I
nd
o
ne
s
ia
n J
o
urna
l o
f
E
lect
rica
l En
g
ineering
a
nd
Co
m
pu
t
er
Science
Vo
l.
25
,
No
.
1
,
J
an
u
ar
y
20
22
,
p
p
.
4
2
~
50
I
SS
N:
2
5
0
2
-
4
7
5
2
,
DOI
:
1
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1
1
5
9
1
/ijeecs.v
25
.i
1
.
pp
42
-
50
42
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Electrical lo
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F
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is p
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)
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stin
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w
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s
:
Daily
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s
af
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tin
g
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ter
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etwo
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t m
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q
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ar
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ev
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n
T
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s
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o
p
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c
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ss
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rticle
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n
d
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e
CC B
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SA
li
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se
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C
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uth
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d
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Salk
u
ti
Dep
ar
tm
en
t o
f
R
ailr
o
ad
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d
E
lectr
ical
E
n
g
in
ee
r
in
g
,
W
o
o
s
o
n
g
Un
iv
er
s
ity
J
ay
an
g
-
Do
n
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-
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,
Dae
j
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n
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R
ep
u
b
lic
o
f
Ko
r
ea
E
m
ail:
s
u
r
en
d
er
@
wsu
.
ac
.
k
r
1.
I
NT
RO
D
UCT
I
O
N
E
lectr
icity
is
an
ex
tr
em
ely
i
m
p
o
r
tan
t
s
o
u
r
ce
o
f
e
n
er
g
y
an
d
p
lay
s
a
s
ig
n
if
ican
t
r
o
le
in
a
co
u
n
tr
y
’
s
ec
o
n
o
m
ic
d
e
v
elo
p
m
e
n
t
[
1
]
.
L
o
ad
f
o
r
ec
asti
n
g
is
n
ec
ess
ar
y
f
o
r
th
e
p
r
o
p
e
r
f
u
n
ctio
n
in
g
o
f
elec
tr
ical
d
is
p
atch
ce
n
ter
s
.
L
o
ad
f
o
r
ec
asti
n
g
is
a
m
eth
o
d
u
s
ed
to
m
ain
t
ain
s
y
n
ch
r
o
n
icity
o
f
d
em
a
n
d
an
d
s
u
p
p
ly
o
f
elec
tr
ical
p
o
wer
.
W
ith
a
g
r
ea
ter
c
o
n
te
n
tio
n
f
o
r
th
e
m
ar
k
et
an
d
g
r
ea
ter
d
ec
en
t
r
aliza
tio
n
,
s
h
o
r
t
-
t
er
m
f
o
r
ec
asti
n
g
is
b
ec
o
m
in
g
m
o
r
e
s
ig
n
if
ica
n
t
[
2
]
.
I
n
an
a
g
e
wh
er
e
s
m
ar
t
g
r
id
s
with
ad
v
an
ce
d
s
en
s
in
g
an
d
co
m
m
u
n
icatio
n
ar
e
f
ast
b
ec
o
m
in
g
a
r
ea
lity
,
lo
ad
f
o
r
ec
asti
n
g
is
a
f
ield
wh
er
e
th
e
s
co
p
e
an
d
n
ec
ess
ity
o
f
ac
cu
r
ac
y
ar
e
in
cr
ea
s
in
g
d
ay
b
y
d
ay
[
3
]
.
Nu
m
e
r
o
u
s
s
ig
n
if
ican
t
d
ec
is
io
n
s
d
e
p
en
d
u
p
o
n
th
e
lo
ad
f
o
r
ec
asts
lik
e
e
co
n
o
m
ic
d
is
p
atch
,
d
is
tr
ib
u
tio
n
s
ch
ed
u
le,
s
ch
ed
u
l
e
o
f
p
r
o
tectio
n
,
an
d
m
ain
ten
a
n
ce
m
ea
s
u
r
es
[
4
]
.
Fro
m
p
r
o
p
er
m
ain
ten
an
ce
o
f
eq
u
ip
m
en
t
to
th
e
ec
o
n
o
m
ic
s
tr
ateg
ies
o
f
th
e
s
u
p
p
lier
s
,
lo
ad
f
o
r
ec
asti
n
g
h
as
a
s
ig
n
if
ican
t
im
p
ac
t
[
5
]
.
E
s
p
ec
ially
f
o
r
s
m
all
-
s
ca
le
co
n
s
u
m
p
tio
n
u
n
its
,
p
e
ak
l
o
ad
f
o
r
ec
asti
n
g
is
v
e
r
y
im
p
o
r
tan
t
[
6
]
.
Mo
r
eo
v
e
r
,
th
e
r
e
h
as
b
ee
n
an
in
c
r
ea
s
ed
ten
d
e
n
cy
o
f
win
te
r
s
b
ein
g
c
o
ld
er
an
d
s
u
m
m
er
s
b
ei
n
g
m
o
r
e
ex
tr
em
e
th
an
b
e
f
o
r
e.
T
h
er
ef
o
r
e,
g
r
ea
ter
u
s
e
o
f
eq
u
ip
m
en
t
lik
e
air
co
n
d
itio
n
er
s
a
n
d
h
ea
t
er
s
,
an
d
th
eir
u
s
e
h
as
b
ec
o
m
e
ev
e
n
m
o
r
e
f
r
eq
u
e
n
t [
7
]
.
T
h
is
h
as led
to
m
o
r
e
s
win
g
s
in
ter
m
s
o
f
p
ea
k
lo
ad
an
d
m
in
im
u
m
l
o
ad
.
Ma
n
y
f
ac
to
r
s
im
p
ac
t
elec
tr
ical
lo
ad
,
th
eir
in
ter
r
elatio
n
is
co
m
p
lex
an
d
s
o
is
th
e
ex
ten
t
to
wh
ich
o
n
e
f
ac
to
r
o
v
e
r
r
id
es
o
n
e
a
n
o
th
er
.
T
h
e
f
ac
to
r
s
ca
n
b
e
d
iv
id
ed
in
t
o
th
r
ee
ca
teg
o
r
ies
[
8
]
.
C
lim
ate
is
co
n
s
id
er
ed
th
e
m
o
s
t
im
p
o
r
tan
t
f
ac
to
r
[
9
]
.
T
h
e
s
h
o
r
t
-
ter
m
f
ac
to
r
s
:
T
h
ey
ar
e
f
ac
to
r
s
th
at
last
o
n
ly
a
li
ttle
d
u
r
atio
n
,
lik
e
a
s
u
d
d
en
wea
th
er
ch
an
g
e.
