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d
ail
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d
s
[
1
]
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[
2
]
.
E
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b
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co
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in
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ical,
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d
[
3
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,
[
4
]
.
Alo
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with
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g
r
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wth
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eq
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p
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[
5
]
.
PLN
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ter
m
p
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tem
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ev
elo
p
m
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t
p
lan
[
6
]
–
[
8
]
.
T
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is
p
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,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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N:
2088
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1
1
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[
1
4
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R
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L
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[
1
5
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tr
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er
n
el
ex
tr
em
e
lear
n
in
g
m
ac
h
in
e
(
MK
E
L
M)
f
o
r
s
h
o
r
t
-
ter
m
lo
ad
f
o
r
ec
asti
n
g
(
STL
F)
,
ac
h
iev
in
g
h
i
g
h
ac
c
u
r
ac
y
an
d
r
o
b
u
s
tn
ess
ag
ain
s
t
d
ata
v
ar
iab
ilit
y
.
Similar
ly
,
Z
ü
g
e
an
d
C
o
elh
o
[
1
6
]
p
r
o
p
o
s
ed
a
g
r
a
n
u
lar
weig
h
te
d
f
u
zz
y
ap
p
r
o
ac
h
th
at
ef
f
ec
tiv
el
y
h
an
d
led
u
n
ce
r
tain
ty
in
s
h
o
r
t
-
ter
m
lo
a
d
d
em
a
n
d
f
o
r
ec
asti
n
g
.
Yo
lcu
et
a
l.
[
1
7
]
d
ev
elo
p
ed
a
ca
s
ca
d
e
i
n
tu
itio
n
is
tic
f
u
zz
y
tim
e
s
er
ies
m
o
d
el
in
teg
r
ated
with
n
eu
r
al
n
etwo
r
k
s
to
e
n
h
an
ce
n
o
n
lin
ea
r
p
atter
n
r
ec
o
g
n
itio
n
in
elec
tr
i
city
lo
ad
p
r
ed
ictio
n
.
Par
k
an
d
Yan
g
[
1
8
]
c
o
n
d
u
cted
a
co
m
p
ar
ativ
e
an
al
y
s
is
o
f
s
ev
er
al
tim
e
-
s
er
ies
alg
o
r
ith
m
s
—
in
clu
d
in
g
au
to
r
eg
r
ess
iv
e
in
teg
r
ated
m
o
v
in
g
av
e
r
ag
e
(
AR
I
MA
)
,
s
ea
s
o
n
al
AR
I
MA
(
SAR
I
MA
)
,
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
L
STM
)
,
an
d
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SVM
)
—
f
o
r
s
h
o
r
t
-
ter
m
f
o
r
ec
asti
n
g
b
ased
o
n
ad
v
an
ce
d
m
eter
in
g
in
f
r
astru
ctu
r
e
(
AM
I
)
d
ata,
w
h
er
e
SVM
ac
h
iev
ed
th
e
b
est
p
er
f
o
r
m
an
ce
in
m
o
d
elin
g
n
o
n
lin
ea
r
a
n
d
v
o
latile
d
em
an
d
p
atter
n
s
.
W
an
g
et
a
l.
[
1
9
]
f
u
r
th
e
r
im
p
r
o
v
ed
s
h
o
r
t
-
ter
m
elec
tr
ical
lo
ad
f
o
r
ec
a
s
tin
g
ac
cu
r
ac
y
b
y
co
m
b
in
in
g
an
ex
tr
em
e
lear
n
i
n
g
m
ac
h
in
e
(
E
L
M)
with
a
n
e
n
h
an
ce
d
o
p
tim
izatio
n
alg
o
r
ith
m
.
A
f
u
zz
y
–
s
war
m
in
tellig
en
ce
h
y
b
r
i
d
m
o
d
el
w
as
p
r
esen
ted
in
[
2
0
]
,
d
em
o
n
s
tr
atin
g
s
u
p
er
io
r
co
n
v
er
g
e
n
c
e
an
d
s
tab
ilit
y
f
o
r
d
y
n
am
ic
lo
a
d
p
r
e
d
ictio
n
.
I
b
r
ah
im
an
d
R
ab
elo
[
2
1
]
p
r
o
p
o
s
ed
a
d
ee
p
lear
n
in
g
-
b
ased
m
o
d
el
f
o
r
p
ea
k
lo
a
d
f
o
r
ec
asti
n
g
u
s
in
g
L
STM
,
em
p
h
asizin
g
its
ca
p
ab
ilit
y
in
ca
p
tu
r
in
g
tem
p
o
r
al
d
ep
e
n
d
en
ci
es.
Fan
et
a
l.
[
2
2
]
d
ev
elo
p
e
d
a
h
y
b
r
id
m
o
d
el
in
teg
r
atin
g
em
p
ir
ical
m
o
d
e
d
ec
o
m
p
o
s
itio
n
(
E
MD
)
,
s
u
p
p
o
r
t
v
ec
to
r
r
eg
r
ess
io
n
(
SVR
)
,
p
ar
ticle
s
war
m
o
p
ti
m
izatio
n
(
PS
O)
,
a
n
d
AR
-
GARC
H
f
o
r
elec
tr
icity
co
n
s
u
m
p
tio
n
f
o
r
ec
asti
n
g
,
r
esu
ltin
g
in
s
ig
n
if
ican
t
er
r
o
r
r
e
d
u
ctio
n
c
o
m
p
ar
e
d
to
co
n
v
en
ti
o
n
al
AR
I
MA
m
o
d
els.
