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[
1
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
:
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5
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52
In
d
o
n
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J
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n
g
&
C
o
m
p
Sci
,
Vo
l.
3
8
,
No
.
2
,
May
20
2
5
:
7
2
3
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3
1
724
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,
g
r
id
s
tab
ilit
y
an
d
r
eliab
ilit
y
,
an
d
en
e
r
g
y
e
f
f
icien
cy
[
5
]
.
Un
d
er
s
tan
d
in
g
an
d
ad
d
r
ess
in
g
th
ese
d
if
f
icu
lties
i
s
es
s
en
tial
f
o
r
d
ev
elo
p
in
g
f
u
t
u
r
e
-
p
r
o
o
f
s
m
ar
t
p
o
wer
s
y
s
tem
s
.
Key
tr
ad
itio
n
al
p
o
wer
s
y
s
tem
is
s
u
es a
r
e
lis
ted
b
elo
w
[
6
]
,
[
7
]
.
E
lectr
icity
d
em
an
d
r
is
es
with
p
o
p
u
latio
n
g
r
o
wth
,
u
r
b
a
n
i
za
tio
n
,
an
d
tech
n
o
lo
g
y
u
s
e.
T
o
m
ee
t
d
em
an
d
,
tr
ad
itio
n
al
p
o
wer
s
y
s
tem
s
m
u
s
t
ex
p
an
d
g
en
er
ati
o
n
,
tr
a
n
s
m
is
s
io
n
,
an
d
d
is
tr
ib
u
tio
n
.
No
t
m
ee
tin
g
in
cr
ea
s
ed
d
em
an
d
ca
n
ca
u
s
e
en
er
g
y
s
h
o
r
tag
es,
b
lack
o
u
ts
,
an
d
g
r
id
in
s
tab
ilit
y
.
E
f
f
ec
tiv
e
en
er
g
y
m
an
a
g
em
en
t
cu
ts
co
s
ts
,
m
ax
im
izes
en
er
g
y
u
s
e,
an
d
r
ed
u
ce
s
en
v
ir
o
n
m
en
t
al
ef
f
ec
t.
C
en
tr
alize
d
co
n
tr
o
l a
n
d
lim
ited
r
ea
l
-
tim
e
d
ata
ca
n
m
ak
e
en
er
g
y
m
an
a
g
em
en
t
in
ef
f
icien
t
in
tr
ad
itio
n
a
l
p
o
wer
s
y
s
tem
s
.
Po
wer
s
y
s
tem
s
m
u
s
t
o
p
tim
ize
en
er
g
y
c
o
n
s
u
m
p
tio
n
,
s
u
p
p
ly
,
p
r
ice,
s
to
r
ag
e
,
a
n
d
r
en
ewa
b
le
en
er
g
y
av
ailab
ilit
y
[
8
]
.
C
y
b
e
r
attac
k
s
an
d
illeg
al
ac
ce
s
s
to
v
ital
in
f
r
astru
ctu
r
e
ar
e
b
ec
o
m
in
g
m
o
r
e
lik
ely
as
p
o
wer
s
y
s
tem
s
b
ec
o
m
e
m
o
r
e
d
ig
ital
an
d
in
ter
co
n
n
ec
ted
.
Po
wer
s
y
s
tem
cy
b
er
-
d
ef
en
s
e
a
n
d
d
ata
p
r
i
v
a
cy
an
d
in
teg
r
ity
ar
e
cr
u
cial
to
g
r
id
s
ec
u
r
ity
an
d
r
eliab
ilit
y
.
E
n
cr
y
p
tio
n
,
in
tr
u
s
i
o
n
d
etec
tio
n
,
a
n
d
s
ec
u
r
e
co
m
m
u
n
icatio
n
p
r
o
t
o
co
ls
ar
e
ess
e
n
tial
to
r
e
d
u
ce
th
ese
h
az
ar
d
s
[
9
]
.
A
d
v
an
ce
d
tech
n
o
lo
g
y
,
d
ata
-
d
r
i
v
en
tec
h
n
iq
u
es,
an
d
in
tellig
en
t
s
y
s
tem
s
ar
e
n
e
ed
ed
to
s
o
lv
e
t
h
ese
p
r
o
b
lem
s
.
Dee
p
lear
n
in
g
ca
n
h
elp
s
m
ar
t
p
o
wer
s
y
s
tem
s
o
v
er
c
o
m
e
th
ese
is
s
u
es
b
y
p
r
o
v
id
i
n
g
ac
c
u
r
ate
f
o
r
ec
asti
n
g
,
r
ea
l
-
tim
e
m
o
n
ito
r
in
g
,
f
a
u
lt d
etec
tio
n
,
o
p
tim
izatio
n
,
an
d
r
o
b
u
s
t c
o
n
tr
o
l sch
em
e
s
.
2.
M
E
T
H
O
D
Dee
p
lear
n
in
g
is
a
s
u
b
s
et
o
f
m
ac
h
in
e
lear
n
in
g
th
at
f
o
cu
s
es
o
n
tr
ain
in
g
ar
tific
ial
n
eu
r
al
n
et
wo
r
k
s
with
m
u
ltip
le
lay
er
s
t
o
lear
n
a
n
d
ex
tr
ac
t
m
ea
n
i
n
g
f
u
l
r
e
p
r
esen
ta
tio
n
s
f
r
o
m
lar
g
e
-
s
ca
le
d
ata.
T
h
ese
n
etwo
r
k
s
ar
e
ca
p
ab
le
o
f
au
to
m
atica
lly
lea
r
n
in
g
h
ier
ar
ch
ical
f
ea
tu
r
es
a
n
d
p
atter
n
s
,
en
ab
lin
g
th
em
to
m
ak
e
ac
c
u
r
ate
p
r
ed
ictio
n
s
,
class
if
y
d
a
ta,
an
d
g
en
e
r
ate
v
al
u
ab
le
i
n
s
ig
h
ts
.
Dee
p
lear
n
in
g
tec
h
n
iq
u
es
h
av
e
b
ee
n
s
u
cc
ess
f
u
lly
ap
p
lied
i
n
v
ar
io
u
s
d
o
m
ain
s
,
in
clu
d
in
g
co
m
p
u
ter
v
is
io
n
,
n
atu
r
al
lan
g
u
ag
e
p
r
o
ce
s
s
in
g
,
an
d
s
p
ee
ch
r
ec
o
g
n
itio
n
.
I
n
th
e
co
n
tex
t
o
f
s
m
ar
t
p
o
wer
s
y
s
tem
s
,
d
ee
p
lea
r
n
in
g
o
f
f
er
s
s
ev
e
r
al
ad
v
an
tag
es
f
o
r
a
d
d
r
ess
in
g
co
m
p
le
x
ch
allen
g
es
an
d
o
p
tim
izin
g
e
n
er
g
y
m
an
a
g
em
en
t
[
1
0
]
–
[
1
2
]
.
Dee
p
lear
n
in
g
tech
n
iq
u
es
h
av
e
b
ee
n
wid
ely
ad
o
p
ted
i
n
v
ar
io
u
s
ap
p
licatio
n
s
with
in
s
m
ar
t p
o
wer
s
y
s
tem
s
.
Op
tim
izin
g
en
er
g
y
g
e
n
er
atio
n
,
s
ch
ed
u
lin
g
,
an
d
r
eso
u
r
ce
allo
c
atio
n
r
eq
u
ir
es
lo
ad
f
o
r
ec
asti
n
g
.
r
ec
u
r
r
en
t
n
e
u
r
al
n
etwo
r
k
s
(
R
NNs
)
an
d
lo
n
g
s
h
o
r
t
-
ter
m
m
e
m
o
r
y
(
L
STM
)
n
etwo
r
k
s
o
u
tp
e
r
f
o
r
m
lo
a
d
esti
m
ates
b
y
ca
p
tu
r
in
g
tem
p
o
r
al
r
elatio
n
s
h
ip
s
,
wea
th
er
,
an
d
h
is
to
r
i
ca
l
lo
ad
tr
en
d
s
.