T
h
e
m
id
d
le
-
ter
m
f
ac
to
r
s
:
T
h
ey
last
f
o
r
a
s
u
b
s
t
an
tial
d
u
r
atio
n
an
d
h
av
e
a
d
is
tin
ct
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
E
lectrica
l lo
a
d
fo
r
ec
a
s
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g
th
r
o
u
g
h
l
o
n
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s
h
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r
t te
r
m
mem
o
r
y
(
Deb
a
n
i P
r
a
s
a
d
Mis
h
r
a
)
43
ch
ar
ac
ter
is
tic
th
at
g
o
v
er
n
s
th
e
co
r
r
esp
o
n
d
in
g
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o
ad
v
ar
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n
.
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r
e
x
am
p
le,
s
ea
s
o
n
al
clim
at
ic
v
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iatio
n
s
.
T
h
e
l
ong
-
ter
m
f
ac
to
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s
:
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ey
last
f
o
r
a
s
ig
n
if
ican
t
tim
e
p
e
r
io
d
,
a
n
d
u
s
u
ally
o
v
er
m
u
ltip
le
f
o
r
ec
asti
n
g
p
er
io
d
s
[
1
0
]
.
Fo
r
a
p
ar
ticu
lar
ar
ea
,
th
e
tem
p
er
atu
r
e
is
th
e
m
ea
s
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r
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f
t
h
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av
er
ag
e
war
m
th
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co
o
ln
ess
o
f
th
e
s
u
r
r
o
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n
d
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.
T
em
p
er
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r
e
is
f
a
r
m
o
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e
in
f
lu
e
n
tial th
an
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f
ac
to
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s
lik
e
wi
n
d
s
p
ee
d
a
n
d
clo
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d
co
v
er
[
1
1
]
.
W
h
e
n
t
h
e
t
e
m
p
e
r
a
t
u
r
e
f
a
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r
e
q
u
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r
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m
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e
n
e
r
g
y
[
1
2
]
.
Similar
ly
,
af
ter
a
tem
p
er
atu
r
e
r
is
e,
m
o
r
e
en
e
r
g
y
is
r
eq
u
i
r
ed
.
B
o
th
in
s
u
m
m
er
s
an
d
win
ter
s
,
th
er
e
is
a
s
tr
o
n
g
co
r
r
elatin
g
co
n
tr
ib
u
tio
n
b
etwe
en
tem
p
er
atu
r
e
an
d
lo
ad
cu
r
v
e.
T
h
er
e
is
p
o
s
itiv
e
co
-
r
elatio
n
f
o
r
s
u
m
m
er
s
,
i
.
e
.
with
tem
p
er
atu
r
e
r
is
e
in
s
u
m
m
er
lead
in
g
to
in
c
r
ea
s
ed
co
n
s
u
m
p
tio
n
o
f
elec
tr
ical
lo
ad
as
ap
p
lian
ce
s
s
u
ch
as
f
an
s
,
co
o
ler
s
,
a
n
d
air
co
n
d
itio
n
er
s
(
AC
s
)
,
ar
e
tu
r
n
ed
o
n
a
n
d
if
in
s
u
m
m
er
th
e
tem
p
e
r
a
tu
r
e
f
alls
,
th
e
s
am
e
ap
p
lian
ce
s
ar
e
tu
r
n
ed
o
f
f
f
o
r
l
es
s
er
lo
ad
co
n
s
u
m
p
tio
n
.
B
u
t
th
er
e
is
a
n
eg
ativ
e
co
-
r
elatio
n
f
o
r
win
ter
s
,
as
o
n
ly
wh
en
th
e
tem
p
er
at
u
r
e
f
alls
,
ap
p
lian
ce
s
u
s
ed
to
k
ee
p
th
e
h
o
u
s
eh
o
ld
s
war
m
ar
e
u
s
ed
.
Gen
er
a
lly
,
it
ca
n
b
e
s
ee
n
,
o
n
wo
r
k
in
g
d
a
y
s
th
er
e
ar
e
s
u
b
s
tan
tial
d
if
f
er
en
ce
s
in
lo
a
d
d
e
m
an
d
s
co
m
p
ar
ed
to
wo
r
k
i
n
g
d
ay
s
an
d
W
ee
k
en
d
s
.
T
h
er
e’
s
lo
wer
co
n
s
u
m
p
tio
n
o
n
T
u
esd
ay
s
to
T
h
u
r
s
d
a
y
s
wh
ile
o
n
wee
k
en
d
s
an
d
d
ay
s
clo
s
er
to
wee
k
en
d
s
s
u
ch
as
Mo
n
d
ay
s
an
d
Frid
a
y
s
th
e
co
n
s
u
m
p
tio
n
is
h
ig
h
er
[
1
3
]
.
An
o
th
er
tr
en
d
th
at
ca
n
b
e
o
b
s
er
v
ed
is
th
at
o
n
m
o
v
in
g
h
o
lid
a
y
s
:
Ho
lid
ay
s
wh
ich
d
o
n
o
t
h
av
e
a
n
y
f
ix
ed
d
ate,
e
.
g
.
th
e
r
elig
io
u
s
f
esti
v
als,
also
im
p
ac
t
th
e
f
o
r
ec
ast.
Gen
er
ally
,
o
n
d
ay
s
o
f
f
esti
v
als,
th
e
d
e
m
an
d
is
r
e
lativ
ely
h
ig
h
er
.
B
u
t
s
in
ce
in
d
u
s
tr
ial
ac
tiv
ities
ar
e
less
er
,
th
e
o
v
er
all
co
n
s
u
m
p
tio
n
p
r
ed
ictio
n
b
ec
o
m
es d
if
f
i
cu
lt.
I
n
a
b
r
o
a
d
s
en
s
e,
th
er
e
ar
e
t
wo
ty
p
es
o
f
m
o
d
els
p
r
o
p
o
s
e
d
o
r
u
s
ed
f
o
r
p
r
ed
ictin
g
f
u
t
u
r
e
elec
tr
ical
d
em
an
d
s
,
c
o
n
v
e
n
tio
n
al
s
tatis
ti
ca
l
tech
n
iq
u
es,
an
d
ar
tific
ial
i
n
tellig
en
ce
(
AI
)
b
ased
tech
n
iq
u
es.
C
lass
ic
m
o
d
els
u
s
e
h
is
to
r
ical
d
ata
an
d
p
r
o
c
ess
th
em
,
an
d
t
h
e
esti
m
ates
o
f
p
ar
am
eter
s
in
s
u
ch
m
o
d
els
ca
n
b
e
ea
s
ily
in
ter
p
r
eted
.