I
n
a
d
d
it
io
n
,
B
o
s
e
an
d
Ma
li
[
2
3
]
p
r
o
v
i
d
ed
a
co
m
p
r
eh
e
n
s
iv
e
s
u
r
v
ey
o
f
f
u
zz
y
tim
e
s
er
ies
f
o
r
ec
asti
n
g
m
o
d
els,
h
i
g
h
lig
h
tin
g
th
eir
ad
ap
ta
b
ilit
y
f
o
r
n
o
n
lin
ea
r
a
n
d
u
n
ce
r
tain
lo
ad
d
ata.
Palo
m
er
o
et
a
l.
[
2
4
]
c
o
n
d
u
cte
d
a
s
y
s
tem
atic
r
ev
iew
o
f
f
u
zz
y
-
b
ased
tim
e
s
er
ies
f
o
r
ec
asti
n
g
an
d
m
o
d
eli
n
g
f
r
o
m
2
0
1
7
to
2
0
2
1
,
co
n
clu
d
in
g
th
at
h
y
b
r
id
f
u
zz
y
–
m
ac
h
i
n
e
lear
n
in
g
m
eth
o
d
s
co
n
s
is
ten
tly
o
u
tp
er
f
o
r
m
class
ical
s
tatis
t
ical
ap
p
r
o
ac
h
es in
ter
m
s
o
f
MA
PE
an
d
R
MSE
m
etr
ics.
T
h
is
s
tu
d
y
in
tr
o
d
u
ce
s
m
ac
h
in
e
lear
n
in
g
m
eth
o
d
s
as
an
ap
p
r
o
ac
h
to
en
h
a
n
ce
th
e
ac
cu
r
ac
y
o
f
s
h
o
r
t
-
ter
m
elec
tr
icity
d
em
an
d
f
o
r
e
ca
s
tin
g
.
Pre
v
io
u
s
r
esear
ch
h
as
d
em
o
n
s
tr
ated
th
e
ef
f
ec
tiv
en
ess
o
f
m
ac
h
in
e
lear
n
in
g
in
p
r
ed
ictin
g
elec
tr
ic
ity
n
ee
d
s
,
o
f
f
er
i
n
g
n
ew
h
o
p
e
in
ad
d
r
ess
in
g
in
ac
cu
r
ac
ies
c
au
s
ed
b
y
d
y
n
am
ic
co
n
s
u
m
p
tio
n
p
atter
n
c
h
an
g
es.
Sp
ec
if
ically
,
th
is
s
tu
d
y
e
m
p
l
o
y
s
th
e
AR
I
MA
m
o
d
el
with
in
th
e
B
o
x
-
J
en
k
in
s
f
r
am
ewo
r
k
t
o
p
r
o
v
id
e
a
s
tr
o
n
g
th
eo
r
etica
l
f
o
u
n
d
atio
n
f
o
r
s
h
o
r
t
-
ter
m
elec
tr
icity
d
em
an
d
f
o
r
ec
asti
n
g
.
T
h
e
AR
I
MA
m
o
d
el
is
s
elec
ted
t
h
r
o
u
g
h
au
to
m
ated
m
o
d
el
s
elec
tio
n
u
s
in
g
th
e
Ak
aik
e
in
f
o
r
m
ati
o
n
c
r
iter
io
n
(
AI
C
)
to
en
s
u
r
e
o
p
tim
al
m
o
d
el
p
er
f
o
r
m
an
ce
.
T
h
e
r
o
b
u
s
tn
ess
o
f
th
e
m
o
d
el
is
v
alid
ated
ac
r
o
s
s
d
if
f
er
en
t
d
ata
s
p
an
s
to
s
tr
en
g
th
en
its
ap
p
licab
ilit
y
.
T
h
e
aim
o
f
th
is
r
esear
ch
is
to
o
p
tim
ize
s
h
o
r
t
-
ter
m
elec
tr
icity
d
em
an
d
f
o
r
ec
asti
n
g
in
So
u
t
h
Su
lawe
s
i,
th
er
eb
y
r
ed
u
cin
g
th
e
g
ap
b
et
wee
n
p
r
o
jecte
d
an
d
ac
t
u
al
p
e
ak
lo
ad
s
.
Mo
r
e
ac
cu
r
ate
an
d
r
ea
lis
tic
f
o
r
ec
asts
ar
e
ex
p
ec
ted
to
s
u
p
p
o
r
t
d
aily
o
p
er
atio
n
al
p
lan
n
in
g
,
im
p
r
o
v
e
in
v
estme
n
t
ef
f
icien
cy
,
a
n
d
en
h
an
ce
d
ec
is
io
n
-
m
ak
in
g
in
th
e
p
o
wer
s
ec
to
r
.
Fu
r
th
e
r
m
o
r
e,
th
e
f
in
d
in
g
s
o
f
th
is
s
tu
d
y
ar
e
an
ticip
ated
to
c
o
n
tr
ib
u
te
t
o
th
e
r
ef
in
em
e
n
t
o
f
p
lan
n
in
g
an
d
o
p
er
atio
n
al
s
tr
ateg
ies
in
th
e
d
ev
elo
p
m
en
t
o
f
th
e
n
ex
t
R
e
n
ca
n
a
Umu
m
P
en
ye
d
ia
a
n
Ten
a
g
a
Lis
t
r
ik
(
R
UPTL
)
.
T
h
is
will
h
elp
m
in
im
ize
d
ev
iatio
n
s
b
et
wee
n
p
r
o
jectio
n
s
an
d
ac
tu
al
lo
ad
s
,
an
d
o
p
tim
iz
e
in
v
estme
n
ts
in
th
e
elec
tr
icity
s
ec
to
r
.
