
ed
m
o
del
T
h
e
least
s
q
u
ar
es
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
L
SS
VM
)
in
t
eg
r
ates
th
e
k
er
n
el
f
u
n
ctio
n
with
r
id
g
e
r
eg
r
ess
io
n
an
d
e
m
p
lo
y
s
th
e
least
s
q
u
ar
es
er
r
o
r
f
u
n
ctio
n
to
s
u
it
th
e
d
ata.
Ho
wev
e
r
,
t
h
e
q
u
a
n
tity
o
f
th
e
ca
lcu
latio
n
is
th
e
th
ir
d
p
o
wer
o
f
s
am
p
le,
wh
ich
is
n
o
t
f
ac
il
itatin
g
th
e
m
o
d
el’
s
s
im
p
lific
a
tio
n
an
d
im
p
r
o
v
i
n
g
ca
lcu
latin
g
s
p
ee
d
.
Su
p
p
o
r
t
v
e
cto
r
m
ac
h
in
e
r
eg
r
ess
io
n
(
SVR
)
is
p
r
o
p
o
s
ed
o
n
th
is
b
asis
,
wh
ich
s
ig
n
if
ican
tl
y
r
ed
u
ce
s
co
m
p
u
tatio
n
al
c
o
m
p
l
ex
ity
b
y
u
tili
zin
g
s
u
p
p
o
r
t
v
ec
to
r
s
,
an
d
h
as
s
am
e
ca
p
ac
ity
a
s
L
SS
VM
to
m
atch
s
am
p
les with
h
ig
h
p
r
ec
is
io
n
l
o
n
g
itu
d
e
[
2
1
]
,
[
2
2
]
.
T
h
e
SVR
r
eg
r
ess
io
n
m
eth
o
d
is
wid
ely
u
s
ed
tech
n
i
q
u
e
in
th
e
an
aly
s
is
o
f
ti
m
e
s
er
ies
p
r
ed
ictin
g
[
2
3
]
-
[
2
5
]
.
I
t
is
ad
e
p
t
at
g
en
er
alizin
g
to
lig
h
twei
g
h
t
,
n
o
n
lin
ea
r
,
a
n
d
tim
e
-
s
er
ie
s
s
am
p
le.
No
n
lin
ea
r
m
ap
p
in
g
t
h
e
in
p
u
t
s
am
p
le
d
ata
to
h
ig
h
d
im
en
s
io
n
al
f
ea
t
u
r
e
s
p
ac
e
f
o
r
lin
ea
r
r
e
g
r
ess
io
n
,
to
im
p
lem
en
t
n
o
n
lin
ea
r
f
itti
n
g
in
th
e
d
ata
s
p
ac
e
[
2
1
]
.
T
h
e
p
r
ed
icted
r
eg
r
es
s
io
n
f
u
n
ctio
n
in
SVR
d
ef
in
ed
as
(
4
)
.
(
)
=
ɸ
(
)
+
(
4
)
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
P
r
ed
ictin
g
tr
a
n
s
mis
s
io
n
lo
s
s
e
s
u
s
in
g
E
E
MD
–
S
V
R
a
lg
o
r
ith
m
(
Hesti Tr
i
Les
ta
r
i
)
2125
W
h
e
r
e
ω
is
a
w
e
i
g
h
ti
n
g
m
a
t
r
ix
,
b
i
s
t
h
e
b
i
a
s
t
e
r
m
,
ɸ
(
x
)
i
s
a
n
o
n
-
l
i
n
e
a
r
m
a
p
p
i
n
g
o
f
i
n
p
u
t
x
i
n
t
o
a
h
i
g
h
e
r
-
d
i
m
e
n
s
i
o
n
a
l
f
e
at
u
r
e
s
p
ac
e
u
s
i
n
g
k
e
r
n
e
l
[
2
6
]
.
X
r
e
p
r
e
s
e
n
t
s
t
h
e
i
n
p
u
t
f
e
at
u
r
e
s
s
u
c
h
a
s
p
r
o
d
u
c
t
i
o
n
e
n
e
r
g
y
,
l
o
a
d
f
l
o
w
,
t
r
a
n
s
m
i
s
s
i
o
n
l
i
n
e
t
e
m
p
e
r
a
t
u
r
e
,
e
n
e
r
g
y
c
o
m
p
o
s
i
ti
o
n
f
o
r
g
e
n
e
r
a
t
i
n
g
p
l
a
n
ts
,
d
a
il
y
p
e
a
k
l
o
a
d
,
a
n
d
c
a
l
e
n
d
a
r
d
a
t
a
.