T
h
ese
m
o
d
e
ls
im
p
r
o
v
e
en
er
g
y
p
lan
n
in
g
a
n
d
g
r
id
o
p
e
r
atio
n
b
y
p
r
ec
is
ely
p
r
ed
ictin
g
s
h
o
r
t
-
a
n
d
lo
n
g
-
ter
m
lo
a
d
.
Dee
p
lear
n
in
g
alg
o
r
ith
m
s
ca
n
d
etec
t
p
o
wer
s
y
s
tem
is
s
u
es,
d
ec
r
ea
s
in
g
d
o
w
n
tim
e.
Usi
n
g
h
i
s
to
r
ical
an
d
r
ea
l
-
tim
e
s
en
s
o
r
d
ata,
d
ee
p
lear
n
in
g
m
o
d
els
m
ay
lear
n
n
o
r
m
al
s
y
s
tem
b
eh
av
io
r
an
d
f
in
d
p
r
o
b
lem
s
.
D
ee
p
b
elief
n
etwo
r
k
s
,
au
to
en
co
d
er
s
,
an
d
r
ec
u
r
r
en
t
n
eu
r
al
n
etwo
r
k
s
ca
n
d
is
co
v
er
f
laws
ea
r
ly
an
d
en
h
a
n
ce
m
ain
ten
an
ce
.
Dee
p
lear
n
i
n
g
ca
n
id
en
tif
y
an
d
m
itig
ate
p
o
wer
s
y
s
tem
cy
b
er
attac
k
s
.
B
y
m
o
n
ito
r
in
g
n
etwo
r
k
tr
af
f
ic,
s
y
s
tem
lo
g
s
,
a
n
d
s
ec
u
r
ity
s
en
s
o
r
d
ata,
d
ee
p
lear
n
in
g
m
o
d
els
ca
n
d
etec
t
cy
b
e
r
attac
k
s
.
Dee
p
l
ea
r
n
in
g
im
p
r
o
v
es
c
y
b
er
s
ec
u
r
ity
b
y
d
etec
tin
g
ab
n
o
r
m
alities
,
in
tr
u
s
io
n
s
,
a
n
d
p
o
wer
s
y
s
tem
ar
ch
itectu
r
e
wea
k
n
ess
es.
Sm
ar
t
p
o
wer
s
y
s
tem
en
er
g
y
m
an
ag
em
en
t
o
p
tim
izatio
n
r
eli
es
o
n
d
ee
p
lear
n
in
g
.
Dee
p
lea
r
n
in
g
alg
o
r
i
th
m
s
an
aly
ze
h
is
to
r
ical
en
er
g
y
u
s
ag
e,
wea
th
er
,
an
d
p
r
icin
g
d
ata
to
d
eliv
er
r
ea
l
-
tim
e
en
er
g
y
d
em
an
d
r
esp
o
n
s
e,
o
p
tim
al
r
eso
u
r
ce
s
ch
ed
u
lin
g
,
an
d
ef
f
icien
t
en
er
g
y
d
is
p
atch
.
R
ein
f
o
r
ce
m
e
n
t
lear
n
in
g
(
R
L
)
allo
ws
au
to
n
o
m
o
u
s
en
er
g
y
o
p
t
im
izatio
n
d
ec
is
io
n
-
m
ak
in
g
b
ased
o
n
ta
r
g
ets an
d
li
m
itatio
n
s
[
1
3
]
,
[
1
4
]
.
T
h
ese
ap
p
licatio
n
s
d
em
o
n
s
tr
ate
d
ee
p
lear
n
in
g
'
s
v
er
s
atility
an
d
ef
f
icac
y
i
n
s
o
lv
in
g
c
h
allen
g
in
g
s
m
ar
t
p
o
wer
s
y
s
tem
p
r
o
b
lem
s
.
Dee
p
lear
n
in
g
al
g
o
r
ith
m
s
ca
n
h
a
n
d
le
m
ass
iv
e
v
o
lu
m
es
o
f
d
at
a,
id
en
tif
y
co
m
p
lex
p
atter
n
s
,
an
d
g
en
er
ate
ac
c
u
r
a
te
p
r
ed
ictio
n
s
,
b
o
o
s
tin
g
en
er
g
y
ef
f
icien
c
y
,
g
r
id
d
e
p
en
d
a
b
ilit
y
,
an
d
r
eso
u
r
ce
u
s
ag
e.
T
h
is
m
eth
o
d
is
e
x
p
la
in
ed
in
Fig
u
r
e
1
.
W
h
ile
tr
a
d
itio
n
al
d
ee
p
lear
n
in
g
tech
n
i
q
u
es
h
a
v
e
s
h
o
w
n
r
em
ar
k
ab
le
ca
p
a
b
ilit
ies
in
v
ar
io
u
s
ap
p
licatio
n
s
,
th
er
e
ar
e
s
ev
er
al
en
h
an
ce
d
d
ee
p
lear
n
in
g
tech
n
iq
u
es
th
at
ca
n
f
u
r
th
er
au
g
m
e
n
t
th
eir
ef
f
ec
tiv
en
ess
in
s
m
ar
t
p
o
wer
s
y
s
tem
s
.
T
h
ese
tech
n
iq
u
es
en
ab
le
im
p
r
o
v
ed
p
er
f
o
r
m
a
n
ce
,
r
o
b
u
s
tn
ess
,
in
ter
p
r
eta
b
ilit
y
,
a
n
d
tr
an
s
f
er
a
b
ilit
y
o
f
d
ee
p
lea
r
n
in
g
m
o
d
els
[
1
5
]
.
So
m
e
o
f
th
e
en
h
an
ce
d
d
ee
p
lear
n
in
g
tech
n
iq
u
es r
elev
a
n
t t
o
s
m
ar
t p
o
wer
s
y
s
tem
s
ar
e
o
u
t
lin
ed
b
elo
w:
R
NNs
ar
e
u
s
ef
u
l
f
o
r
p
o
wer
s
y
s
tem
tim
e
s
er
ies
an
aly
s
is
b
e
ca
u
s
e
th
ey
ca
n
r
e
p
r
esen
t
s
eq
u
en
tial
an
d
tem
p
o
r
al
d
ata.
L
STM
n
etwo
r
k
s
an
d
g
ated
r
ec
u
r
r
en
t
u
n
its
(
GR
Us
)
co
m
b
at
th
e
v
an
is
h
in
g
g
r
ad
ien
t
p
r
o
b
lem
an
d
im
p
r
o
v
e
lo
n
g
-
ter
m
r
elian
ce
.
T
h
ese
ar
ch
itectu
r
es
ar
e
g
o
o
d
at
lo
ad
,
r
en
ewa
b
le
en
er
g
y
,
an
d
p
o
wer
s
y
s
tem
d
ata
tim
e
-
s
er
ies
an
aly
s
is
.
C
o
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
s
(
C
NNs
)
p
r
im
ar
ily
h
a
n
d
le
im
a
g
es
an
d
s
p
atial
d
ata.
I
n
s
m
ar
t
p
o
wer
s
y
s
tem
s
,
C
NNs
ca
n
an
aly
ze
s
atellite
im
ag
er
y
,
s
en
s
o
r
d
ata,
an
d
g
eo
g
r
a
p
h
ic
in
f
o
r
m
atio
n
f
o
r
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:
2
5
0
2
-
4
7
52
I
mp
leme
n
ta
tio
n
o
f i
n
n
o
v
a
tive
d
ee
p
lea
r
n
in
g
tech
n
iq
u
es in
s
ma
r
t p
o
w
er sys
te
ms
(
Od
u
g
u
R
a
ma
Dev
i
)
725
r
en
ewa
b
le
en
e
r
g
y
p
r
o
jectio
n
s
,
f
au
lt
d
etec
tio
n
,
a
n
d
s
ec
u
r
ity
.