2.
M
E
T
H
O
D
2
.
1
.
E
lect
rica
l
s
y
s
t
em
T
h
e
elec
tr
ical
s
y
s
tem
is
an
in
ter
co
n
n
ec
ted
u
n
it
wh
er
e
el
ec
tr
icity
p
r
o
d
u
ce
d
b
y
p
o
wer
p
lan
ts
i
s
d
eliv
er
ed
to
elec
tr
icity
u
s
er
s
(
co
n
s
u
m
er
s
)
ac
c
o
r
d
i
n
g
to
t
h
eir
n
ee
d
s
,
as
illu
s
tr
ated
in
Fig
u
r
e
1
[
2
5
]
.
E
lectr
icity
is
an
en
e
r
g
y
th
at
ca
n
b
e
wasted
if
n
o
t
u
s
ed
im
m
ed
iately
,
an
d
it
ca
n
n
o
t
b
e
s
to
r
ed
in
lar
g
e
q
u
an
titi
es
b
ec
au
s
e,
to
th
is
d
ay
,
b
atter
y
s
to
r
ag
e
ca
p
ac
ity
r
em
ain
s
v
er
y
lim
ited
[
2
6
]
.
T
h
er
e
f
o
r
e,
th
e
elec
tr
icit
y
p
r
o
d
u
ce
d
m
u
s
t
b
e
ad
ju
s
ted
to
m
atch
th
e
am
o
u
n
t
o
f
elec
tr
ical
lo
ad
r
eq
u
ir
e
d
b
y
c
o
n
s
u
m
er
s
[
2
7
]
.
2
.
2
.
F
o
re
ca
s
t
ing
Fo
r
ec
asti
n
g
,
o
r
p
r
ed
ictio
n
,
is
a
s
y
s
tem
atic
p
r
o
ce
s
s
o
f
esti
m
atin
g
wh
at
m
ay
h
ap
p
en
in
th
e
f
u
tu
r
e
b
ased
o
n
p
ast
an
d
p
r
esen
t
in
f
o
r
m
atio
n
t
o
m
in
im
ize
er
r
o
r
s
[
2
8
]
.
Acc
o
r
d
in
g
to
Heiz
er
a
n
d
R
en
d
er
(
2
0
1
4
)
,
f
o
r
ec
asti
n
g
is
b
o
th
an
ar
t
an
d
a
s
cien
ce
o
f
p
r
ed
ictin
g
f
u
tu
r
e
ev
en
ts
u
s
in
g
h
is
to
r
ical
d
ata
an
d
p
r
o
jectin
g
th
em
in
to
th
e
f
u
t
u
r
e
with
v
a
r
io
u
s
m
ath
em
atica
l
m
o
d
els
[
2
9
]
.
T
h
er
ef
o
r
e
,
f
o
r
ec
asti
n
g
d
o
es
n
o
t
g
u
a
r
an
tee
ce
r
tain
ty
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
6
,
Decem
b
e
r
20
25
:
5
9
2
4
-
5
9
3
3
5926
b
u
t p
r
o
v
id
es a
p
r
o
b
a
b
ilit
y
b
ased
o
n
s
o
lid
g
r
o
u
n
d
s
,
aid
in
g
d
e
cisi
o
n
-
m
ak
in
g
b
y
co
n
s
id
er
in
g
f
ac
to
r
s
s
u
ch
as d
ata
s
o
u
r
ce
s
,
m
o
d
elin
g
m
eth
o
d
s
,
an
d
f
u
tu
r
e
co
n
d
itio
n
s
[
3
0
]
.
Fo
r
ec
asti
n
g
ca
n
b
e
c
ateg
o
r
iz
ed
in
to
two
ty
p
es:
q
u
alitativ
e
f
o
r
ec
asti
n
g
,
wh
ic
h
u
s
es
ca
teg
o
r
ical
d
ata
f
r
o
m
th
e
p
ast,
an
d
q
u
an
titativ
e
f
o
r
ec
asti
n
g
,
wh
ic
h
em
p
lo
y
s
n
u
m
er
ical
d
ata
u
n
d
er
th
e
ass
u
m
p
tio
n
th
at
ce
r
tain
p
atter
n
s
f
r
o
m
th
e
p
ast
will
co
n
tin
u
e
in
th
e
f
u
tu
r
e.
I
n
th
e
co
n
tex
t
o
f
d
em
an
d
f
o
r
ec
asti
n
g
,
p
r
ed
ictin
g
f
u
tu
r
e
e
lectr
icity
co
n
s
u
m
p
tio
n
is
cr
u
cial
to
en
s
u
r
e
th
at
en
er
g
y
is
av
ailab
le
wh
en
n
ee
d
ed
.
Acc
u
r
ate
f
o
r
ec
asti
n
g
f
o
r
m
s
th
e
b
asis
f
o
r
d
ev
elo
p
in
g
i
n
v
estme
n
t
p
lan
s
an
d
o
p
er
atio
n
al
s
tr
ateg
ies
f
o
r
th
e
p
o
wer
s
y
s
tem
.
I
n
v
estme
n
t
p
lan
s
f
o
r
p
o
wer
p
lan
t
d
ev
el
o
p
m
en
t
ar
e
cr
ea
ted
b
y
t
h
e
I
n
d
o
n
esian
g
o
v
er
n
m
e
n
t,
ex
e
cu
ted
b
y
s
tate
-
o
wn
ed
e
n
ter
p
r
is
es
o
r
p
r
iv
ate
c
o
m
p
an
ies,
an
d
ar
e
b
ased
o
n
ec
o
n
o
m
ic
g
r
o
wth
p
r
o
jectio
n
s
.