S
VR
m
i
n
i
m
i
ze
s
a
n
o
b
j
e
c
t
i
v
e
t
h
a
t
i
s
c
o
m
p
o
s
e
d
o
f
t
w
o
c
o
m
p
o
n
e
n
t
s
:
i
)
m
a
r
g
i
n
e
r
r
o
r
w
i
t
h
t
o
l
e
r
a
n
c
e
,
a
n
d
i
i
)
m
o
d
e
l
r
e
g
u
l
a
r
i
z
at
i
o
n
to
p
r
e
v
e
n
t
o
v
e
r
f
i
t
t
i
n
g
.
T
h
e
f
u
n
c
ti
o
n
t
o
b
e
m
i
n
i
m
i
z
e
d
is
t
h
e
o
b
j
ec
t
i
v
e
f
u
n
c
t
i
o
n
.
1
2
|
|
|
|
2
+
∑
(
+
∗
=
1
)
(
5
)
|
|
|
|
2
is
th
e
n
o
r
m
weig
h
t
f
ac
to
r
th
at
is
r
ed
u
ce
d
to
m
ain
tai
n
th
e
s
im
p
licity
o
f
th
e
m
o
d
el,
C
is
a
h
y
p
er
p
ar
am
eter
t
h
at
r
eg
u
lates
th
e
b
alan
ce
b
etwe
en
m
in
im
i
zin
g
er
r
o
r
an
d
r
e
g
u
latin
g
m
o
d
el
co
m
p
lex
ity
,
th
e
v
ar
iab
les
an
d
∗
ar
e
s
lack
v
ar
iab
les
th
at
en
a
b
le
d
ata
v
alu
es
to
f
all
o
u
ts
id
e
th
e
m
ar
g
in
ϵ
.
T
h
e
m
o
d
el
is
s
u
b
ject
to
th
e
co
n
s
tr
ain
t
t
h
at
t
h
e
er
r
o
r
m
a
r
g
in
s
h
o
u
ld
n
o
t
e
x
ce
ed
ϵ
f
o
r
m
o
s
t
o
f
t
h
e
d
ata,
b
u
t
o
u
tlier
s
ca
n
b
e
allo
wed
o
u
ts
id
e
th
e
m
a
r
g
in
v
ia
s
lack
v
ar
iab
les.
T
h
ese
co
n
s
tr
ain
ts
ar
e
ex
p
r
ess
ed
as
(
6
)
.
–
(
ɸ
(
)
+
)
≤
+
(
ɸ
(
)
+
)
−
≤
ϵ
+
∗
∗
≥
0
(
6
)
W
h
er
e
co
n
tr
o
ls
th
e
m
ar
g
in
o
f
to
ler
an
ce
f
o
r
th
e
er
r
o
r
,
∗
ar
e
s
lack
v
ar
iab
les
th
at
allo
w
ce
r
tain
d
ata
p
o
in
ts
to
ex
ce
ed
th
e
m
a
r
g
in
with
p
e
n
alty
.
T
h
e
r
ad
ial
b
asis
f
u
n
ctio
n
(
R
B
F
)
k
er
n
el
is
im
p
lem
e
n
te
d
.
SVR
ca
n
ca
p
tu
r
e
non
-
lin
ea
r
r
elatio
n
s
h
ip
s
b
etwe
en
th
e
in
p
u
t
f
ea
tu
r
es
an
d
th
e
tar
g
et
u
s
in
g
th
e
R
B
F
k
er
n
el.
T
h
e
k
er
n
el
is
d
ef
in
ed
as
(
7
)
.
(
,
)
=
(
−
|
|
−
|
|
2
)
(
7
)
W
h
er
e
γ
co
n
tr
o
ls
th
e
i
n
f
lu
en
c
e
o
f
a
s
in
g
le
tr
ain
i
n
g
e
x
am
p
le.
W
e
u
s
ed
m
ea
n
ab
s
o
lu
te
e
r
r
o
r
(
MA
E
)
as
th
e
lo
s
s
f
u
n
ctio
n
as (
8
)
.