T
r
an
s
f
er
lear
n
in
g
u
tili
zin
g
p
r
e
-
tr
ain
ed
m
o
d
els
o
n
h
u
g
e
im
ag
e
d
atasets
to
lear
n
s
p
atial
in
f
o
r
m
atio
n
im
p
r
o
v
es
C
NN
p
er
f
o
r
m
an
ce
.
Gen
er
ativ
e
ad
v
er
s
ar
ial
n
etwo
r
k
s
(
GANs
)
p
it
a
g
en
er
ato
r
an
d
a
d
is
cr
im
in
ato
r
n
eu
r
al
n
etwo
r
k
ag
ain
s
t
o
n
e
o
t
h
er
in
a
g
am
e
-
th
eo
r
etic
en
v
ir
o
n
m
en
t.
GANs
ca
n
p
r
o
d
u
ce
r
ea
lis
tic
s
y
n
th
etic
d
ata
f
o
r
u
n
u
s
u
al
o
r
c
r
itical
s
ce
n
ar
io
s
.
GANs
s
im
u
late
p
o
wer
co
n
s
u
m
p
tio
n
f
o
r
lo
a
d
f
o
r
ec
asti
n
g
,
d
u
p
licate
r
en
ewa
b
l
e
en
er
g
y
p
atter
n
s
,
an
d
tr
ain
p
o
wer
s
y
s
tem
d
ef
ec
t
d
etec
tio
n
m
o
d
els.
A
R
L
ag
en
t
lear
n
s
o
p
tim
al
d
ec
is
io
n
s
th
r
o
u
g
h
tr
ial
an
d
er
r
o
r
with
its
s
u
r
r
o
u
n
d
in
g
s
.
I
n
s
m
ar
t
p
o
wer
s
y
s
tem
s
,
R
L
ag
en
ts
o
p
tim
ize
en
er
g
y
d
is
p
at
ch
,
s
to
r
ag
e,
an
d
d
em
a
n
d
r
esp
o
n
s
e
f
o
r
d
y
n
am
ic
en
e
r
g
y
co
n
tr
o
l.
R
L
alg
o
r
ith
m
s
lik
e
Q
-
lear
n
in
g
an
d
d
ee
p
Q
-
n
etwo
r
k
s
(
DQNs)
m
ak
e
r
ea
l
-
tim
e
au
t
o
n
o
m
o
u
s
d
ec
is
io
n
s
b
ased
o
n
e
n
v
ir
o
n
m
en
t a
n
d
s
y
s
tem
d
y
n
am
ics.
DR
L
lear
n
s
h
ig
h
-
d
im
en
s
io
n
al
s
tate
an
d
ac
tio
n
s
p
ac
es
th
r
o
u
g
h
d
ee
p
an
d
RL
.
Dee
p
d
et
er
m
in
is
tic
p
o
licy
g
r
ad
ie
n
t
(
DDPG)
an
d
p
r
o
x
im
al
p
o
licy
o
p
tim
izat
io
n
(
PPO
)
d
ee
p
r
ein
f
o
r
c
em
e
n
t
lear
n
in
g
(
DR
L
)
alg
o
r
ith
m
s
ca
n
o
p
tim
ize
c
o
m
p
lex
s
m
ar
t
p
o
wer
s
y
s
tem
en
er
g
y
m
an
ag
e
m
en
t
ch
allen
g
es.
DR
L
ca
n
lear
n
r
eso
u
r
ce
-
ef
f
icien
t
d
em
an
d
r
es
p
o
n
s
e,
en
er
g
y
s
to
r
ag
e
co
n
tr
o
l,
an
d
d
is
tr
ib
u
ted
en
er
g
y
r
eso
u
r
ce
m
an
a
g
em
en
t
r
u
les.
Un
d
er
s
tan
d
a
b
le
Po
wer
Sy
s
tem
AI
:
Po
wer
s
y
s
tem
d
ee
p
lear
n
in
g
m
o
d
els
m
u
s
t
b
e
in
ter
p
r
etab
le
an
d
ex
p
lain
ab
le.
Dee
p
lear
n
i
n
g
m
o
d
el
d
ec
is
io
n
-
m
ak
i
n
g
ca
n
b
e
s
h
o
wn
b
y
atten
tio
n
p
r
o
ce
s
s
es,
s
alien
cy
m
ap
s
,
an
d
m
o
d
el
-
ag
n
o
s
tic
m
eth
o
d
s
lik
e
L
I
ME
.
I
n
te
r
p
r
etab
le
d
ee
p
lear
n
in
g
m
o
d
els
b
o
o
s
t
s
m
ar
t
p
o
wer
s
y
s
tem
tr
an
s
p
ar
en
cy
,
tr
u
s
t,
an
d
co
m
p
l
ian
ce
.
Fig
u
r
e
1
.
A
n
o
v
el
m
et
h
o
d
f
o
r
p
r
ed
ictin
g
p
o
wer
lo
a
d
ac
cu
r
at
ely
-
R
ec
en
t
ad
v
an
ce
m
en
ts
in
d
ee
p
lear
n
in
g
f
o
r
s
m
ar
t p
o
wer
s
y
s
tem
s
I
n
r
ec
en
t
y
ea
r
s
,
s
ig
n
if
ican
t
ad
v
an
ce
m
e
n
ts
h
av
e
b
ee
n
m
a
d
e
in
th
e
ap
p
licatio
n
o
f
d
ee
p
lear
n
in
g
tech
n
iq
u
es
f
o
r
s
m
ar
t
p
o
wer
s
y
s
tem
s
[
1
6
]
,
[
1
7
]
.
T
h
ese
ad
v
an
ce
m
en
ts
h
av
e
co
n
t
r
ib
u
ted
to
im
p
r
o
v
ed
p
er
f
o
r
m
an
ce
,
s
ca
lab
ilit
y
,
in
ter
p
r
etab
ilit
y
,
an
d
r
o
b
u
s
tn
ess
o
f
d
ee
p
lear
n
in
g
m
o
d
els.
So
m
e
o
f
th
e
n
o
ta
b
le
r
ec
en
t
ad
v
an
ce
m
e
n
ts
ar
e
d
is
cu
s
s
ed
b
elo
w:
I
n
p
o
wer
s
y
s
tem
s
,
d
ee
p
lear
n
in
g
an
d
I
o
T
h
av
e
en
ab
led
r
ea
l
-
tim
e
d
ata
c
o
llectio
n
f
r
o
m
a
wid
e
r
an
g
e
o
f
s
en
s
o
r
s
an
d
eq
u
ip
m
en
t.
I
o
T
d
ev
ices
lik
e
s
m
ar
t
m
eter
s
,
p
h
aso
r
m
ea
s
u
r
em
en
t
u
n
its
,
an
d
d
is
tr
ib
u
ted
s
en
s
o
r
s
cr
ea
te
m
ass
iv
e
d
ata
s
ets
th
at
d
ee
p
lear
n
i
n
g
m
o
d
els
ca
n
u
s
e.
Mo
r
e
ac
c
u
r
ate
lo
a
d
f
o
r
ec
asti
n
g
,
f
a
u
lt
d
etec
tio
n
,
an
d
r
ea
l
-
tim
e
p
o
wer
s
y
s
tem
p
ar
am
eter
m
o
n
ito
r
in
g
ar
e
p
o
s
s
ib
le
with
I
o
T
an
d
d
ee
p
lear
n
i
n
g
[
1
8
]
.
Fed
er
ated
lear
n
in
g
lets
s
ev
er
al
d
ev
ices
o
r
en
titi
es
tr
ain
a
d
ee
p
lear
n
i
n
g
m
o
d
el
with
o
u
t
s
h
ar
in
g
d
at
a.
I
n
p
o
we
r
s
y
s
tem
s
with
s
en
s
itiv
e
cu
s
to
m
er
d
ata,
th
is
s
tr
ateg
y
is
u
s
ef
u
l
f
o
r
d
ata
p
r
iv
ac
y
an
d
s
ec
u
r
ity
.