Me
an
wh
ile,
th
e
o
p
e
r
atio
n
al
p
l
an
f
o
r
t
h
e
p
o
wer
s
y
s
tem
is
d
es
ig
n
ed
to
e
n
s
u
r
e
t
h
e
co
n
tin
u
o
u
s
av
ailab
ilit
y
o
f
elec
tr
icity
,
r
ely
in
g
o
n
h
is
to
r
ical
u
s
ag
e
d
ata
f
r
o
m
p
r
ev
io
u
s
p
er
io
d
s
.
Fig
u
r
e
1
.
E
lectr
ical
s
y
s
tem
2
.
3
.
T
im
e
s
er
ies a
na
ly
s
is
a
nd
f
o
re
ca
s
t
ing
T
im
e
s
er
ies
d
ata
is
a
ty
p
e
o
f
d
ata
co
llected
in
a
s
p
ec
if
ic
ti
m
e
o
r
d
er
o
v
e
r
a
ce
r
tain
p
er
io
d
.
T
h
e
b
asic
p
r
em
is
e
o
f
tim
e
s
er
ies
is
th
at
th
e
cu
r
r
e
n
t
o
b
s
er
v
atio
n
(
)
is
in
f
lu
en
ce
d
b
y
o
n
e
o
r
m
o
r
e
p
r
e
v
io
u
s
o
b
s
er
v
atio
n
s
(
−
)
[
3
1
]
.
T
h
e
p
u
r
p
o
s
e
o
f
ti
m
e
s
er
ies
an
aly
s
is
is
to
u
n
d
er
s
tan
d
an
d
e
x
p
lain
s
p
ec
if
ic
m
ec
h
an
is
m
s
,
f
o
r
ec
ast
a
f
u
tu
r
e
v
al
u
e,
an
d
o
p
tim
ize
a
co
n
tr
o
l
s
y
s
tem
.
Fo
r
ec
asti
n
g
i
s
th
e
ac
tiv
ity
o
f
esti
m
atin
g
s
o
m
eth
in
g
th
at
will
h
ap
p
en
in
t
h
e
f
u
tu
r
e
o
v
er
a
r
e
lativ
ely
lo
n
g
p
er
i
o
d
.
I
n
co
n
tr
a
s
t,
a
p
r
ed
ictio
n
r
e
f
er
s
to
a
co
n
d
itio
n
ex
p
ec
ted
to
o
cc
u
r
i
n
th
e
f
u
tu
r
e.
T
o
m
a
k
e
s
u
ch
p
r
ed
ictio
n
s
,
ac
c
u
r
ate
p
ast
d
ata
is
r
eq
u
ir
ed
t
o
h
el
p
d
eter
m
in
e
f
u
tu
r
e
s
itu
atio
n
s
.
2
.
4
.
ARIM
A
m
et
ho
d
T
h
e
AR
I
MA
m
eth
o
d
is
a
tim
e
s
er
ies
an
aly
s
is
m
eth
o
d
k
n
o
wn
as
th
e
B
o
x
-
J
en
k
in
s
m
eth
o
d
[
3
2
]
.
T
h
is
m
eth
o
d
co
m
b
i
n
es
th
e
a
u
to
r
eg
r
ess
iv
e
(
AR
)
an
d
m
o
v
in
g
av
e
r
ag
e
(
MA
)
m
o
d
els
d
ev
elo
p
e
d
b
y
Geo
r
g
e
B
o
x
an
d
Gwily
m
J
en
k
in
s
.
Acc
o
r
d
in
g
to
th
e
B
o
x
-
J
en
k
in
s
m
eth
o
d
o
l
o
g
y
,
th
e
AR
I
MA
m
eth
o
d
co
n
s
is
ts
o
f
f
o
u
r
s
tag
es:
id
en
tific
atio
n
o
f
th
e
tim
e
s
er
ies
m
o
d
el,
esti
m
atio
n
o
f
p
ar
a
m
eter
s
f
o
r
alter
n
ativ
e
m
o
d
els,
m
o
d
el
test
in
g
,
an
d
f
o
r
ec
asti
n
g
o
f
tim
e
s
er
ies
v
alu
es
[
3
3
]
.
T
h
e
s
tatio
n
ar
ity
ass
u
m
p
tio
n
is
a
p
r
er
eq
u
is
ite
f
o
r
m
o
d
elin
g
tim
e
s
er
ies.
A
n
o
n
-
s
tatio
n
ar
y
s
er
ies
ca
n
b
e
tr
an
s
f
o
r
m
ed
in
to
a
s
tatio
n
ar
y
s
er
ies
b
y
d
if
f
er
en
ci
n
g
.
T
o
v
er
i
f
y
s
tatio
n
ar
ity
an
d
g
u
id
e
d
if
f
er
e
n
cin
g
s
elec
tio
n
,
an
au
g
m
en
ted
Dick
ey
-
Fu
ller
(
ADF)
test
was
co
n
d
u
cted
o
n
th
e
p
ea
k
lo
ad
d
ata.
T
h
e
test
r
esu
lts
in
f
o
r
m
e
d
th
e
a
p
p
r
o
p
r
iate
d
if
f
er
en
cin
g
o
r
d
er
(
d
)
ap
p
lied
b
e
f
o
r
e
m
o
d
el
f
itti
n
g
.