T
h
e
m
o
d
els
an
d
t
ec
h
n
iq
u
es
th
at
f
all
u
n
d
er
th
is
ca
teg
o
r
y
i
n
clu
d
e
a
u
to
r
eg
r
ess
iv
e
m
o
v
i
n
g
a
v
er
ag
e
(
AR
I
MA
)
m
o
d
el
[
1
4
]
,
th
e
r
e
g
r
e
s
s
i
o
n
s
e
as
o
n
a
l
AR
I
MA
g
e
n
er
a
l
i
z
e
d
a
u
t
o
r
e
g
r
es
s
i
v
e
c
o
n
d
i
t
i
o
n
a
l
h
e
t
e
r
o
s
k
e
d
as
t
ic
(
R
e
g
-
S
AR
I
MA
-
G
AR
C
H
)
m
o
d
el
[
1
5
]
,
s
u
p
p
o
r
t
v
ec
t
o
r
m
ac
h
in
e
m
o
d
els
[
1
6
]
.
T
im
e
s
er
ies
m
o
d
el
f
o
r
s
er
ies
ex
h
ib
itin
g
m
u
ltip
le
co
m
p
lex
s
ea
s
o
n
alities
(
T
B
AT
S)
[
1
7
]
.
A
I
tech
n
iq
u
es
o
n
th
e
o
th
er
h
a
n
d
p
r
o
v
e
t
o
b
e
m
o
r
e
f
lex
ib
le
d
u
e
to
th
eir
ab
ilit
y
to
ad
ap
t
to
m
o
v
in
g
d
ata.
T
h
e
AI
f
u
n
ctio
n
s
ar
e
n
o
n
lin
ea
r
an
d
n
o
n
p
a
r
am
etr
ic.
I
n
g
en
er
al,
th
e
AI
m
o
d
els
y
ield
b
etter
r
esu
lts
th
an
tr
ad
itio
n
a
l
o
n
es
[
1
8
]
.
Ne
u
r
al
n
etwo
r
k
s
an
d
d
ee
p
lear
n
in
g
m
o
d
els
h
av
e
p
r
o
v
en
t
o
b
e
m
o
r
e
ac
cu
r
ate
f
o
r
elec
tr
ical
lo
a
d
f
o
r
ec
asts
th
an
th
e
tr
a
d
itio
n
al
m
o
d
el.
W
ith
th
e
ad
v
en
t
o
f
s
m
ar
t
g
r
id
s
an
d
th
e
ev
er
-
d
iv
er
s
if
y
i
n
g
ap
p
licatio
n
o
f
d
ata
an
aly
tics
,
a
h
u
g
e
am
o
u
n
t
o
f
d
ata
in
f
lo
w
an
d
ev
er
-
i
n
cr
ea
s
in
g
ap
p
licati
o
n
s
b
ased
o
n
th
eir
an
aly
s
is
ar
e
ex
p
ec
ted
[
1
9
]
,
[
2
0
]
.
AI
an
d
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
es
ar
e
ex
p
ec
ted
to
f
i
n
d
a
v
a
r
i
e
t
y
o
f
u
s
es
n
o
t
o
n
l
y
in
l
o
a
d
f
o
r
e
c
a
s
t
i
n
g
b
u
t
a
ls
o
i
n
t
h
e
f
t
d
e
t
ec
t
i
o
n
[
2
1
]
,
p
r
o
t
e
c
t
i
o
n
a
n
d
s
a
f
e
t
y
o
f
n
u
c
l
e
a
r
[
2
2
]
a
n
d
t
h
e
r
m
a
l
p
o
w
e
r
p
l
a
n
t
s
[
2
3
]
a
l
o
n
g
w
i
t
h
p
o
w
e
r
p
r
i
c
e
d
e
t
e
r
m
i
n
a
t
i
o
n
[
2
4
]
.
T
h
is
p
ap
er
h
as
attem
p
ted
to
ex
p
lo
r
e
th
e
im
p
lem
en
tatio
n
o
f
r
elativ
ely
n
ewe
r
AI
tech
n
i
q
u
es
in
th
e
d
o
m
ain
o
f
elec
tr
ical
lo
a
d
f
o
r
e
ca
s
tin
g
.
Fo
r
ec
asti
n
g
f
o
r
elec
tr
ical
lo
ad
s
is
a
co
m
p
le
x
p
r
o
ce
s
s
th
at
is
p
r
o
n
e
to
s
lig
h
t
er
r
o
r
s
ev
en
wh
en
u
tm
o
s
t
ca
r
e
is
tak
en
in
ch
o
o
s
in
g
th
e
m
eth
o
d
s
.
T
h
is
o
cc
u
r
s
d
u
e
to
th
e
m
u
ltip
le
f
ac
to
r
s
in
f
lu
en
cin
g
l
o
ad
p
atter
n
s
.
E
v
e
n
s
u
ch
s
lig
h
t
er
r
o
r
s
ca
n
lead
t
o
g
r
av
e
co
n
s
eq
u
en
ce
s
to
p
o
we
r
s
y
s
tem
eq
u
ip
m
en
t
an
d
also
g
r
a
v
ely
im
p
ac
t
ec
o
n
o
m
ic
in
ter
ests
.
T
h
ese
p
atter
n
s
ar
e
s
o
m
etim
es
co
m
p
letely
in
d
e
p
en
d
e
n
t
o
f
e
ac
h
o
th
er
a
n
d
th
u
s
it
b
ec
o
m
es
in
h
er
en
tly
im
p
o
s
s
ib
le
to
f
in
d
a
c
o
-
r
elatio
n
.
T
h
is
p
a
p
er
in
s
p
ec
ts
th
e
u
s
e
o
f
th
e
lo
n
g
s
h
o
r
t
ter
m
m
em
o
r
y
(
L
STM
)
m
o
d
el
to
s
o
lv
e
t
h
is
co
m
p
le
x
p
r
o
b
lem
.
Fo
r
th
e
s
am
e,
we
h
a
v
e
to
en
s
u
r
e
th
at
th
e
d
ata
s
et
o
n
wh
ich
t
h
is
s
tu
d
y
is
to
b
e
d
o
n
e,
is
o
r
g
an
ically
d
y
n
am
ic
a
n
d
en
ca
p
s
u
lates
th
e
im
p
ac
t
o
f
all
th
e
f
ac
to
r
s
.
T
o
ac
h
iev
e
th
is
,
we
u
s
e
d
ata
tak
en
f
r
o
m
a
m
ajo
r
Dis
p
atch
C
en
ter
,
Sta
te
L
o
ad
Desp
atch
C
en
ter
(
SLDC
)
State
L
o
ad
Dis
p
atch
C
en
ter
,
lo
ca
ted
in
Delh
i,
o
n
e
o
f
th
e
b
ig
g
est
cit
ies
o
f
o
n
e
o
f
th
e
b
ig
g
est
cities
in
th
e
wo
r
ld
in
ter
m
s
o
f
ac
tiv
e
c
o
n
s
u
m
er
s
.