=
1
∑
|
−
^
|
=
1
(
8
)
W
h
er
e
^
is
th
e
p
r
ed
icted
tr
an
s
m
is
s
io
n
lo
s
s
e
s
,
is
th
e
ac
tu
al
t
r
an
s
m
is
s
io
n
lo
s
s
e
s
at
tim
e
τ
.
W
e
ch
o
s
e
th
e
MA
E
b
ec
au
s
e
it is
less
s
en
s
iti
v
e
to
p
o
s
s
ib
le
o
u
tlier
s
[
1
1
]
.
Fig
u
r
e
2
.
I
MFs lo
w
co
m
p
o
n
e
n
t a
f
ter
E
E
MD
d
ec
o
m
p
o
s
itio
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
6
9
4
I
n
t J Po
w
E
lec
&
Dr
i Sy
s
t
,
Vo
l.
16
,
No
.
3
,
Sep
tem
b
er
20
25
:
212
2
-
21
29
2126
3.
RE
SU
L
T
S
AND
D
I
SCU
SS
I
O
N
I
n
th
is
wo
r
k
we
im
p
lem
e
n
ted
th
e
E
E
MD
m
eth
o
d
with
SVR
to
p
r
e
d
ict
tr
an
s
m
is
s
io
n
lo
s
s
es
in
th
e
J
av
a
-
B
ali
s
y
s
tem
.
T
h
e
E
E
MD
-
SVR
m
o
d
el
ef
f
ec
tiv
ely
ca
p
tu
r
ed
b
o
th
s
h
o
r
t
-
ter
m
v
ar
iat
io
n
s
an
d
lo
n
g
-
ter
m
tr
en
d
s
in
tr
a
n
s
m
is
s
io
n
lo
s
s
es
d
ata
b
y
d
ec
o
m
p
o
s
in
g
th
e
s
ig
n
al
in
to
s
ev
er
al
I
MFs.
In
(
8
)
I
MFs
wer
e
o
b
tain
ed
b
ec
au
s
e
o
f
th
e
d
ec
o
m
p
o
s
itio
n
o
f
th
e
tar
g
et
v
ar
iab
le,
tr
an
s
m
is
s
io
n
lo
s
s
es
(
k
W
h
)
,
u
s
in
g
E
E
MD
.
T
h
ese
I
MFs
ar
e
o
s
cillatio
n
s
th
at
o
cc
u
r
at
v
ar
y
in
g
f
r
eq
u
en
cies.
T
h
e
E
E
M
D
d
ec
o
m
p
o
s
itio
n
was
f
o
llo
we
d
b
y
th
e
tr
ain
i
n
g
o
f
two
d
is
tin
ct
SV
R
m
o
d
els
u
s
in
g
th
e
h
ig
h
-
f
r
e
q
u
en
c
y
an
d
l
o
w
-
f
r
eq
u
en
cy
I
MFs.
B
ased
o
n
Fig
u
r
e
1
,
h
ig
h
-
f
r
eq
u
e
n
cy
I
MFs
ar
e
r
esp
o
n
s
ib
le
f
o
r
d
o
cu
m
en
tin
g
r
ap
id
,
tem
p
o
r
ar
y
ch
an
g
es
in
th
e
lo
s
s
es,
wh
ich
m
ay
b
e
lin
k
ed
to
g
r
id
d
is
r
u
p
tio
n
s
,
we
ath
er
co
n
d
itio
n
s
,
o
r
lo
ad
im
b
a
lan
ce
s
in
th
e
p
o
wer
s
y
s
tem
.
Fig
u
r
e
2
s
h
o
ws
lo
w
-
f
r
eq
u
e
n
cy
I
MFs
id
en
tif
y
in
g
th
e
u
n
d
er
ly
in
g
tr
en
d
s
in
th
e
tr
an
s
m
is
s
io
n
lo
s
s
es
d
ata
th
at
m
ay
b
e
i
n
d
icativ
e
o
f
lo
n
g
-
ter
m
s
h
if
ts
in
g
r
id
p
er
f
o
r
m
an
ce
an
d
p
r
o
d
u
ctio
n
en
e
r
g
y
p
atter
n
s
.