No
n
-
s
tatio
n
ar
ity
in
a
tim
e
s
er
ies
ca
n
in
v
o
lv
e
a
n
o
n
-
co
n
s
tan
t
m
ea
n
,
a
n
o
n
-
c
o
n
s
tan
t
v
ar
ian
ce
,
o
r
b
o
t
h
(
n
o
n
-
co
n
s
tan
t
m
ea
n
an
d
v
ar
ian
ce
)
[
3
4
]
.
T
h
e
g
en
er
al
f
o
r
m
o
f
t
h
e
AR
I
MA
m
o
d
el
eq
u
a
tio
n
is
:
Φ
(
)
(
1
−
)
=
Θ
(
)
wh
er
e
is
th
e
b
ac
k
s
h
if
t
o
p
e
r
ato
r
,
Φ
(
)
is
th
e
au
to
r
eg
r
ess
iv
e
o
p
er
ato
r
,
Θ
(
)
is
th
e
m
o
v
in
g
av
e
r
ag
e
o
p
er
ato
r
,
an
d
r
ep
r
esen
ts
wh
ite
n
o
is
e.
2
.
5
.
Sim
ula
t
i
o
n
a
nd
im
plementa
t
io
n
det
a
il
t
he
im
plem
en
t
a
t
io
n
o
f
t
he
ARIM
A
Mo
d
els
in
th
is
s
tu
d
y
was
co
n
d
u
cted
u
s
in
g
Py
th
o
n
v
e
r
s
io
n
3
.
1
0
with
th
e
p
md
a
r
ima
lib
r
a
r
y
v
er
s
io
n
1
.
8
.
5
.
T
h
e
d
ataset
u
s
ed
c
o
n
s
is
ts
o
f
d
aily
p
ea
k
lo
ad
d
ata
o
b
t
ain
ed
f
r
o
m
th
e
So
u
th
Su
lawe
s
i
elec
tr
ical
s
y
s
tem
d
atab
ase
m
an
ag
e
d
b
y
PT
.
PLN
(
Per
s
er
o
)
,
co
v
er
in
g
two
p
er
io
d
s
:
J
an
u
ar
y
1
,
2
0
2
2
to
Octo
b
er
3
1
,
2
0
2
2
(
3
9
6
r
ec
o
r
d
s
)
,
a
n
d
J
an
u
a
r
y
1
,
2
0
1
8
to
J
u
l
y
1
4
,
2
0
2
3
(
2
0
2
1
r
ec
o
r
d
s
)
.
T
h
e
v
ar
iab
les
ex
tr
a
cted
in
clu
d
e
d
ail
y
m
ax
im
u
m
p
ea
k
(
DM
P),
p
o
we
r
b
alan
ce
(
B
P),
MV
Sen
t,
MV
r
ec
eiv
ed
,
ca
p
ac
ity
a
v
ailab
le
(
C
AD)
,
an
d
s
y
s
tem
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I
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N:
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0
8
Op
timiz
in
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s
h
o
r
t
-
term e
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erg
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d
ema
n
d
f
o
r
ec
a
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g
:
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c
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mp
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eh
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(
F
ir
ma
n
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z
iz
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5927
s
tatu
s
,
co
llected
f
o
r
b
o
th
d
ay
ti
m
e
an
d
n
ig
h
ttime
.
Data
p
r
ep
r
o
ce
s
s
in
g
in
clu
d
ed
h
a
n
d
lin
g
m
is
s
in
g
v
alu
es
u
s
in
g
lin
ea
r
in
ter
p
o
latio
n
an
d
n
o
r
m
aliza
tio
n
(
m
in
-
m
ax
s
ca
lin
g
)
f
o
r
v
is
u
aliza
tio
n
.
T
h
e
s
tatio
n
ar
ity
o
f
th
e
d
ata
was
v
er
if
ied
u
s
in
g
th
e
au
g
m
en
ted
d
ick
ey
-
f
u
ller
(
ADF)
test
.
Mo
d
el
s
elec
tio
n
was
p
er
f
o
r
m
ed
th
r
o
u
g
h
a
u
to
_
a
r
ima
f
r
o
m
p
md
a
r
ima
,
wh
ich
au
to
m
ated
th
e
s
ea
r
ch
f
o
r
o
p
tim
al
(
p
,
d
,
q
)
p
a
r
am
eter
s
b
ased
o
n
th
e
m
in
im
u
m
AI
C
.
T
h
e
s
ea
r
ch
s
p
ac
e
was
s
et
f
r
o
m
0
to
5
f
o
r
p
an
d
q
,
an
d
u
p
to
2
f
o
r
d
,
in
cl
u
d
in
g
s
ea
s
o
n
al
an
d
n
o
n
-
s
ea
s
o
n
al
s
ettin
g
s
.
T
h
e
m
o
d
el
tr
ain
in
g
u
s
ed
th
e
en
tire
d
ataset
f
o
r
ea
ch
ex
p
er
im
en
tal
p
er
i
o
d
,
an
d
R
MSE
wa
s
u
s
ed
to
ev
alu
ate
f
o
r
ec
asti
n
g
ac
cu
r
ac
y
.
Simu
latio
n
s
wer
e
r
u
n
o
n
a
s
tan
d
ar
d
lap
to
p
with
I
n
tel
C
o
r
e
i7
p
r
o
ce
s
s
o
r
an
d
1
6
GB
R
AM
,
en
s
u
r
in
g
r
ep
r
o
d
u
cib
ilit
y
with
o
p
en
-
s
o
u
r
ce
to
o
ls
.
2
.
6
.