Fed
er
ated
lear
n
in
g
tr
ain
s
s
tr
o
n
g
d
ee
p
lear
n
in
g
m
o
d
els
wh
ile
p
r
o
tectin
g
d
ata
an
d
d
ec
r
ea
s
in
g
co
m
m
u
n
icatio
n
.
Hy
b
r
i
d
m
o
d
els h
ar
n
ess
th
e
s
tr
en
g
th
s
o
f
d
ee
p
lear
n
in
g
a
n
d
a
d
d
itio
n
al
o
p
tim
izatio
n
o
r
f
o
r
ec
asti
n
g
m
et
h
o
d
s
.
L
o
a
d
f
o
r
ec
asti
n
g
h
y
b
r
id
m
eth
o
d
s
co
m
b
i
n
e
d
ee
p
lea
r
n
in
g
with
s
tatis
tical
m
o
d
els
lik
e
au
to
r
eg
r
ess
iv
e
in
teg
r
ated
m
o
v
in
g
av
er
a
g
e
(
AR
I
MA
)
.
T
h
ese
m
o
d
els en
ca
p
s
u
late
d
ee
p
lear
n
in
g
alg
o
r
ith
m
s
'
co
m
p
licated
tem
p
o
r
al
p
atter
n
s
an
d
tim
e
s
er
ies
d
ata'
s
s
ta
tis
tica
l
f
ea
tu
r
es,
im
p
r
o
v
in
g
p
r
e
d
ictin
g
ac
c
u
r
ac
y
.
Dee
p
lear
n
in
g
in
s
m
ar
t
p
o
we
r
s
y
s
tem
s
is
b
ein
g
im
p
r
o
v
e
d
with
th
ese
ad
v
an
ce
s
.
R
esear
ch
er
s
an
d
p
r
ac
titi
o
n
er
s
ar
e
p
u
s
h
in
g
d
ee
p
lear
n
i
n
g
to
ad
d
r
ess
p
o
wer
s
y
s
tem
d
if
f
icu
lties
an
d
r
e
q
u
i
r
em
en
ts
b
y
i
n
teg
r
atin
g
I
o
T
,
f
ed
er
ated
lear
n
in
g
,
ed
g
e
co
m
p
u
tin
g
,
an
d
h
y
b
r
id
m
o
d
els,
r
esu
ltin
g
in
m
o
r
e
e
f
f
i
cien
t,
r
eliab
le,
an
d
s
ec
u
r
e
en
e
r
g
y
m
an
a
g
em
en
t.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
8
,
No
.
2
,
May
20
2
5
:
7
2
3
-
7
3
1
726
R
ec
u
r
r
en
t
an
d
c
o
n
v
o
l
u
tio
n
al
n
eu
r
al
n
etwo
r
k
s
im
p
r
o
v
e
p
o
wer
s
y
s
tem
f
o
r
ec
asts
.
C
o
m
p
l
ex
tem
p
o
r
al
an
d
s
p
atial
tr
e
n
d
s
ca
n
b
e
ca
p
tu
r
ed
b
y
th
ese
m
o
d
els,
b
o
o
s
ti
n
g
lo
a
d
,
r
en
ewa
b
le
en
er
g
y
,
a
n
d
f
au
lt
p
r
ed
ictio
n
[
1
9
]
,
[
2
0
]
.
Fo
r
ec
asti
n
g
ac
cu
r
a
cy
en
h
an
ce
s
r
eso
u
r
ce
allo
ca
tio
n
,
en
er
g
y
m
an
a
g
em
en
t,
a
n
d
g
r
id
ef
f
icien
cy
.
Dee
p
lear
n
in
g
m
o
d
els
ca
n
o
p
tim
ize
en
er
g
y
m
an
a
g
em
en
t
u
s
in
g
h
i
s
to
r
ical
co
n
s
u
m
p
tio
n
,
wea
th
er
,
an
d
p
r
icin
g
d
a
ta.
T
h
ey
ca
n
ef
f
icien
tly
d
is
p
atch
en
er
g
y
,
o
p
tim
ize
r
eso
u
r
ce
s
ch
ed
u
lin
g
,
an
d
r
esp
o
n
d
to
en
e
r
g
y
d
em
a
n
d
in
r
ea
l
tim
e.
E
n
er
g
y
e
f
f
icien
cy
,
waste
r
ed
u
ctio
n
,
a
n
d
cu
s
to
m
er
s
av
in
g
s
ar
e
im
p
r
o
v
ed
b
y
d
ee
p
l
ea
r
n
in
g
[
2
1
]
.
Dee
p
lear
n
in
g
s
y
s
tem
s
ca
n
d
etec
t
p
o
wer
s
y
s
tem
b
r
ea
k
d
o
wn
s
u
s
in
g
s
en
s
o
r
d
ata
a
n
d
h
is
to
r
ic
al
p
atter
n
s
.
Def
ec
t
d
etec
tio
n
s
av
es
d
o
wn
tim
e,
im
p
r
o
v
es
p
o
wer
s
y
s
tem
r
eliab
ilit
y
,
an
d
b
o
o
s
ts
s
af
ety
[
2
2
]
.
De
ep
lear
n
in
g
m
o
d
els
ca
n
id
en
tify
d
ef
ec
t
s
o
u
r
ce
s
f
o
r
tar
g
eted
m
ain
ten
an
ce
.
Dee
p
l
ea
r
n
in
g
o
p
tim
izes
r
en
ewa
b
le
e
n
e
r
g
y
,
s
to
r
ag
e,
an
d
d
em
an
d
r
esp
o
n
s
e.
Usi
n
g
h
is
to
r
ical
d
ata,
wea
th
er
,
an
d
s
y
s
tem
lim
its
,
d
ee
p
lear
n
in
g
m
o
d
el
s
o
p
tim
ize
r
eso
u
r
ce
co
n
s
u
m
p
tio
n
,
en
er
g
y
im
b
alan
ce
s
,
an
d
g
r
id
s
tab
ilit
y
.
Op
tim
i
zin
g
r
eso
u
r
ce
allo
ca
tio
n
b
o
o
s
ts
s
y
s
tem
r
eliab
ilit
y
,
en
er
g
y
p
r
icin
g
,
an
d
r
en
ewa
b
le
en
er
g
y
[
2
3
]
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
Dee
p
lear
n
in
g
tech
n
iq
u
es
h
av
e
b
ee
n
s
u
cc
ess
f
u
lly
ap
p
lied
in
v
ar
io
u
s
p
r
ac
tical
im
p
lem
en
t
atio
n
s
an
d
ca
s
e
s
tu
d
ies
in
s
m
ar
t
p
o
wer
s
y
s
tem
s
.
T
h
ese
r
ea
l
-
wo
r
ld
ap
p
licatio
n
s
d
em
o
n
s
tr
ate
th
e
ef
f
ec
tiv
en
ess
an
d
b
en
ef
its
o
f
d
ee
p
lear
n
in
g
in
ad
d
r
ess
in
g
s
p
ec
if
ic
ch
allen
g
es
a
n
d
im
p
r
o
v
in
g
s
y
s
tem
p
er
f
o
r
m
an
ce
.
So
m
e
n
o
tab
le
ca
s
e
s
tu
d
ies an
d
p
r
ac
tical
im
p
l
em
en
tatio
n
s
ar
e
o
u
tlin
ed
b
elo
w:
T
h
e
au
th
o
r
s
o
f
f
er
a
d
ee
p
lea
r
n
in
g
s
tr
ateg
y
th
at
ac
c
o
u
n
ts
f
o
r
p
r
ed
ictio
n
u
n
ce
r
tain
t
y
an
d
lev
er
ag
e
s
ad
v
an
ce
d
alg
o
r
ith
m
s
.
T
im
e
-
s
er
ies
p
o
wer
p
lan
t
e
n
er
g
y
lo
a
d
d
ata
was
u
s
ed
t
o
e
v
alu
ate
th
e
m
eth
o
d
[
2
4
]
.