T
h
is
p
a
p
er
,
th
er
ef
o
r
e,
in
s
p
ec
ts
th
e
ap
p
licab
ilit
y
o
f
th
e
L
STM
m
o
d
el
in
lo
ad
f
o
r
ec
asti
n
g
o
v
e
r
a
d
y
n
am
ic
co
n
s
u
m
er
b
ase.
T
h
is
ca
n
cr
ea
te
a
p
latf
o
r
m
f
o
r
f
u
r
th
er
ex
p
lo
r
atio
n
o
f
t
h
e
p
r
o
b
lem
th
r
o
u
g
h
L
STM
u
s
in
g
o
p
tim
izer
s
an
d
s
u
p
p
o
r
tin
g
m
e
ch
an
is
m
s
.
L
STM
p
r
o
v
es
to
b
e
ap
p
r
ec
ia
b
ly
v
ia
b
le
in
h
an
d
lin
g
th
e
c
o
m
p
lex
p
r
o
b
lem
th
at
elec
tr
ical
lo
ad
f
o
r
ec
asti
n
g
p
r
esen
ts
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
Ou
r
f
o
c
u
s
in
th
is
p
ap
er
was
to
u
s
e
th
e
L
STM
m
o
d
el
to
c
o
r
r
e
ctly
p
r
ed
ict
t
h
e
elec
tr
ical
lo
a
d
.
L
STM
is
o
f
ten
u
s
ed
f
o
r
tim
e
s
er
ies
f
o
r
ec
asti
n
g
,
we
ch
o
s
e
to
te
s
t
h
o
w
ac
cu
r
ate
it
i
s
f
o
r
elec
tr
ical
lo
ad
f
o
r
ec
asti
n
g
.
T
o
im
p
lem
en
t
th
e
alg
o
r
ith
m
o
n
o
r
g
an
ic
a
n
d
p
o
ten
t
d
ata
s
et,
we
s
cr
ap
p
ed
th
e
s
ite
s
tate
lo
ad
d
is
p
atch
ce
n
t
r
e
,
Delh
i.
W
e
s
cr
ap
p
ed
th
r
o
u
g
h
t
h
e
d
ata
f
r
o
m
t
h
e
2
8
th
o
f
J
an
u
ar
y
to
th
e
2
8
th
o
f
Feb
r
u
a
r
y
.
P
ar
am
eter
s
f
o
r
ea
ch
ep
o
ch
o
f
m
o
d
el
d
ev
elo
p
m
en
t
an
d
tr
ain
in
g
a
r
e
p
r
esen
ted
i
n
T
ab
le
1
.
Fro
m
T
ab
le
1
,
2
0
e
p
o
c
h
s
wer
e
tak
en
,
with
a
b
atch
co
n
s
is
tin
g
o
f
4
6
0
0
d
at
a
p
o
in
ts
.
Fo
r
cr
o
ss
-
v
alid
atin
g
,
1
0
ep
o
ch
s
wer
e
tak
e
n
.
T
h
e
ep
o
ch
s
an
d
b
atch
s
ize
wer
e
d
ec
id
ed
b
ased
o
n
ca
lcu
la
tio
n
s
an
d
th
en
ap
p
r
o
x
im
ated
b
y
tr
ial
an
d
er
r
o
r
f
o
r
th
e
b
est p
o
s
s
ib
le
r
esu
lt.
Af
ter
th
e
f
o
r
ec
asts
,
r
o
o
t
m
ea
n
s
q
u
ar
e
er
r
o
r
is
u
s
ed
to
c
o
m
p
ar
e
t
h
e
ac
tu
al
lo
ad
to
th
e
f
o
r
ec
ast
d
o
n
e
b
y
th
e
m
o
d
el
an
d
r
ec
eiv
ed
s
atis
f
ac
to
r
y
r
esu
lts
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
25
,
No
.
1
,
J
an
u
ar
y
20
22
:
4
2
-
50
44
T
ab
le
1
.
Par
am
eter
s
f
o
r
ea
ch
e
p
o
ch
o
f
m
o
d
el
d
ev
el
o
p
m
en
t a
n
d
tr
ain
in
g
EPO
C
H
S
Ti
me
t
a
k
e
n
a
n
d
Ti
me
p
e
r
st
e
p
Lo
ss
V
a
l
u
e
Lo
ss
EPO
C
H
S
Ti
me
t
a
k
e
n
a
n
d
Ti
me
p
e
r
st
e
p
Lo
ss
V
a
l
u
e
Lo
ss
1
8
s
1
ms
/
st
e
p
0
.
0
3
2
0
0
.
0
1
9
0
11
6
s
1
ms
/
st
e
p
0
.
0
0
8
8
0
.
0
0
7
4
2
6
s
1
ms
/
st
e
p
0
.
0
2
3
5
0
.
0
1
3
7
12
6
s
1
ms
/
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e
p
0
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8
7
0
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0
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3
6
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1
ms
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e
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6
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0
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4
13
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1
ms
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0
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4
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ms
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ms
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0
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8
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4
0
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0
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0
6
6
s
1
ms
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e
p
0
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0
0
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9
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5
16
9
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1
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9
8
6
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1
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p
0
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7
s
1
ms
/
st
e
p
0
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0
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3
0
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0
0
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8
9
6
s
1
ms
/
st
e
p
0
.
0
0
9
1
0
.
0
0
7
8
19
6
s
1
ms
/
st
e
p
0
.
0
0
8
2
0
.
0
0
6
7
10
6
s
1
ms
/
st
e
p
0
.
0
0
9
0
0
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0
0
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6
20
7
s
1
ms
/
st
e
p
0
.
0
0
8
2
0
.
0
0
6
7
2
.
1
.
T
he
L
ST
M
m
o
del
T
r
ad
itio
n
al
n
eu
r
al
n
etwo
r
k
s
c
an
n
o
t
u
s
e
th
e
co
n
ce
p
t
o
f
m
e
m
o
r
y
.
T
h
ey
ca
n
’
t
u
s
e
th
e
k
n
o
wled
g
e
o
f
p
r
ev
io
u
s
s
tates.
T
h
is
is
a
m
aj
o
r
d
r
awb
ac
k
.
R
ec
u
r
r
en
t
n
eu
r
a
l
n
etwo
r
k
s
(
R
NN)
in
ter
m
s
o
f
ar
ch
itectu
r
e
is
n
o
t
th
at
d
if
f
er
en
t
f
r
o
m
th
e
co
n
v
en
tio
n
al
n
eu
r
al
n
etwo
r
k
.
An
R
NN
h
o
wev
er
is
ca
p
ab
le
o
f
l
ea
r
n
in
g
f
r
o
m
m
em
o
r
y
.