J
us
t
if
ica
t
io
n o
f
m
et
ho
d
o
lo
g
y
a
nd
co
ncept
ua
l f
ra
m
e
wo
rk
Ap
p
r
o
ac
h
i
n
th
is
s
tu
d
y
is
b
ased
o
n
th
e
AR
I
MA
m
o
d
el
with
in
th
e
B
o
x
-
J
en
k
in
s
f
r
am
ewo
r
k
,
co
n
s
is
tin
g
o
f
m
o
d
el
id
en
tific
atio
n
,
p
ar
am
eter
esti
m
atio
n
,
d
iag
n
o
s
tic
ch
ec
k
in
g
,
an
d
f
o
r
ec
asti
n
g
.
T
o
en
s
u
r
e
r
ig
o
r
in
m
o
d
el
s
elec
tio
n
,
au
to
m
ated
h
y
p
er
p
a
r
am
eter
tu
n
in
g
f
o
r
p
,
d
,
an
d
q
was
p
er
f
o
r
m
ed
u
s
in
g
th
e
Py
th
o
n
-
b
ased
p
m
d
a
r
ima
lib
r
ar
y
.
T
h
is
to
o
l
s
y
s
tem
atica
lly
ev
alu
ates
m
u
ltip
le
AR
I
M
A
co
n
f
ig
u
r
atio
n
s
an
d
s
elec
ts
th
e
o
p
tim
al
m
o
d
el
b
ased
o
n
th
e
lo
west
AI
C
v
alu
e,
th
er
eb
y
b
alan
cin
g
m
o
d
el
co
m
p
lex
ity
an
d
f
o
r
ec
asti
n
g
a
cc
u
r
ac
y
.
B
ay
esian
in
f
o
r
m
atio
n
cr
iter
io
n
(
B
I
C
)
v
alu
es
wer
e
also
ex
am
in
e
d
t
o
p
r
o
v
id
e
ad
d
itio
n
al
co
n
f
ir
m
atio
n
d
u
r
in
g
m
o
d
el
s
elec
tio
n
.
C
r
o
s
s
-
v
alid
atio
n
w
as
n
o
t
ap
p
lied
in
th
is
s
tu
d
y
d
u
e
to
th
e
s
eq
u
en
tial
d
ep
en
d
e
n
cy
in
h
er
en
t
in
tim
e
s
er
ies
d
ata
an
d
b
ec
au
s
e
t
h
e
u
s
e
o
f
in
f
o
r
m
atio
n
cr
iter
ia
(
AI
C
,
B
I
C
)
is
s
tan
d
ar
d
in
th
e
B
o
x
-
J
en
k
in
s
m
eth
o
d
o
l
o
g
y
.
T
h
e
co
n
ce
p
t
u
a
l
n
o
v
elty
o
f
th
is
s
tu
d
y
lies
i
n
ap
p
ly
in
g
AR
I
MA
ac
r
o
s
s
d
if
f
er
en
t
d
ata
s
p
an
s
(
s
h
o
r
t
-
ter
m
a
n
d
lo
n
g
-
te
r
m
d
at
asets
)
to
v
alid
ate
m
o
d
el
r
o
b
u
s
tn
ess
f
o
r
s
h
o
r
t
-
ter
m
p
ea
k
l
o
a
d
f
o
r
ec
asti
n
g
.
T
h
is
ap
p
r
o
ac
h
p
r
o
v
id
es
p
r
ac
tical
v
alu
e
f
o
r
d
aily
o
p
er
atio
n
al
p
lan
n
in
g
an
d
in
v
estme
n
t
ef
f
icien
cy
,
an
d
o
f
f
e
r
s
a
f
o
u
n
d
atio
n
f
o
r
f
u
tu
r
e
e
n
h
an
ce
m
en
ts
th
r
o
u
g
h
m
ac
h
in
e
lear
n
i
n
g
in
teg
r
atio
n
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
3
.
1
.
Da
t
a
s
et
T
h
e
s
u
cc
ess
o
f
r
esear
ch
h
ea
v
ily
d
ep
e
n
d
s
o
n
th
e
q
u
ality
o
f
th
e
d
ata
o
b
tain
ed
.
W
ith
o
u
t
a
th
o
r
o
u
g
h
u
n
d
er
s
tan
d
i
n
g
o
f
th
e
ap
p
r
o
p
r
iate
d
ata
co
llectio
n
tech
n
iq
u
e
s
,
r
esear
ch
er
s
m
ay
s
tr
u
g
g
le
to
o
b
tain
d
ata
th
at
m
ee
ts
th
e
d
esire
d
s
tan
d
ar
d
s
o
f
v
alid
ity
a
n
d
r
eliab
ilit
y
.
T
h
er
ef
o
r
e
,
d
ata
c
o
llectio
n
m
et
h
o
d
s
ar
e
a
cr
u
cial
elem
en
t
in
s
cien
tific
r
esear
c
h
.
T
o
ac
q
u
ir
e
v
alid
a
n
d
ac
cu
r
ate
d
ata,
r
esear
ch
er
s
m
u
s
t
h
av
e
ac
ce
s
s
to
tr
u
s
two
r
th
y
an
d
r
elev
a
n
t
d
ata
s
o
u
r
ce
s
.
I
n
th
is
s
tu
d
y
,
t
h
e
r
esear
ch
er
g
ain
e
d
ac
ce
s
s
to
th
e
elec
tr
ical
s
y
s
tem
d
atab
ase
an
d
Po
wer
B
alan
ce
m
an
ag
ed
b
y
PT
.
PLN
(
Per
s
er
o
)
.