T
h
e
p
o
wer
s
y
s
tem
will
co
n
tin
u
ally
r
ec
o
r
d
p
o
wer
lo
ad
d
ata
in
2
0
2
1
,
b
u
t
Fig
u
r
e
2
o
n
ly
s
h
o
ws
two
d
ay
s
d
u
e
to
d
at
a
v
o
lu
m
e.
T
h
e
d
ata
cu
r
v
e
m
ay
ap
p
ea
r
to
f
o
llo
w
r
u
les
o
r
p
a
tter
n
s
,
b
u
t
a
clo
s
er
in
s
p
ec
tio
n
in
d
icate
s
th
at
th
e
p
o
wer
g
r
id
'
s
p
o
wer
lo
ad
h
as
ch
an
g
ed
,
s
h
o
win
g
u
n
ce
r
tain
ty
.
T
h
e
a
u
th
o
r
s
also
ca
r
ef
u
lly
in
v
esti
g
ated
ea
r
lier
d
ata
to
p
r
e
d
ict
th
eir
b
e
h
av
io
r
an
d
s
tu
d
y
p
o
wer
l
o
ad
ch
a
n
g
es
,
p
ar
ticu
lar
ly
t
h
e
im
p
ac
t
o
f
d
if
f
er
en
t
s
ea
s
o
n
s
an
d
tim
e
p
er
io
d
s
,
to
i
n
s
p
ir
e
h
is
to
r
i
ca
l
d
ata
an
al
y
s
is
.
Po
wer
lo
ad
cu
r
v
es
h
a
v
e
a
g
en
e
r
ally
s
tab
le
d
is
tr
ib
u
tio
n
ac
r
o
s
s
all
tim
e
p
o
in
ts
o
n
wee
k
en
d
s
(
Satu
r
d
ay
an
d
Su
n
d
ay
)
,
b
u
t
n
o
t
th
r
o
u
g
h
o
u
t
th
e
wee
k
(
Mo
n
d
ay
th
r
o
u
g
h
Frid
ay
)
d
u
e
to
p
o
wer
d
em
a
n
d
'
s
co
m
p
lex
ity
an
d
f
lu
ctu
atio
n
,
m
a
k
in
g
wee
k
d
ay
p
r
o
jectio
n
s
h
ar
d
er
.
Su
m
m
er
d
ata
v
ar
iatio
n
s
n
eg
ativ
ely
im
p
ac
t
elec
tr
icity
d
em
an
d
p
r
ed
ictio
n
s
m
o
r
e
th
an
o
th
er
s
ea
s
o
n
s
,
i
n
d
icatin
g
in
c
r
ea
s
ed
u
n
ce
r
tain
ty
.
Fig
u
r
e
3
d
is
p
lay
s
th
e
v
ar
ia
n
ce
co
m
p
u
tatio
n
o
f
w
o
r
k
d
a
y
,
we
ek
e
n
d
,
an
d
s
u
m
m
e
r
2
0
2
1
[
2
5
]
d
ata
to
s
tatis
t
ically
s
tu
d
y
p
o
wer
g
r
id
p
o
wer
cu
r
v
e
v
a
r
iatio
n
.
Fro
m
th
e
ca
lc
u
latio
n
r
esu
lts
in
T
ab
le
1
,
it
ca
n
b
e
s
ee
n
th
at
th
e
p
o
we
r
f
lu
ctu
ati
o
n
d
eg
r
ee
o
f
t
h
e
h
is
to
r
ical
d
ata
is
f
o
r
wee
k
e
n
d
s
,
wee
k
d
ay
s
,
s
u
m
m
e
r
,
a
n
d
o
t
h
er
tim
e
p
er
io
d
s
.
I
t
is
f
o
r
eseea
b
le
th
at
d
u
e
to
th
e
u
n
ce
r
tain
ty
b
r
o
u
g
h
t a
b
o
u
t b
y
f
lu
ctu
atio
n
s
,
th
e
d
if
f
icu
lty
o
f
f
o
r
ec
asti
n
g
is
g
r
ea
test
o
n
wee
k
en
d
s
,
wo
r
k
in
g
d
ay
s
,
s
u
m
m
er
,
an
d
o
th
er
tim
e
p
er
io
d
s
.
Ho
wev
er
,
if
th
e
u
n
ce
r
t
ain
ty
o
f
p
o
wer
lo
ad
f
lu
ct
u
at
io
n
s
ca
n
b
e
well
co
n
tr
o
lled
,
th
e
p
r
ed
ictio
n
ac
c
u
r
ac
y
i
n
d
if
f
er
en
t
tim
e
p
er
i
o
d
s
ca
n
b
e
r
ed
u
c
ed
a
n
d
s
tab
le
a
n
d
ac
cu
r
ate
p
o
wer
lo
ad
p
r
ed
ictio
n
r
esu
lts
ca
n
b
e
o
u
tp
u
t.
I
n
o
r
d
er
to
v
e
r
if
y
th
is
ac
ad
em
ic
p
o
in
t
o
f
v
iew,
th
e
h
is
to
r
ical
d
ata
wer
e
p
r
ed
icted
f
o
r
d
if
f
er
e
n
t
tim
e
p
er
io
d
s
.
I
n
o
r
d
er
to
test
th
e
p
r
ed
ictio
n
ac
cu
r
ac
y
,
two
m
et
r
ics,
th
e
r
o
o
t
m
ea
n
s
q
u
ar
e
er
r
o
r
(
R
MSE
)
an
d
t
h
e
m
ea
n
ab
s
o
lu
te
p
e
r
ce
n
tag
e
e
r
r
o
r
(
MA
PE)
,
wer
e
u
s
ed
.
Fig
u
r
e
2
.
Po
wer
c
o
n
s
u
m
p
tio
n
r
ec
o
r
d
s
f
o
r
th
e
p
r
ec
ed
in
g
two
d
ay
s
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:
2
5
0
2
-
4
7
52
I
mp
leme
n
ta
tio
n
o
f i
n
n
o
v
a
tive
d
ee
p
lea
r
n
in
g
tech
n
iq
u
es in
s
ma
r
t p
o
w
er sys
te
ms
(
Od
u
g
u
R
a
ma
Dev
i
)
727
T
ab
le
1
.
Po
wer
c
o
n
s
u
m
p
tio
n
f
lu
ctu
atio
n
s
P
o
r
t
i
o
n
o
f
T
i
me
V
a
r
i
a
b
i
l
i
t
y
i
n
u
n
c
e
r
t
a
i
n
t
y
/
(
M
V
h
)
P
e
r
i
o
d
o
f
L
a
b
o
u
r
3
5
5
4
.
3
W
e
e
k
e
n
d
s
2
7
1
9
.
1
S
u
mm
e
r
Ti
m
e
4
6
5
2
.
7
Fig
u
r
e
3
s
h
o
ws
ca
lcu
lated
p
o
wer
f
lu
ctu
atio
n
f
o
r
wee
k
e
n
d
s
,
wee
k
d
ay
s
,
s
u
m
m
er
,
an
d
o
th
e
r
s
ea
s
o
n
s
.
Swin
g
s
'
u
n
p
r
ed
ictab
ilit
y
m
ak
es
f
o
r
ec
asti
n
g
d
if
f
icu
lt
o
n
w
ee
k
en
d
s
,
wo
r
k
d
ay
s
,
s
u
m
m
er
,
an
d
o
th
er
tim
es
.
Ho
wev
er
,
co
n
tr
o
llin
g
p
o
wer
l
o
ad
v
o
latilit
y
im
p
r
o
v
es
p
o
wer
lo
ad
esti
m
ates
ac
r
o
s
s
tim
e
p
er
io
d
s
an
d
p
r
o
d
u
ce
s
tr
u
s
two
r
th
y
an
d
ac
c
u
r
ate
r
esu
lts
.
Pre
d
ictio
n
s
o
f
h
is
to
r
ical
d
ata
f
o
r
s
ev
er
al
ep
o
ch
s
s
u
p
p
o
r
ted
th
is
s
ch
o
lar
ly
v
iew.