Fig
u
r
e
1
s
h
o
ws
tr
a
d
itio
n
al
n
e
u
r
al
n
etwo
r
k
s
,
it
is
clea
r
th
at
s
in
ce
th
e
o
u
tp
u
t
o
f
n
e
u
r
al
n
et
wo
r
k
s
d
o
esn
’
t
lo
o
p
b
ac
k
to
p
r
e
v
io
u
s
lay
er
s
,
th
e
p
r
ev
io
u
s
s
tates
h
av
e
n
o
co
n
t
r
ib
u
tio
n
t
o
war
d
s
f
u
tu
r
e
o
n
es.
Fig
u
r
e
2
s
h
o
ws
a
s
im
p
le
R
NN,
with
a
l
o
o
p
.
R
ec
u
r
r
en
t
n
eu
r
al
n
etwo
r
k
s
h
a
v
e
p
r
o
v
ed
to
b
e
p
o
wer
f
u
l
a
n
d
ac
cu
r
ate
in
th
eir
ap
p
licatio
n
.
L
STM
is
a
s
lig
h
t
ly
d
if
f
er
e
n
t
k
in
d
o
f
R
NN
th
at
o
v
er
co
m
es
s
o
m
e
s
h
o
r
tco
m
in
g
s
o
f
t
h
e
s
tan
d
ar
d
v
er
s
io
n
.
L
STM
d
o
es
n
o
t
s
u
f
f
er
f
r
o
m
t
h
e
s
h
o
r
t
-
ter
m
d
ep
e
n
d
en
c
y
p
r
o
b
lem
o
f
u
s
u
al
R
NNs.
R
ec
u
r
r
en
t
n
eu
r
al
n
etwo
r
k
s
ten
d
to
p
r
o
v
e
in
ef
f
ic
ien
t
wh
en
d
ata
s
h
o
ws
m
o
r
e
lo
n
g
ter
m
d
e
p
en
d
e
n
cy
th
an
s
h
o
r
t
ter
m
.
L
STM
d
o
es
n
o
t
h
av
e
th
is
is
s
u
e
an
d
is
co
n
s
id
er
ed
s
u
itab
le
f
o
r
tim
e
s
er
ies
m
o
d
ellin
g
.
L
STM
s
lik
e
R
NN
h
av
e
a
c
h
ain
-
lik
e
s
tr
u
ctu
r
e.
Ho
wev
er
,
in
L
STM
th
e
r
ep
ea
tin
g
m
o
d
u
le
h
as a
s
lig
h
tly
m
o
r
e
c
o
m
p
lex
s
tr
u
ct
u
r
e.
E
ac
h
m
o
d
u
le
h
as 4
lay
er
s
an
d
ea
ch
lay
er
in
ter
ac
ts
with
o
n
e
an
o
th
er
.
Fro
m
Fig
u
r
e
3
,
it
ca
n
s
ee
a
ce
ll
s
tate,
r
ep
r
esen
ted
b
y
th
e
to
p
li
n
e
r
u
n
n
i
n
g
th
r
o
u
g
h
th
e
en
tir
e
ch
ain
.
T
h
e
ce
ll
s
tate
o
n
ly
i
n
v
o
lv
es
a
f
ew
lin
ea
r
in
ter
ac
tio
n
s
.
L
STM
r
ep
ea
tin
g
m
o
d
u
le
ca
n
eith
er
attac
h
o
r
d
e
lete
th
e
in
f
o
r
m
atio
n
r
u
n
n
in
g
in
th
e
ce
ll st
ate.
T
h
is
is
ac
h
iev
ed
th
r
o
u
g
h
a
“Ga
te”.
Gate
is
m
ain
tain
ed
o
r
ch
a
n
g
es
th
e
c
ell
s
tate.
Fig
u
r
e
1
.
T
r
ad
itio
n
al
n
e
u
r
al
n
e
two
r
k
Fig
u
r
e
2
.
R
NN
d
iag
r
am
Fig
u
r
e
3
.
B
asic
s
tr
u
ctu
r
e
o
f
L
STM
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
E
lectrica
l lo
a
d
fo
r
ec
a
s
tin
g
th
r
o
u
g
h
l
o
n
g
s
h
o
r
t te
r
m
mem
o
r
y
(
Deb
a
n
i P
r
a
s
a
d
Mis
h
r
a
)
45
Fig
u
r
e
4
s
h
o
ws
h
o
w
o
n
e
o
f
t
h
e
r
ea
s
o
n
s
L
STM
is
d
if
f
er
en
t
f
r
o
m
R
NN
is
th
e
ab
s
en
ce
o
f
a
ce
ll
s
tate
.
T
h
e
ce
ll
s
tate
i
s
th
e
k
ey
to
L
STM
’
s
ab
ilit
y
to
r
ec
o
g
n
ize
s
h
o
r
t
-
ter
m
p
atter
n
s
.
Fig
u
r
e
5
h
ig
h
lig
h
ts
th
e
in
itial
s
tep
,
wh
ich
is
to
d
eter
m
in
e
i
f
t
h
e
in
co
m
in
g
in
f
o
r
m
atio
n
f
r
o
m
th
e
ce
ll st
ate
is
to
b
e
d
elete
d
o
r
n
o
t.
Fig
u
r
e
4
.
R
NN
d
o
es n
o
t h
av
e
t
h
e
ce
ll st
ates
Fig
u
r
e
5
.
Hig
h
lig
h
ti
n
g
a
ce
ll st
ate
T
h
e
in
itial
s
tep
is
to
d
eter
m
in
e
if
th
e
in
f
o
r
m
atio
n
f
r
o
m
a
n
in
co
m
in
g
ce
ll
s
tate
h
as
to
b
e
d
elete
d
.
T
h
is
is
d
eter
m
in
ed
b
y
th
e
“
f
o
r
g
et
g
ate
lay
er
”.
T
h
er
ea
f
ter
,
we
d
ec
id
e
if
we
h
av
e
to
ad
d
an
y
i
n
f
o
r
m
atio
n
.
T
h
is
is
d
iv
id
ed
in
to
two
s
tep
s
.
An
“in
p
u
t
Gate
lay
er
”
will
d
eter
m
in
e
th
e
v
alu
es
wh
ich
ar
e
to
b
e
u
p
d
ated
,
af
ter
war
d
a
tan
h
s
ec
tio
n
th
at
cr
ea
tes a
s
et
o
f
v
alu
e
th
at
is
to
b
e
ad
d
ed
in
ce
ll st
ate.
Her
ea
f
ter
,
th
ey
ar
e
co
m
b
in
ed
to
u
p
d
ate
th
e
s
tate.
T
o
ac
h
iev
e
th
at,
we
m
u
ltip
ly
th
e
o
l
d
s
tate
with
Fu
n
ctio
n
F(t)
.