T
h
is
ac
ce
s
s
allo
ws
th
e
r
ese
ar
ch
er
t
o
co
llect
t
h
e
n
ec
ess
ar
y
d
ata
d
ir
ec
tly
f
r
o
m
an
au
th
en
tic
s
o
u
r
ce
,
th
er
e
b
y
s
u
p
p
o
r
tin
g
th
e
v
alid
ity
o
f
th
e
f
in
d
in
g
s
an
d
co
n
clu
s
io
n
s
d
r
awn
.
T
h
e
d
at
aset
o
b
tain
ed
co
v
er
s
two
p
e
r
io
d
s
:
J
an
u
ar
y
1
,
2
0
2
2
,
to
Octo
b
er
3
1
,
2
0
2
2
,
co
n
s
is
tin
g
o
f
3
9
6
d
aily
p
ea
k
l
o
ad
r
ec
o
r
d
s
,
an
d
J
an
u
ar
y
1
,
2
0
1
8
,
to
J
u
l
y
1
4
,
2
0
2
3
,
co
n
s
is
ti
n
g
o
f
2
0
2
1
r
ec
o
r
d
s
.
T
h
e
d
ata
in
clu
d
e
d
etailed
o
p
e
r
atio
n
al
co
n
d
itio
n
s
s
u
ch
as
d
a
ily
m
ax
im
u
m
p
ea
k
(
DM
P),
p
o
wer
b
alan
ce
(
B
P),
MV
s
en
t,
MV
r
ec
eiv
ed
,
ca
p
ac
ity
av
ailab
le
(
C
AD)
,
an
d
Sy
s
tem
Statu
s
,
r
ec
o
r
d
ed
f
o
r
b
o
th
d
ay
tim
e
an
d
n
ig
h
t
tim
e
p
er
io
d
s
.
W
ith
d
ata
o
b
tai
n
ed
th
r
o
u
g
h
s
tr
u
ctu
r
e
d
an
d
s
y
s
tem
atic
m
eth
o
d
s
,
th
is
r
esear
ch
is
ex
p
ec
ted
to
m
ak
e
a
s
ig
n
if
ican
t
co
n
tr
i
b
u
ti
o
n
to
t
h
e
r
elate
d
f
ield
o
f
s
tu
d
y
a
n
d
m
ee
t
th
e
h
i
g
h
s
tan
d
ar
d
s
r
eq
u
ir
ed
f
o
r
p
u
b
licatio
n
in
r
ep
u
ta
b
le
s
cien
tific
jo
u
r
n
als.
3
.
2
.
Da
t
a
no
r
m
a
liza
t
i
o
n
T
h
e
d
ata
o
b
tain
ed
f
r
o
m
th
e
web
-
b
ased
ap
p
licatio
n
is
c
o
n
v
er
ted
in
to
E
x
ce
l
(
*
.
x
ls
x
)
f
o
r
m
at
t
o
f
ac
ilit
ate
f
u
r
th
er
p
r
o
ce
s
s
in
g
.
T
h
e
f
ir
s
t
s
tep
in
v
o
lv
es
d
ata
n
o
r
m
aliza
tio
n
b
y
s
ep
ar
atin
g
it
in
t
o
in
d
e
p
en
d
en
t
a
n
d
d
ep
en
d
e
n
t
v
a
r
iab
les.
I
n
th
is
s
tu
d
y
,
th
e
in
d
ep
en
d
en
t
v
ar
ia
b
les
co
n
s
is
t
o
f
th
e
d
ate
a
n
d
p
ea
k
lo
a
d
c
o
lu
m
n
s
,
wh
ich
ar
e
u
s
ed
as
p
r
ed
icto
r
s
.
T
h
e
d
ep
e
n
d
en
t
v
ar
iab
les
in
cl
u
d
e
ca
p
ac
ity
,
r
eser
v
e
p
o
wer
,
an
d
s
y
s
tem
s
tatu
s
,
wh
ich
r
ep
r
esen
t
th
e
o
u
t
p
u
ts
to
b
e
p
r
ed
icted
.
Af
te
r
s
ep
ar
ati
o
n
,
th
e
i
n
d
ep
e
n
d
en
t
v
ar
iab
les
ar
e
ex
p
o
r
ted
in
t
o
C
SV
(
*
.
c
s
v
)
f
o
r
m
at
to
e
n
s
u
r
e
co
m
p
atib
ilit
y
with
d
ata
an
al
y
s
is
an
d
m
ac
h
in
e
lear
n
in
g
to
o
l
s
.
T
h
is
f
o
r
m
at
also
m
ak
es
d
ata
h
an
d
lin
g
an
d
s
y
s
tem
in
teg
r
atio
n
ea
s
ier
.
T
h
e
C
SV
f
ile
i
s
th
en
ch
ec
k
ed
f
o
r
an
o
m
alies
s
u
ch
as
m
is
s
in
g
o
r
ze
r
o
v
al
u
es to
m
ain
tain
d
ata
in
teg
r
ity
.
E
n
s
u
r
in
g
clea
n
an
d
c
o
m
p
lete
d
ata
is
cr
itic
al
f
o
r
th
e
ac
c
u
r
ac
y
o
f
th
e
m
ac
h
i
n
e
lear
n
in
g
m
o
d
e
l.
B
y
f
o
llo
win
g
th
ese
s
tep
s
,
th
e
r
esear
ch
er
en
s
u
r
es
th
at
th
e
d
ataset
m
ee
ts
h
ig
h
q
u
ality
s
tan
d
ar
d
s
.
T
h
is
p
r
o
ce
s
s
s
u
p
p
o
r
ts
th
e
d
ev
el
o
p
m
e
n
t
o
f
a
v
alid
an
d
r
eliab
le
p
r
e
d
ictio
n
m
o
d
el,
wh
ile
also
m
ee
tin
g
th
e
m
eth
o
d
o
l
o
g
ical
s
tan
d
ar
d
s
r
e
q
u
ir
ed
f
o
r
s
cien
tific
p
u
b
licatio
n
.