T
h
e
R
B
F
k
er
n
el
was
ch
o
s
en
to
co
p
e
with
th
e
n
o
n
-
lin
ea
r
r
elatio
n
s
h
i
p
s
p
r
esen
t
in
th
e
d
ata.
C
o
m
p
ar
is
o
n
o
f
th
e
r
esu
lts
b
etwe
en
E
E
MD
-
SVR
m
o
d
el
an
d
ac
tu
al
tr
an
s
m
is
s
io
n
lo
s
s
es in
J
av
a
-
B
ali
s
y
s
tem
is
s
h
o
wn
i
n
Fig
u
r
e
3
.
Fig
u
r
e
3
.
C
o
m
p
a
r
is
o
n
b
etwe
e
n
E
E
MD
_
SVR
m
o
d
el
an
d
ac
t
u
al
tr
an
s
m
is
s
io
n
lo
s
s
es
T
h
e
E
E
MD
-
SVR
p
r
ed
ictio
n
li
n
e
(
in
r
e
d
)
clo
s
ely
f
o
llo
ws
th
e
ac
tu
al
lo
s
s
es
lin
e
(
in
b
lu
e
)
f
o
r
m
o
s
t
o
f
th
e
g
r
ap
h
,
s
u
g
g
esti
n
g
th
at
th
e
m
o
d
el
ac
cu
r
ately
d
ep
icts
th
e
g
en
er
al
p
atter
n
o
f
tr
an
s
m
is
s
io
n
lo
s
s
es.
T
h
e
g
en
er
al
tr
en
d
o
f
b
o
th
lin
es
is
s
im
ilar
,
with
r
eg
u
lar
p
ea
k
s
an
d
v
alley
s
.
T
h
is
s
h
o
ws
th
at
th
e
m
o
d
el
h
as
lear
n
ed
b
o
th
th
e
s
h
o
r
t
-
ter
m
ch
an
g
es
an
d
th
e
lo
n
g
er
-
te
r
m
tr
en
d
s
in
th
e
d
ata
.
T
h
e
E
E
MD
-
SVR
m
o
d
el
d
o
es
n
o
t
co
n
s
is
ten
tly
ca
p
tu
r
e
s
p
ik
es
o
r
ab
r
u
p
t
in
cr
e
ases
in
ac
tu
al
lo
s
s
e
s
.
T
h
e
m
o
d
el
f
r
eq
u
en
tly
u
n
d
er
esti
m
ates
th
e
ab
r
u
p
t
s
u
r
g
es
in
lo
s
s
es,
s
h
o
wn
wh
en
th
e
b
lu
e
lin
e
ex
ce
ed
s
th
e
r
ed
lin
e.
T
h
e
m
o
d
el
also
n
o
t
as
ac
cu
r
ately
p
r
ed
ict
s
u
d
d
en
d
ec
lin
es
in
th
e
ac
t
u
al
lo
s
s
es.
T
h
e
m
o
d
el
h
as
a
p
r
o
p
en
s
ity
t
o
o
v
e
r
esti
m
ate
th
e
ex
ten
t
o
f
t
h
e
d
ip
,
lik
e
th
e
s
p
ik
e
is
s
u
e.
T
h
is
is
ev
id
en
t in
th
e
in
s
tan
ce
s
wh
er
e
th
e
r
ed
lin
e
r
em
ain
s
ab
o
v
e
th
e
b
lu
e
lin
e
d
u
r
in
g
s
u
ch
d
ec
lin
es.
T
h
e
E
E
MD
d
ec
o
m
p
o
s
itio
n
p
r
o
ce
s
s
is
lik
ely
to
b
e
a
c
o
n
tr
ib
u
tin
g
f
ac
to
r
to
t
h
is
s
m
o
o
th
in
g
ef
f
ec
t.
T
h
e
m
o
d
el
ca
n
ca
p
tu
r
e
th
e
g
en
er
al
tr
en
d
s
an
d
p
atter
n
s
;
h
o
wev
e
r
,
th
e
s
h
ar
p
,
s
u
d
d
en
c
h
an
g
es
(
s
p
ik
es
an
d
d
ec
lin
es)
in
th
e
ac
tu
al
lo
s
s
es
d
ata
m
a
y
b
e
atten
u
ated
d
u
r
in
g
th
e
d
ec
o
m
p
o
s
itio
n
p
r
o
ce
s
s
,
p
ar
ticu
lar
ly
in
th
e
h
ig
h
-
f
r
eq
u
e
n
cy
co
m
p
o
n
e
n
ts
.