An
d
we
ad
d
to
it,
I
t
∗
C
~
t.
Fin
ally
,
we
em
p
lo
y
a
s
ig
m
o
id
lay
er
wh
ich
d
eter
m
in
es
wh
at
will
b
e
t
h
e
o
u
tp
u
t.
W
e
n
o
w
u
s
e
ta
n
h
w
h
ich
r
estricts
v
alu
es
to
a
s
m
aller
r
an
g
e.
No
w
we
e
n
s
u
r
e
ce
r
tain
s
elec
ted
s
ec
tio
n
s
ar
e
tr
ea
ted
as o
u
tg
o
in
g
o
u
tp
u
t
u
s
in
g
an
o
t
h
er
g
ate.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
3
.
1
.
Da
t
a
s
et
s
o
urce
Fo
r
th
e
d
ata,
we
h
av
e
s
cr
ap
p
e
d
th
e
s
ite
o
f
SLDC
,
Delh
i.
T
h
e
SLDC
i
s
r
esp
o
n
s
ib
le
f
o
r
an
in
teg
r
ated
p
o
wer
s
u
p
p
ly
to
Delh
i.
T
h
e
s
ite
u
p
d
ates
lo
ad
d
ata
ev
er
y
f
iv
e
m
in
u
tes.
T
o
s
cr
ap
th
e
d
ata,
we
h
av
e
u
s
ed
B
ea
u
tifu
l
s
o
u
p
,
a
p
y
t
h
o
n
lib
r
ar
y
u
s
ed
f
o
r
b
asic
s
cr
ap
p
i
n
g
.
I
t
is
ca
p
ab
le
o
f
ex
tr
ac
tin
g
d
ata
f
r
o
m
h
y
p
er
tex
t
m
ar
k
u
p
lan
g
u
ag
e
an
d
ex
ten
s
ib
le
m
ar
k
u
p
la
n
g
u
a
g
e
d
o
cu
m
e
n
ts
.
W
e
to
o
k
lo
ad
d
ata
f
o
r
th
e
la
s
t
m
o
n
th
.
T
h
e
lo
a
d
d
ata
is
tak
en
ev
er
y
5
m
in
u
tes.
Fig
u
r
e
6
s
h
o
ws
th
e
lo
ad
d
ata
o
b
tain
ed
f
o
r
th
e
2
3
rd
o
f
Feb
r
u
ar
y
2
0
2
1
.
I
t
ca
n
b
e
s
e
e
n
,
t
h
e
l
o
a
d
d
e
m
a
n
d
e
d
i
s
l
e
s
s
e
r
i
n
t
h
e
n
i
g
h
t
a
n
d
p
e
a
k
s
d
u
r
i
n
g
a
t
i
m
e
s
p
a
n
o
f
9
t
o
1
2
o
’
c
l
o
c
k
s
p
a
n
.
I
n
F
i
g
u
r
e
7
we
s
ee
th
e
v
ar
iatio
n
o
f
lo
ad
f
r
o
m
2
8
th
o
f
J
an
u
ar
y
2
0
2
1
till
2
8
th
o
f
Feb
r
u
ar
y
2
0
2
1
.
I
t
s
h
o
w
s
th
e
en
tire
30
-
d
ay
p
er
io
d
lo
a
d
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
25
,
No
.
1
,
J
an
u
ar
y
20
22
:
4
2
-
50
46
Fig
u
r
e
6
.
L
o
ad
d
ata
o
n
a
p
ar
ti
cu
lar
d
ate
Fig
u
r
e
7
.
Var
iatio
n
o
f
l
o
ad
in
a
p
ar
ticu
lar
m
o
n
th
3
.
2
.
Da
t
a
clea
nin
g
a
nd
prepa
ra
t
io
n
No
w
u
s
in
g
s
ea
s
o
n
al
d
ec
o
m
p
o
s
e
f
r
o
m
Py
th
o
n
’
s
Stats
m
o
d
el
lib
r
ar
y
,
we
d
ec
o
m
p
o
s
e
th
e
d
ata
(
u
s
in
g
d
aily
f
r
e
q
u
en
c
y
as
a
b
asis
)
in
to
tr
en
d
s
,
s
ea
s
o
n
ality
,
a
n
d
r
esid
u
e.
Fig
u
r
e
8
s
h
o
ws
th
e
r
esu
lts
o
f
u
s
in
g
s
ea
s
o
n
a
l
d
ec
o
m
p
o
s
e,
th
e
to
p
m
o
s
t
is
th
e
ac
tu
al
o
b
s
er
v
ed
d
ata,
th
e
n
e
x
t
s
ec
tio
n
s
h
o
ws
th
e
p
r
ev
alen
t
tr
en
d
an
d
th
en
th
e
r
eg
u
lar
ity
o
f
s
tr
u
ctu
r
e
is
s
h
o
wn
b
y
th
e
s
ea
s
o
n
al
p
ar
t.
T
h
e
n
e
x
t sectio
n
is
th
e
r
esid
u
al
p
ar
t.
Fig
u
r
e
8
.
Ob
s
er
v
ed
,
tr
e
n
d
,
s
ea
s
o
n
al
,
an
d
r
esid
u
al
d
ata
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
E
lectrica
l lo
a
d
fo
r
ec
a
s
tin
g
th
r
o
u
g
h
l
o
n
g
s
h
o
r
t te
r
m
mem
o
r
y
(
Deb
a
n
i P
r
a
s
a
d
Mis
h
r
a
)
47
3
.
3
.
I
dentif
y
ing
t
he
t
re
nd
o
f
t
he
t
im
e
s
er
ies da
t
a
s
et
a
nd
det
re
nd
ing
Fig
u
r
e
8
s
h
o
ws
th
e
tr
en
d
o
f
d
ata,
wh
ich
ca
n
b
e
c
o
n
s
id
er
e
d
to
b
e
a
tim
e
s
er
ies
d
ata
s
et.
A
tr
en
d
is
d
ef
in
ed
as
a
r
e
g
u
lar
i
n
cr
ea
s
e
an
d
d
ec
r
ea
s
e
o
f
v
alu
es
o
v
er
th
e
m
ea
n
.
T
h
e
tr
en
d
p
r
esen
t
i
n
th
e
d
ata
s
et
is
o
f
s
to
ch
asti
c
ty
p
e.
Pan
d
a
et
a
l.
[
2
5
]
ar
g
u
ed
th
at
d
etr
e
n
d
in
g
ca
n
r
ed
u
ce
er
r
o
r
s
in
f
o
r
ec
asts
an
d
im
p
r
o
v
e
o
v
e
r
all
p
er
f
o
r
m
an
ce
.