Pre
d
ictio
n
m
o
d
el
d
ep
e
n
d
ab
ilit
y
is
m
ea
s
u
r
ed
b
y
R
MSE
an
d
m
ea
n
ab
s
o
lu
te
d
e
v
iatio
n
(
MA
D
)
.
C
o
m
m
o
n
o
n
es
lik
e
L
STM
,
SVM,
an
d
r
an
d
o
m
f
o
r
est
ar
e
co
m
p
ar
e
d
.
T
h
e
p
r
o
jecte
d
d
a
ily
p
o
wer
co
n
s
u
m
p
tio
n
d
u
r
i
n
g
b
u
s
in
ess
h
o
u
r
s
is
s
h
o
wn
in
Fig
u
r
e
4
.
An
aly
s
is
in
clu
d
ed
all
g
r
id
p
o
we
r
lo
ad
d
ata
f
r
o
m
1
0
0
wee
k
d
ay
s
(
ex
clu
d
in
g
wee
k
en
d
s
)
.
R
an
d
o
m
f
o
r
est
an
d
L
ST
M
o
u
tp
er
f
o
r
m
SVM
b
u
t
n
o
t
th
e
au
th
o
r
s
'
m
eth
o
d
in
p
r
ed
ictio
n
o
u
tco
m
es.
Du
e
t
o
th
e
au
th
o
r
'
s
m
o
d
el
u
n
ce
r
t
ain
ty
an
aly
s
is
,
th
e
n
etwo
r
k
ca
n
au
to
m
atica
lly
co
m
p
en
s
ate
f
o
r
u
n
p
r
e
d
ictab
le
p
o
wer
v
ar
iatio
n
s
.
Fig
u
r
e
5
d
is
p
lay
s
th
e
wee
k
en
d
p
o
wer
lo
a
d
f
o
r
ec
ast.
An
aly
s
is
in
clu
d
ed
all
g
r
id
p
o
w
er
lo
a
d
d
ata
f
r
o
m
1
0
0
co
n
s
ec
u
tiv
e
wee
k
en
d
s
(
ex
clu
d
in
g
wee
k
d
ay
s
)
.
T
h
e
r
an
d
o
m
f
o
r
est
an
d
L
STM
alg
o
r
ith
m
s
f
o
r
ec
ast
s
im
ilar
ly
with
R
MSE
s
o
f
1
7
.
3
a
n
d
1
7
.
1
,
r
esp
ec
tiv
ely
,
wh
ile
th
e
SVM
p
r
ed
ictio
n
h
as a
b
ig
g
e
r
R
MSE
er
r
o
r
o
f
2
7
.
5
.
T
h
e
a
u
th
o
r
s
'
tech
n
iq
u
e
p
r
ed
icts
b
est with
1
4
.
8
.
Fig
u
r
e
3
.
Po
wer
c
o
n
s
u
m
p
tio
n
f
lu
ctu
atio
n
s
Fig
u
r
e
4
.
Pre
d
ictio
n
s
o
f
th
e
d
a
y
tim
e
p
o
wer
d
em
an
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
8
,
No
.
2
,
May
20
2
5
:
7
2
3
-
7
3
1
728
T
ab
le
2
s
h
o
ws
th
e
p
r
ed
icted
r
esu
lts
o
f
p
o
we
r
lo
ad
o
n
wo
r
k
in
g
d
ay
s
.
I
n
th
e
an
aly
s
is
,
th
e
co
m
p
lete
p
o
wer
l
o
ad
d
ata
o
f
th
e
g
r
id
f
o
r
1
0
0
co
n
s
ec
u
tiv
e
wo
r
k
in
g
d
a
y
s
(
ex
clu
d
in
g
wee
k
en
d
s
)
wer
e
s
elec
ted
.
Fro
m
th
e
p
r
ed
ictio
n
r
esu
lts
,
th
e
p
r
e
d
ictio
n
r
esu
lts
o
f
r
an
d
o
m
f
o
r
est
a
n
d
th
e
L
STM
alg
o
r
ith
m
a
r
e
r
elativ
ely
clo
s
e,
m
o
r
e
ac
cu
r
ate
th
an
th
e
SVM
b
u
t
n
o
t
as
g
o
o
d
as
th
e
au
th
o
r
s
’
m
eth
o
d
.
T
h
is
is
b
ec
au
s
e
th
e
au
th
o
r
’
s
m
eth
o
d
an
aly
s
es
th
e
u
n
ce
r
tain
ty
o
f
th
e
m
o
d
el,
an
d
th
e
n
etwo
r
k
will
ad
ap
tiv
ely
co
m
p
en
s
ate
f
o
r
th
e
e
f
f
ec
t
s
o
f
r
an
d
o
m
p
o
wer
f
lu
ctu
atio
n
s
.
T
ab
le
3
s
h
o
ws
th
e
f
o
r
ec
ast
r
es
u
lts
o
f
th
e
p
o
wer
lo
ad
f
o
r
th
e
wee
k
en
d
.
T
h
e
co
m
p
lete
p
o
we
r
lo
ad
d
ata
o
f
th
e
g
r
id
f
o
r
1
0
0
co
n
s
ec
u
tiv
e
wee
k
en
d
s
(
ex
clu
d
i
n
g
wee
k
d
ay
s
)
wer
e
s
elec
ted
f
o
r
th
e
an
aly
s
is
.
Fro
m
th
e
p
r
ed
icted
r
esu
lts
,
th
e
p
r
ed
icti
o
n
r
esu
lts
o
f
r
an
d
o
m
f
o
r
est
an
d
L
STM
alg
o
r
ith
m
s
ar
e
r
e
lativ
ely
clo
s
e,
with
R
MSE
o
f
1
7
.
3
an
d
1
7
.
1
,
r
es
p
ec
tiv
ely
,
wh
ile
th
e
SVM
p
r
ed
ictio
n
h
as
a
lar
g
er
R
MSE
er
r
o
r
o
f
2
7
.
5
;
th
e
au
th
o
r
s
’
m
eth
o
d
p
r
ed
icts
th
e
b
est with
1
4
.
8
.
T
ab
le
4
s
p
ec
if
ically
an
aly
s
es
t
h
e
p
o
we
r
lo
a
d
f
o
r
ec
ast
r
esu
lts
f
o
r
th
e
t
h
r
ee
m
o
n
th
s
o
f
s
u
m
m
er
.
Du
e
to
th
e
lar
g
e
f
lu
ctu
atio
n
s
in
elec
tr
i
city
co
n
s
u
m
p
tio
n
in
th
e
s
u
m
m
er
,
th
e
r
esu
ltin
g
u
n
ce
r
tain
ty
also
in
cr
ea
s
es.
Fro
m
th
e
p
r
ed
ictio
n
r
esu
lts
,
th
e
R
MSE
p
r
ed
icted
b
y
th
e
r
an
d
o
m
f
o
r
est
an
d
L
STM
alg
o
r
ith
m
s
is
2
7
.
8
an
d
2
7
.
5
,
r
esp
ec
tiv
ely
,
an
d
th
e
SVM
p
r
ed
ictio
n
R
MSE
er
r
o
r
is
3
5
.
1
.
T
h
e
p
r
ed
ictio
n
ef
f
ec
t
R
MSE
o
f
th
e
au
th
o
r
s
’
m
et
h
o
d
is
1
8
.
3
.
I
t
ca
n
b
e
s
ee
n
th
at
th
e
p
o
wer
f
lu
ctu
atio
n
h
as
a
g
r
ea
t
in
f
lu
e
n
ce
o
n
th
e
p
r
e
d
ictio
n
ac
cu
r
ac
y
,
b
u
t
th
e
au
th
o
r
s
’
m
et
h
o
d
ca
n
s
till
ac
cu
r
ately
p
r
e
d
ict
th
e
p
o
wer
l
o
ad
.