T
h
e
te
s
t
d
ata
d
is
p
lay
ed
a
MA
E
b
ec
a
u
s
e
o
f
th
e
co
m
b
in
ed
p
r
ed
ictio
n
s
f
r
o
m
b
o
th
SVR
m
o
d
els,
wh
ich
s
u
g
g
ests
a
h
ig
h
d
e
g
r
ee
o
f
ac
cu
r
ac
y
in
ac
cu
r
ately
p
r
e
d
ictin
g
tr
an
s
m
is
s
io
n
lo
s
s
es.
Fro
m
th
is
p
r
o
p
o
s
ed
m
o
d
el
MA
E
r
an
g
e
b
etwe
en
0
.
0
4
%
-
2
9
.
6
2
%,
th
e
av
er
ag
e
MA
E
ty
p
ically
ar
o
u
n
d
5
.
4
3
%.
L
ar
g
est
MA
E
v
alu
es,
in
ter
m
s
o
f
b
o
th
k
W
h
an
d
p
er
ce
n
ta
g
e,
ar
e
o
b
s
er
v
ed
d
u
r
in
g
p
e
r
io
d
s
o
f
s
u
b
s
tan
tial
ch
an
g
e
o
r
in
cr
ea
s
es
in
th
e
ac
tu
al
tr
a
n
s
m
is
s
io
n
lo
s
s
es.
I
t
is
p
o
s
s
ib
le
th
at
th
e
m
o
d
el
is
u
n
a
b
le
to
ac
c
u
r
at
ely
ca
p
tu
r
e
s
u
d
d
en
ch
an
g
es
o
r
f
lu
ct
u
atio
n
s
in
th
e
d
ata,
wh
ic
h
co
u
ld
b
e
a
r
esu
lt
o
f
eith
e
r
in
s
u
f
f
icien
t
tr
ain
in
g
d
ata
f
o
r
th
o
s
e
p
er
io
d
s
o
r
an
in
ca
p
ac
ity
o
f
th
e
m
o
d
el
to
ca
p
tu
r
e
h
ig
h
v
a
r
iab
ilit
y
in
th
e
u
n
d
er
ly
in
g
s
y
s
tem
.
Fig
u
r
e
4
s
h
o
ws
th
at
MA
E
f
r
o
m
E
E
MD
-
SVR
m
o
d
el.
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
P
r
ed
ictin
g
tr
a
n
s
mis
s
io
n
lo
s
s
e
s
u
s
in
g
E
E
MD
–
S
V
R
a
lg
o
r
ith
m
(
Hesti Tr
i
Les
ta
r
i
)
2127
Fig
u
r
e
4
.
Me
an
ab
s
o
lu
te
er
r
o
r
f
r
o
m
E
E
MD
-
SVR
m
o
d
el
4.
CO
NCLU
SI
O
N
T
h
e
J
av
a
-
B
ali
elec
tr
icity
s
y
s
t
em
in
I
n
d
o
n
esia’
s
lar
g
est
ele
ctr
icity
n
etwo
r
k
,
co
v
e
r
in
g
f
iv
e
r
eg
io
n
s
:
J
ak
ar
ta
-
B
an
ten
,
W
est
J
av
a,
C
en
tr
al
J
av
a,
E
ast
J
av
a,
an
d
B
ali.
T
h
e
s
y
s
tem
aim
s
to
ac
h
iev
e
ec
o
n
o
m
ic,
en
v
ir
o
n
m
en
tal
s
u
s
tain
ab
ilit
y
,
q
u
ality
,
an
d
r
eliab
ilit
y
o
b
jecti
v
es
.
Am
o
n
g
th
ese
o
b
jectiv
es,
ec
o
n
o
m
ic
ef
f
icien
c
y
is
cr
u
cial,
a
n
d
o
n
e
k
ey
asp
ec
t
is
r
ed
u
cin
g
tr
an
s
m
is
s
io
n
lo
s
s
es,
wh
ich
s
ig
n
i
f
ican
tly
im
p
ac
t
th
e
o
v
e
r
all
ef
f
icien
cy
an
d
ef
f
ec
tiv
e
n
ess
o
f
th
e
s
y
s
tem
.