T
h
u
s
r
em
o
v
al
o
f
th
is
tr
e
n
d
ca
n
im
p
r
o
v
e
th
e
f
o
r
ec
asti
n
g
ab
ilit
y
o
f
th
e
m
o
d
e
l.
T
h
e
r
em
o
v
al
o
f
a
tr
en
d
is
ca
lled
d
etr
en
d
in
g
.
Detr
en
d
in
g
m
u
s
t
b
e
d
o
n
e
with
p
r
o
p
er
m
et
h
o
d
s
el
s
e
b
ec
o
m
es
d
etr
im
en
tal.
Detr
en
d
in
g
d
o
esn
’
t
alwa
y
s
i
m
p
r
o
v
e
p
e
r
f
o
r
m
an
ce
,
s
p
ec
if
ic
ally
f
o
r
m
ac
h
in
e
lear
n
in
g
ap
p
licatio
n
s
.
Ho
wev
er
,
we
h
av
e
ch
o
s
en
to
d
etr
e
n
d
o
u
r
d
ata
b
ec
au
s
e
it h
as p
r
o
v
en
to
b
e
b
en
ef
icial
f
o
r
tim
e
s
er
ies f
o
r
ec
asti
n
g
[
2
6
]
.
3
.
4
.
Re
m
o
v
ing
s
ea
s
o
na
lity
a
nd
re
s
ca
lin
g
t
he
da
t
a
S
e
a
s
o
n
al
i
t
y
r
e
f
e
r
s
t
o
r
e
g
u
l
a
r
l
y
r
e
p
e
a
t
i
n
g
p
a
tt
e
r
n
s
i
n
t
h
e
d
a
t
a
s
e
t
.
Se
a
s
o
n
a
l
c
o
m
p
o
n
e
n
t
s
t
e
n
d
to
o
b
s
c
u
r
e
t
h
e
a
c
t
u
a
l
d
a
t
a
p
a
t
te
r
n
t
h
a
t
i
s
s
i
g
n
i
f
i
c
a
n
t
f
o
r
m
o
d
e
l
i
n
g
[
2
7
]
,
[
2
8
]
.
I
n
t
h
i
s
w
o
r
k
,
a
d
i
f
f
e
r
e
n
t
m
e
t
h
o
d
t
o
r
e
m
o
v
e
s
e
as
o
n
a
l
i
t
y
f
o
r
o
u
r
d
a
t
a
s
e
t
.
No
w
b
e
f
o
r
e
t
h
e
d
a
t
a
s
e
t
c
a
n
b
e
u
s
e
d
f
o
r
t
r
a
i
n
i
n
g
a
n
d
f
i
t
ti
n
g
in
t
o
t
h
e
n
e
t
w
o
r
k
,
i
t
s
h
o
u
l
d
b
e
s
c
a
l
e
d
d
o
w
n
t
o
m
u
ch
l
o
w
e
r
v
a
l
u
e
s
s
o
t
h
a
t
t
h
o
s
e
p
r
o
c
e
s
s
i
n
g
a
r
e
f
a
s
t
e
r
a
n
d
m
o
r
e
e
f
f
i
c
i
e
n
t
[
2
9
]
,
[
3
0
]
.
W
e
h
av
e
s
ca
led
d
o
wn
o
u
r
d
at
a
to
lie
b
etwe
en
-
1
to
1
.
Fig
u
r
e
9
s
h
o
ws
th
e
d
ata
af
ter
d
etr
en
d
in
g
,
r
e
m
o
v
al
o
f
s
ea
s
o
n
ality
,
an
d
r
escalin
g
.
Fig
u
r
e
9
.
Detr
en
d
ed
a
n
d
r
esca
led
d
ata
3
.
5
.
M
o
del
t
ra
ini
ng
a
nd
f
o
re
ca
s
t
First
d
ata
i
s
r
esh
ap
ed
an
d
tr
ea
ted
to
s
tar
t
th
e
m
o
d
el
tr
ain
in
g
.
Fro
m
Ker
as
lay
er
s
,
we
d
ir
ec
tly
in
v
o
k
e
L
STM
an
d
f
r
o
m
Ker
as.
Mo
d
els
we
in
v
o
k
e
s
eq
u
en
tially
,
n
o
w
we
h
a
v
e
t
o
d
ec
id
e
t
h
e
ep
o
c
h
s
an
d
b
atc
h
s
ize
[
3
1
]
,
[
3
2
]
.
W
e
h
av
e
d
ec
id
ed
to
r
u
n
th
e
m
o
d
el
tr
ain
in
g
f
o
r
3
0
ep
o
ch
s
th
e
b
atch
s
ize
h
as b
ee
n
tak
e
n
as
o
n
e.
Af
ter
tr
ain
in
g
f
o
r
3
0
ep
o
ch
s
a
n
d
cr
o
s
s
-
v
alid
atin
g
as
well,
th
e
m
o
d
el
is
r
ea
d
y
f
o
r
m
ak
in
g
f
o
r
ec
asts
.
Fig
u
r
e
1
0
s
h
o
ws
th
e
lo
ad
f
o
r
ec
ast.
R
o
o
t
m
ea
n
s
q
u
ar
e
e
r
r
o
r
(
R
MSE
)
o
r
r
o
o
t
m
ea
n
s
q
u
ar
e
d
er
r
o
r
is
o
n
e
o
f
t
h
e
s
tan
d
a
r
d
er
r
o
r
p
ar
am
eter
s
wh
en
o
n
ly
t
wo
d
im
en
s
io
n
s
ar
e
in
v
o
lv
ed
.
I
n
th
is
wo
r
k
,
R
MSE
is
s
elec
t
ed
as
it
d
o
esn
’
t
g
et
af
f
ec
ted
b
y
th
e
cu
r
s
e
o
f
d
im
e
n
s
io
n
ality
.
Fig
u
r
e
1
0
.
L
o
ad
f
o
r
ec
ast
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
25
,
No
.
1
,
J
an
u
ar
y
20
22
:
4
2
-
50
48
3
.
6
.
Co
m
pa
riso
n o
f
f
o
re
ca
s
t
wit
h a
ct
ua
l lo
a
d
Fig
u
r
e
1
1
s
h
o
ws
a
co
m
p
ar
is
o
n
b
etwe
en
f
o
r
ec
asts
an
d
ac
tu
a
l
lo
ad
.
T
h
e
f
o
r
ec
ast
is
d
ep
icte
d
in
o
r
a
n
g
e
co
lo
r
,
wh
ile
th
e
b
lu
e
g
r
ap
h
r
ep
r
esen
ts
ac
tu
al
lo
ad
[
3
3
]
.
I
t
s
h
o
ws
ap
p
r
ec
iab
le
ac
c
u
r
ac
y
e
x
ce
p
t
f
o
r
a
d
is
tin
ct
r
eg
io
n
lo
ca
te
d
lef
t to
th
e
m
id
d
le
m
ar
k
.