I
t
ca
n
b
e
s
ee
n
th
at
th
e
a
u
th
o
r
s
’
m
eth
o
d
is
a
r
ea
s
o
n
ab
le
an
d
ef
f
ec
tiv
e
p
o
we
r
lo
ad
f
o
r
ec
asti
n
g
m
eth
o
d
.
T
ab
le
2
.
Pre
d
ictio
n
s
o
f
th
e
d
ay
tim
e
p
o
wer
d
em
a
n
d
P
r
e
d
i
c
t
i
v
e
T
e
c
h
n
i
q
u
e
P
r
e
c
i
s
i
o
n
i
n
f
o
r
e
c
a
s
t
i
n
g
R
M
S
E
M
A
P
E
S
V
M
2
6
.
1
0
.
0
2
5
R
a
n
d
o
m f
o
r
e
s
t
1
9
.
5
0
.
0
2
3
LSTM
1
9
.
3
0
.
0
2
3
P
r
o
p
o
se
d
T
e
c
h
n
i
q
u
e
1
7
.
2
0
.
0
2
1
T
ab
le
3
.
Pre
d
ictio
n
s
r
eg
ar
d
i
n
g
th
is
wee
k
en
d
'
s
p
o
wer
co
n
s
u
m
p
tio
n
P
r
e
d
i
c
t
i
v
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T
e
c
h
n
i
q
u
e
P
r
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c
i
s
i
o
n
i
n
f
o
r
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c
a
s
t
i
n
g
R
M
S
E
M
A
P
E
S
V
M
2
7
.
5
0
.
0
2
6
R
a
n
d
o
m f
o
r
e
s
t
1
7
.
3
0
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0
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2
LSTM
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7
.
1
0
.
0
2
2
P
r
o
p
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se
d
T
e
c
h
n
i
q
u
e
1
4
.
8
0
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0
2
0
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ab
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4
.
Pre
d
icted
p
o
wer
c
o
n
s
u
m
p
tio
n
d
u
r
in
g
th
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M
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S
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M
3
5
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1
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0
3
3
R
a
n
d
o
m f
o
r
e
s
t
2
7
.
8
0
.
0
2
7
LSTM
2
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5
0
.
0
2
6
M
e
t
h
o
d
1
8
.
3
0
.
0
2
2
Fig
u
r
e
5
.
Pre
d
ictio
n
s
r
eg
ar
d
in
g
th
is
wee
k
en
d
'
s
p
o
wer
co
n
s
u
m
p
tio
n
Evaluation Warning : The document was created with Spire.PDF for Python.
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p
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I
mp
leme
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ta
tio
n
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f i
n
n
o
v
a
tive
d
ee
p
lea
r
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in
g
tech
n
iq
u
es in
s
ma
r
t p
o
w
er sys
te
ms
(
Od
u
g
u
R
a
ma
Dev
i
)
729
T
h
e
au
th
o
r
s
u
s
e
th
e
d
ee
p
f
o
r
est
'
s
s
ca
lab
ilit
y
to
s
am
p
le
s
iz
e
b
y
ch
an
g
in
g
f
o
r
est
s
ettin
g
s
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Fig
u
r
e
6
d
etails
s
u
m
m
er
tim
e
p
o
wer
lo
ad
f
o
r
ec
asts
.
Su
m
m
er
'
s
h
ig
h
elec
tr
icity
u
s
ag
e
v
ar
iab
ilit
y
in
cr
ea
s
es
unp
r
e
d
ictab
ilit
y
.
T
h
e
SVM
alg
o
r
ith
m
h
as
3
5
.
1
R
MSE
er
r
o
r
,
wh
ile
th
e
r
an
d
o
m
f
o
r
est
a
n
d
L
STM
m
eth
o
d
s
h
av
e
2
7
.
8
an
d
2
7
.
5
.
T
h
e
a
u
th
o
r
s
'
p
r
ed
ictio
n
ap
p
r
o
ac
h
h
as
1
8
.
3
R
MSE
.
Po
wer
f
lu
ctu
atio
n
s
im
p
ac
t
f
o
r
ec
ast
ac
cu
r
ac
y
,
alth
o
u
g
h
th
e
a
u
th
o
r
s
'
tech
n
iq
u
e
f
o
r
ec
asts
p
o
wer
u
s
e
ac
cu
r
ately
.
Po
wer
lo
a
d
s
ar
e
p
r
ed
icted
l
o
g
ically
an
d
ef
f
ec
tiv
ely
b
y
th
e
au
th
o
r
s
.
C
ase
s
tu
d
ies
an
d
r
ea
l
im
p
l
em
en
tatio
n
s
d
em
o
n
s
tr
ate
h
o
w
d
ee
p
lear
n
in
g
m
ay
im
p
r
o
v
e
s
m
ar
t
p
o
wer
s
y
s
tem
s
.
As
d
ee
p
lear
n
in
g
r
esear
ch
an
d
d
ev
el
o
p
m
en
t
c
o
n
tin
u
e,
s
m
ar
t
p
o
wer
s
y
s
tem
s
will b
en
ef
it f
r
o
m
i
n
n
o
v
ativ
e
a
n
d
ef
f
ec
tiv
e
s
o
lu
tio
n
s
.
Fig
u
r
e
6
.
Pre
d
icte
d
p
o
wer
co
n
s
u
m
p
tio
n
d
u
r
in
g
t
h
e
n
e
x
t th
r
ee
m
o
n
th
s
4.
CO
NCLU
SI
O
N
Dee
p
lear
n
in
g
tec
h
n
iq
u
es
h
av
e
em
er
g
e
d
as
p
o
wer
f
u
l
t
o
o
ls
f
o
r
a
d
d
r
ess
in
g
c
h
allen
g
es
a
n
d
en
h
an
cin
g
v
ar
io
u
s
asp
ec
ts
o
f
s
m
ar
t
p
o
w
er
s
y
s
tem
s
.
T
h
e
in
teg
r
atio
n
o
f
d
ee
p
lea
r
n
in
g
with
s
m
ar
t
p
o
wer
s
y
s
tem
s
h
as
en
ab
led
im
p
r
o
v
e
d
f
o
r
ec
asti
n
g
ac
cu
r
ac
y
,
e
n
h
an
ce
d
en
e
r
g
y
ef
f
icien
cy
,
f
au
lt
d
etec
tio
n
an
d
d
iag
n
o
s
is
,
cy
b
er
s
ec
u
r
ity
,
o
p
tim
al
r
eso
u
r
c
e
allo
ca
tio
n
,
a
n
d
p
r
ed
ictiv
e
m
ain
t
en
an
ce
.
T
h
ese
ad
v
a
n
ce
m
e
n
ts
h
av
e
p
av
ed
th
e
way
f
o
r
m
o
r
e
ef
f
icien
t,
r
eliab
le,
an
d
s
u
s
tain
ab
le
en
er
g
y
m
an
ag
em
en
t.
T
h
e
en
h
an
ce
d
ap
p
licatio
n
o
f
d
ee
p
lear
n
in
g
tech
n
iq
u
es
in
s
m
ar
t
p
o
wer
s
y
s
tem
s
h
o
ld
s
tr
e
m
en
d
o
u
s
p
o
ten
tial
f
o
r
tr
an
s
f
o
r
m
in
g
th
e
en
er
g
y
lan
d
s
ca
p
e.
T
h
i
s
r
esear
ch
a
r
ticle
aim
s
to
p
r
o
v
i
d
e
a
co
m
p
r
eh
e
n
s
iv
e
u
n
d
er
s
tan
d
in
g
o
f
th
e
en
h
an
ce
d
a
p
p
licatio
n
o
f
d
ee
p
lear
n
in
g
tech
n
iq
u
es
in
s
m
ar
t
p
o
wer
s
y
s
tem
s
.
I
t h
ig
h
l
ig
h
ts
th
e
p
o
te
n
tial
b
en
ef
its
,
r
e
ce
n
t
ad
v
an
ce
m
en
ts
,
an
d
ch
allen
g
es
ass
o
ciate
d
with
in
teg
r
atin
g
d
ee
p
lear
n
i
n
g
in
to
p
o
wer
s
y
s
tem
s
.