I
n
o
u
r
s
tu
d
y
,
we
p
r
o
p
o
s
ed
an
E
E
MD
-
SVR
m
o
d
el
to
p
r
ed
ict
tr
an
s
m
is
s
io
n
lo
s
s
es
in
J
av
a
-
B
ali
elec
tr
icity
s
y
s
tem
.
T
h
is
m
o
d
el
ex
h
ib
its
th
e
ca
p
ac
ity
to
id
e
n
tify
n
o
n
-
lin
ea
r
r
elatio
n
s
h
ip
s
b
etwe
en
in
p
u
t
a
n
d
tar
g
et
v
ar
iab
les,
u
tili
zin
g
th
e
R
B
F
k
er
n
el
to
m
an
ag
e
d
ata
c
o
m
p
lex
ity
.
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
ac
h
iev
ed
a
MA
E
o
f
ap
p
r
o
x
im
ately
5
.
4
3
%,
with
p
r
e
d
ictio
n
ac
c
u
r
ac
y
r
an
g
i
n
g
f
r
o
m
0
.
0
4
%
to
2
9
.
6
2
%
d
ep
en
d
i
n
g
o
n
lo
ad
v
ar
iatio
n
.
T
h
e
m
o
d
el
ef
f
ec
tiv
e
ly
ca
p
tu
r
es
b
o
th
s
h
o
r
t
-
ter
m
f
l
u
ctu
atio
n
s
an
d
lo
n
g
-
ter
m
p
att
er
n
s
in
tr
an
s
m
is
s
io
n
lo
s
s
es.
Fro
m
a
p
r
ac
tical
s
tan
d
p
o
i
n
t
,
th
e
p
r
ed
ictiv
e
o
u
tp
u
t
o
f
th
e
m
o
d
el
ca
n
ass
is
t
s
y
s
tem
o
p
er
ato
r
s
an
d
p
lan
n
er
s
in
o
p
tim
izin
g
o
p
er
at
io
n
al
s
tr
ateg
ies.
Fo
r
in
s
tan
ce
,
p
r
ed
ictio
n
s
ca
n
b
e
in
teg
r
ated
in
to
lo
a
d
d
is
p
atch
p
lan
n
in
g
,
h
el
p
in
g
o
p
e
r
ato
r
s
r
er
o
u
te
p
o
wer
f
lo
w
t
h
r
o
u
g
h
m
o
r
e
e
f
f
icien
t
tr
a
n
s
m
is
s
io
n
c
o
r
r
id
o
r
s
o
r
s
ch
ed
u
le
g
en
er
atio
n
m
o
r
e
ec
o
n
o
m
ically
to
r
e
d
u
ce
m
a
r
g
in
al
lo
s
s
es.
T
h
e
m
o
d
el
en
ab
les
p
r
o
ac
tiv
e
m
itig
atio
n
d
u
r
in
g
p
er
io
d
s
o
f
p
r
ed
icted
h
ig
h
l
o
s
s
es
b
y
s
u
p
p
o
r
tin
g
d
ec
is
io
n
s
s
u
ch
as
r
ea
ctiv
e
p
o
wer
co
m
p
e
n
s
atio
n
,
g
e
n
er
ato
r
d
is
p
atch
r
ea
llo
ca
tio
n
s
,
an
d
d
em
an
d
r
esp
o
n
s
e
c
o
o
r
d
i
n
atio
n
.
Ad
d
itio
n
ally
,
p
la
n
n
er
s
ca
n
u
s
e
th
e
f
o
r
ec
ast
to
ju
s
tify
in
f
r
astru
ctu
r
e
r
ein
f
o
r
c
em
en
t
in
h
ig
h
-
lo
s
s
ar
ea
s
to
im
p
r
o
v
e
lo
n
g
-
ter
m
s
y
s
tem
ef
f
icien
cy
.
T
h
ese
ap
p
licatio
n
s
d
em
o
n
s
tr
ate
th
e
m
o
d
el’
s
p
o
ten
tial
n
o
t
o
n
ly
f
o
r
m
o
n
ito
r
in
g
b
u
t
also
f
o
r
g
u
id
in
g
p
o
licy
an
d
o
p
er
atio
n
al
d
ec
is
io
n
s
to
r
ed
u
c
e
tr
an
s
m
is
s
io
n
lo
s
s
es a
cr
o
s
s
th
e
J
av
a
-
B
ali
g
r
id
.