T
h
e
R
MSE
ev
alu
atio
n
r
ev
ea
l
s
an
er
r
o
r
o
f
1
2
7
W
,
wh
ich
is
well
with
in
th
e
r
a
n
g
e
o
f
a
p
p
r
ec
iab
le
ac
cu
r
ac
y
,
is
ab
o
u
t
4
.
1
%
to
3
.
2
%
o
f
th
e
o
b
s
er
v
ed
r
an
g
e
o
f
p
ea
k
lo
ad
ex
p
e
r
ien
ce
d
in
a
d
ay
.
T
h
u
s
l
o
n
g
s
h
o
r
t
ter
m
n
eu
r
al
n
etwo
r
k
m
o
d
els
s
h
o
w
s
ig
n
if
ican
t
ac
cu
r
ac
y
i
n
ter
m
s
o
f
l
o
ad
f
o
r
ec
asti
n
g
.
Ho
wev
er
,
f
u
r
th
er
ac
cu
r
ac
y
is
r
eq
u
i
r
ed
t
o
e
n
s
u
r
e
r
ea
l
life
ap
p
licatio
n
in
ac
tu
al
p
o
wer
p
lan
ts
wh
er
e
a
d
if
f
er
e
n
ce
o
f
4
.
1
to
3
.
2
%
m
ig
h
t im
p
ly
a
d
if
f
er
e
n
ce
o
f
th
e
m
ag
n
itu
d
es o
f
1
0
0
0
s
o
f
KW
s
.
Fig
u
r
e
1
1
.
C
o
m
p
ar
is
o
n
b
etwe
en
ac
tu
al
lo
ad
a
n
d
lo
a
d
f
o
r
ec
a
s
t
4.
CO
NCLU
SI
O
N
L
STM
s
h
o
ws
ap
p
r
ec
iab
le
ac
cu
r
ac
y
f
o
r
elec
tr
ical
lo
ad
f
o
r
ec
asts
.
I
t
o
u
tp
er
f
o
r
m
s
tr
ad
itio
n
al
s
tati
s
tica
l
p
r
ed
ictio
n
m
o
d
els
an
d
also
o
u
tp
er
f
o
r
m
s
m
a
n
y
ea
r
lier
u
s
ed
s
tan
d
ar
d
R
NN
m
eth
o
d
s
.
Ho
we
v
er
,
r
o
o
m
f
o
r
er
r
o
r
p
er
s
is
ts
.
T
h
er
ef
o
r
e,
L
STM
c
an
b
e
en
h
a
n
c
ed
with
th
e
a
d
d
itio
n
o
f
o
t
h
er
tech
n
iq
u
es.
T
h
e
L
STM
m
o
d
el
s
u
p
p
o
r
ted
b
y
p
in
b
all
lo
s
s
r
esu
lts
in
b
etter
p
er
f
o
r
m
a
n
ce
th
an
a
s
tan
d
ar
d
o
n
e.
A
n
o
th
er
m
et
h
o
d
is
to
u
s
e
v
ar
io
u
s
o
p
tim
izer
s
to
im
p
r
o
v
e
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
L
STM
n
etwo
r
k
an
d
th
e
n
u
s
in
g
it
f
o
r
th
e
f
o
r
ec
asts
.
L
STM
s
ca
n
p
r
o
v
e
to
b
e
q
u
ite
ef
f
icien
t
f
o
r
lo
ad
f
o
r
ec
asts
,
esp
ec
ially
if
f
u
r
th
er
im
p
r
o
v
e
m
en
ts
ar
e
a
p
p
lie
d
to
t
h
e
m
o
d
el.
T
h
e
p
r
o
p
o
s
ed
m
eth
o
d
o
lo
g
y
is
s
u
b
j
ec
ted
to
a
d
y
n
am
ic
d
ata
s
et.
F
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ia.
His
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
P
o
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r
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e
c
tro
n
ics
,
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o
a
d
F
o
re
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a
stin
g
,
P
o
we
r
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rid
,
a
n
d
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a
c
h
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rn
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g
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c
a
n
b
e
c
o
n
tac
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b
y
e
m
a
il
:
b
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1
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0
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it
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b
h
.
a
c
.
i
n
.
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r
e
n
d
e
r
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d
d
y
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a
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k
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th
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h
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d
e
g
re
e
in
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e
c
t
rica
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g
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e
e
rin
g
fro
m
th
e
In
d
ian
In
stit
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te
o
f
Tec
h
n
o
lo
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w
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lh
i,
In
d
ia,
in
2
0
1
3
.
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wa
s
a
P
o
st
d
o
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t
o
ra
l
Re
se
a
rc
h
e
r
with
Ho
wa
rd
Un
iv
e
rsity
,
Was
h
in
g
to
n
,
DC,
USA,
f
ro
m
2
0
1
3
t
o
2
0
1
4
.
He
is
c
u
rre
n
tl
y
a
n
As
so
c
iate
P
ro
fe
ss
o
r
with
th
e
De
p
a
rtme
n
t
o
f
Ra
il
ro
a
d
a
n
d
El
e
c
tri
c
a
l
En
g
i
n
e
e
rin
g
,
Wo
o
s
o
n
g
Un
i
v
e
rsity
,
Da
e
jeo
n
,
S
o
u
t
h
Ko
re
a
.
His
c
u
rre
n
t
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
p
o
we
r
sy
ste
m
re
stru
c
tu
rin
g
issu
e
s,
a
n
c
il
lary
se
rv
ice
p
ricin
g
,
re
a
l
a
n
d
re
a
c
ti
v
e
p
o
we
r
p
rici
n
g
,
c
o
n
g
e
sti
o
n
m
a
n
a
g
e
m
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n
t,
a
n
d
m
a
rk
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t
c
lea
rin
g
,
in
c
l
u
d
i
n
g
re
n
e
wa
b
le
e
n
e
rg
y
so
u
rc
e
s,
d
e
m
a
n
d
re
sp
o
n
se
,
sm
a
rt
g
r
id
d
e
v
e
lo
p
m
e
n
t
wit
h
i
n
teg
ra
ti
o
n
o
f
wi
n
d
a
n
d
so
lar
p
h
o
to
v
o
lt
a
ic
e
n
e
r
g
y
so
u
rc
e
s,
a
rti
ficia
l
in
telli
g
e
n
c
e
a
p
p
li
c
a
ti
o
n
s
in
p
o
we
r
sy
ste
m
s,
a
n
d
p
o
we
r
sy
ste
m
a
n
a
ly
sis
a
n
d
o
p
ti
m
iz
a
ti
o
n
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
su
re
n
d
e
r@ws
u
.
a
c
.
k
r
.
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