T
h
e
in
clu
s
io
n
o
f
ca
s
e
s
tu
d
ies
an
d
p
r
ac
tical
im
p
lem
en
tatio
n
s
d
em
o
n
s
tr
ates
th
e
p
r
ac
ticality
an
d
ef
f
ec
tiv
en
ess
o
f
d
ee
p
lear
n
in
g
in
ad
d
r
ess
in
g
v
ar
io
u
s
p
o
wer
s
y
s
tem
ch
allen
g
es.
T
h
is
ar
ticle
en
co
u
r
ag
es
f
u
r
th
er
r
esear
ch
a
n
d
in
n
o
v
atio
n
in
th
is
d
o
m
ain
t
o
r
ea
lize
th
e
f
u
ll p
o
ten
tial o
f
s
m
ar
t p
o
wer
s
y
s
tem
s
in
th
e
f
u
tu
r
e.
RE
F
E
R
E
NC
E
S
[
1
]
W
.
G
.
H
a
t
c
h
e
r
a
n
d
W
.
Y
u
,
“
A
s
u
r
v
e
y
o
f
d
e
e
p
l
e
a
r
n
i
n
g
:
p
l
a
t
f
o
r
ms,
a
p
p
l
i
c
a
t
i
o
n
s
a
n
d
e
m
e
r
g
i
n
g
r
e
s
e
a
r
c
h
t
r
e
n
d
s,
”
I
EEE
Ac
c
e
ss
,
v
o
l
.
6
,
p
p
.
2
4
4
1
1
–
2
4
4
3
2
,
2
0
1
8
,
d
o
i
:
1
0
.
1
1
0
9
/
A
C
C
ESS
.
2
0
1
8
.
2
8
3
0
6
6
1
.
[
2
]
A
.
K
u
m
b
h
a
r
,
P
.
G
.
D
h
a
w
a
l
e
,
S
.
K
u
mb
h
a
r
,
U
.
P
a
t
i
l
,
a
n
d
P
.
M
a
g
d
u
m
,
“
A
c
o
mp
r
e
h
e
n
s
i
v
e
r
e
v
i
e
w
:
M
a
c
h
i
n
e
l
e
a
r
n
i
n
g
a
n
d
i
t
s
a
p
p
l
i
c
a
t
i
o
n
i
n
i
n
t
e
g
r
a
t
e
d
p
o
w
e
r
s
y
st
e
m,”
E
n
e
rg
y
R
e
p
o
r
t
s
,
v
o
l
.
7
,
p
p
.
5
4
67
–
5
4
7
4
,
N
o
v
.
2
0
2
1
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
g
y
r
.
2
0
2
1
.
0
8
.
1
3
3
.
[
3
]
J.
X
i
e
,
I
.
A
l
v
a
r
e
z
-
F
e
r
n
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c
h
i
n
e
lea
rn
in
g
,
Ari
ficia
l
In
telli
g
e
n
c
e
,
so
ftwa
re
e
n
g
in
e
e
rin
g
,
d
a
ta
e
n
g
i
n
e
e
rin
g
,
a
n
d
q
u
a
li
ty
a
ss
u
ra
n
c
e
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
n
a
g
u
lcs
e
@g
m
a
il
.
c
o
m
.
K
a
m
p
a
r
a
p
u
V
.
V
.
S
a
ty
a
Tr
in
a
d
h
N
a
id
u
is
c
u
rre
n
tl
y
wo
r
k
in
g
a
s
As
sista
n
t
P
ro
fe
ss
o
r
i
n
M
a
d
a
n
a
p
a
ll
e
In
stit
u
te
o
f
Tec
h
n
o
l
o
g
y
&
S
c
ien
c
e
,
a
ff
il
iate
d
to
JN
TUA,
An
g
a
ll
u
(V),
M
a
d
a
n
a
p
a
ll
e
.
He
h
a
s
3
y
e
a
rs
o
f
tea
c
h
in
g
e
x
p
e
rien
c
e
i
n
e
n
g
in
e
e
rin
g
e
d
u
c
a
ti
o
n
.
He
re
c
e
iv
e
d
h
is
M
.
Tec
h
in
2
0
1
9
fro
m
P
ra
g
a
ti
En
g
in
e
e
rin
g
Co
l
leg
e
a
ffil
iate
d
to
JN
TUK,
S
u
ra
m
p
a
lem
.
His
re
se
a
rc
h
in
ter
e
sts
in
c
lu
d
e
m
a
c
h
in
e
lea
rn
i
n
g
,
d
e
e
p
lea
rn
i
n
g
a
n
d
c
lo
u
d
c
o
m
p
u
ti
n
g
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
k
.
tri
n
a
d
h
1
2
6
9
@
g
m
a
il
.
c
o
m
.
Chu
n
d
u
r
i
M
o
h
a
n
c
u
rre
n
tl
y
w
o
rk
i
n
g
a
s
As
so
c
iate
P
ro
fe
ss
o
r
in
BVSR
En
g
i
n
e
e
rin
g
c
o
ll
e
g
e
a
ffil
iate
d
to
JN
TUK,
c
h
ima
k
u
rth
y
,
o
n
g
o
le
.
H
e
h
a
s
1
3
y
e
a
rs
o
f
tea
c
h
in
g
e
x
p
e
rien
c
e
in
e
n
g
i
n
e
e
rin
g
e
d
u
c
a
ti
o
n
.
He
re
c
e
iv
e
d
h
is
M
.
Tec
h
in
2
0
1
2
fr
o
m
JN
TUA.
His
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
m
a
c
h
in
e
lea
rn
in
g
,
d
e
e
p
lea
rn
i
n
g
,
n
e
tw
o
rk
s,
a
rti
ficia
l
i
n
telli
g
e
n
c
e
,
a
n
d
c
lo
u
d
c
o
m
p
u
ti
n
g
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
i
l:
c
h
u
n
d
u
ri
2
4
@
g
m
a
il
.
c
o
m
.
Po
tta
sir
i
Ch
a
n
d
r
a
M
o
h
a
n
a
R
a
i
wo
rk
in
g
a
s
a
ss
t.
p
ro
fe
ss
o
r
in
k
lef
fro
m
m
a
rc
h
1
4
,
2
0
2
2
.
Ov
e
ra
ll
,
h
e
h
a
s
1
4
y
e
a
rs
o
f
tea
c
h
in
g
e
x
p
e
rien
c
e
.
H
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
c
m
ra
i.
p
o
tt
a
siri@k
l
u
n
i
v
e
rsity
.
in
.
La
k
shm
i
Bh
u
k
y
a
c
u
rre
n
tl
y
i
n
th
e
De
p
a
rtme
n
t
o
f
El
e
c
tro
n
ics
a
n
d
Co
m
m
u
n
ica
ti
o
n
En
g
i
n
e
e
rin
g
,
In
d
ian
I
n
stit
u
te
o
f
In
fo
rm
a
ti
o
n
Tec
h
n
o
l
o
g
y
,
Ra
j
iv
G
a
n
d
h
i
U
n
iv
e
rsit
y
o
f
Kn
o
wle
d
g
e
Tec
h
n
o
lo
g
ies
,
An
d
h
r
a
P
ra
d
e
sh
,
In
d
ia.
Re
se
a
rc
h
in
tres
t
s
a
re
Artifi
c
ial
In
telli
g
e
n
c
e
in
ECE
,
e
m
b
e
d
d
e
d
sy
ste
m
s,
n
a
n
o
e
lec
tro
n
ics
,
i
n
tern
e
t
o
f
t
h
i
n
g
s
(
Io
T)
s
e
c
u
rit
y
,
r
o
b
o
ti
c
s
a
n
d
a
u
to
m
a
ti
o
n
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
b
lak
sh
m
it
h
@g
m
a
il
.
c
o
m
.
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