W
h
ile
th
e
m
o
d
el
p
e
r
f
o
r
m
s
ef
f
ec
tiv
ely
u
n
d
e
r
n
o
r
m
al
co
n
d
iti
o
n
s
,
lim
itatio
n
s
r
em
ain
in
r
esp
o
n
d
in
g
to
ab
r
u
p
t
s
p
ik
es
o
r
d
ip
s
in
lo
s
s
es.
T
h
er
ef
o
r
e,
f
u
tu
r
e
wo
r
k
s
h
o
u
l
d
f
o
cu
s
o
n
im
p
r
o
v
i
n
g
th
e
s
en
s
itiv
ity
o
f
th
e
m
o
d
el
to
r
ap
i
d
p
o
wer
s
y
s
tem
ch
an
g
e
s
,
p
o
s
s
ib
ly
th
r
o
u
g
h
th
e
i
n
teg
r
a
tio
n
o
f
a
d
ap
tiv
e
d
ec
o
m
p
o
s
itio
n
o
r
h
y
b
r
i
d
m
o
d
els
with
r
ea
l
-
tim
e
d
ata
p
r
o
ce
s
s
in
g
ca
p
ab
ilit
ies.
T
h
is
wo
r
k
h
as
s
u
cc
ess
f
u
lly
d
ev
elo
p
ed
a
p
r
ed
ictiv
e
m
o
d
el
to
aid
p
o
wer
s
y
s
tem
o
p
er
ato
r
s
in
m
o
n
ito
r
in
g
an
d
m
itig
atin
g
tr
an
s
m
is
s
io
n
lo
s
s
e
s
,
wh
ile
en
h
an
ce
m
en
ts
ar
e
n
ee
d
ed
to
ad
d
r
ess
ab
r
u
p
t
v
ar
iatio
n
s
in
l
o
s
s
d
ata.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
Au
th
o
r
s
s
tate
th
er
e
is
n
o
f
u
n
d
i
n
g
in
v
o
lv
ed
i
n
th
is
r
esear
ch
.
AUTHO
R
CO
NT
RI
B
UT
I
O
NS ST
A
T
E
M
E
N
T
T
h
is
jo
u
r
n
al
u
s
es
th
e
C
o
n
tr
ib
u
to
r
R
o
les
T
ax
o
n
o
m
y
(
C
R
ed
iT)
to
r
ec
o
g
n
ize
in
d
iv
id
u
al
au
th
o
r
co
n
tr
ib
u
tio
n
s
,
r
ed
u
ce
au
th
o
r
s
h
ip
d
is
p
u
tes,
an
d
f
ac
ilit
ate
co
llab
o
r
atio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
6
9
4
I
n
t J Po
w
E
lec
&
Dr
i Sy
s
t
,
Vo
l.
16
,
No
.
3
,
Sep
tem
b
er
20
25
:
212
2
-
21
29
2128
Na
m
e
o
f
Aut
ho
r
C
M
So
Va
Fo
I
R
D
O
E
Vi
Su
P
Fu
Hesti T
ri
L
estar
i
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
C
ath
er
in
e
O
liv
ia
Ser
ea
ti
✓
✓
✓
✓
✓
Ma
r
s
u
l Sir
eg
ar
✓
✓
✓
✓
✓
✓
✓
Kar
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R
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S
[
1
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W.
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.
[
3
]
D
.
K
.
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.
[
4
]
M
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5
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[
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.
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.
[
7
]
J.
H
e
n
g
s
t
,
T.
S
e
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,
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.
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g
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.
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y
,
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v
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f
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smis
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(
DHT
)
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[
8
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V
.
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.
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,
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.
[
9
]
D
.
Li
,
P
.
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u
,
J.
G
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d
Y
.
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u
,
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:
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[
1
0
]
J.
Y
u
,
F
.
Zh
o
u
,
K
.
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u
,
C
.
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e
,
J.
W
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d
C
.
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e
,
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a
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smiss
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ss p
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O
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f
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re
n
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s:
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3
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9
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.
[
1
1
]
J.
Tu
l
e
n
sa
l
o
,
J.
S
e
p
p
ä
n
e
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