3
.
3
.
Alg
o
rit
hm
i
m
plem
ent
a
t
io
n
T
h
e
im
p
lem
e
n
tatio
n
o
f
AR
I
MA
m
o
d
els
in
th
is
s
tu
d
y
b
u
ild
s
u
p
o
n
th
e
m
eth
o
d
o
lo
g
ical
f
o
u
n
d
atio
n
o
u
tlin
ed
i
n
th
e
p
r
ev
io
u
s
s
ec
tio
n
.
T
h
is
ap
p
r
o
ac
h
f
o
llo
w
s
th
e
B
o
x
-
J
en
k
i
n
s
f
r
am
ew
o
r
k
f
o
r
tim
e
s
er
ies
f
o
r
ec
asti
n
g
,
in
co
r
p
o
r
atin
g
m
o
d
el
id
en
tific
atio
n
,
p
ar
am
ete
r
esti
m
atio
n
,
d
iag
n
o
s
tic
ch
ec
k
i
n
g
,
an
d
f
o
r
ec
asti
n
g
.
T
o
en
s
u
r
e
r
ig
o
r
in
m
o
d
el
s
elec
tio
n
,
th
e
s
tu
d
y
em
p
lo
y
s
au
to
m
ated
h
y
p
e
r
p
ar
am
ete
r
tu
n
in
g
u
s
in
g
th
e
Py
th
o
n
-
b
ased
p
md
a
r
ima
lib
r
ar
y
.
T
h
is
to
o
l
s
y
s
tem
atica
lly
ev
alu
ates
m
u
ltip
le
AR
I
MA
c
o
n
f
ig
u
r
ati
o
n
s
an
d
s
elec
ts
th
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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Vo
l.
15
,
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o
p
tim
al
m
o
d
el
b
ased
o
n
th
e
l
o
west
AI
C
s
co
r
e,
th
er
eb
y
b
alan
cin
g
m
o
d
el
co
m
p
lex
ity
an
d
f
o
r
ec
asti
n
g
ac
cu
r
ac
y
.
B
y
ap
p
ly
in
g
th
is
r
o
b
u
s
t
co
n
c
ep
tu
al
an
d
tech
n
ical
f
r
am
ew
o
r
k
,
th
e
r
esu
lts
p
r
esen
ted
in
th
is
s
ec
tio
n
aim
to
p
r
o
v
id
e
r
eliab
le
an
d
p
r
ac
tical
in
s
ig
h
ts
f
o
r
s
h
o
r
t
-
te
r
m
elec
tr
ic
ity
d
em
an
d
f
o
r
ec
asti
n
g
an
d
e
n
er
g
y
m
a
n
ag
em
en
t.
T
h
e
an
aly
s
is
co
n
d
u
cted
in
th
is
r
ep
o
r
t
u
s
es
th
e
AR
I
MA
m
o
d
el
to
f
o
r
ec
ast
p
ea
k
elec
tr
ical
lo
ad
s
.
T
h
e
test
in
g
is
ca
r
r
ied
o
u
t
in
two
p
h
ases
.
I
n
th
e
f
ir
s
t
ex
p
er
im
en
t,
th
e
p
ea
k
lo
ad
d
ata
u
s
ed
co
m
es
f
r
o
m
th
e
So
u
th
Su
lawe
s
i
Sy
s
tem
f
o
r
th
e
p
er
io
d
f
r
o
m
J
an
u
ar
y
1
,
2
0
2
2
,
to
Octo
b
er
3
1
,
2
0
2
2
,
with
a
to
tal
o
f
3
9
6
p
ea
k
lo
ad
r
ec
o
r
d
s
,
as
illu
s
tr
ated
in
Fig
u
r
e
2
(
a
)
.
B
ased
o
n
th
e
m
o
d
elin
g
r
esu
lt
s
co
n
d
u
cted
u
s
in
g
th
e
AR
I
M
A
m
eth
o
d
with
th
e
h
elp
o
f
th
e
Py
th
o
n
-
b
ased
p
md
a
r
ima
f
u
n
ctio
n
,
2
1
m
o
d
els
h
av
e
b
ee
n
id
e
n
tifie
d
a
s
s
u
itab
le
f
o
r
th
e
“T
r
ain
i
n
g
”
d
ata
ch
ar
ac
ter
is
tics
.
T
h
ese
m
o
d
els
h
av
e
b
ee
n
s
ele
cted
as
th
e
m
o
s
t
ap
p
r
o
p
r
iate
f
o
r
f
o
r
ec
asti
n
g
f
u
t
u
r
e
d
ata.
T
h
is
m
o
d
el
s
elec
tio
n
p
r
o
ce
s
s
was
ca
r
r
ied
o
u
t
m
eticu
lo
u
s
ly
to
en
s
u
r
e
th
at
ea
ch
c
h
o
s
en
m
o
d
el
co
u
l
d
ac
cu
r
ately
ca
p
tu
r
e
th
e
p
atter
n
s
an
d
tr
en
d
s
f
r
o
m
h
is
to
r
ical
d
a
ta,
th
er
eb
y
p
r
o
v
id
in
g
r
eliab
le
p
r
ed
ictio
n
s
f
o
r
f
u
tu
r
e
p
er
io
d
s
.
W
ith
2
1
m
o
d
els
av
ailab
le,
th
e
r
esear
ch
er
h
as
th
e
f
lex
ib
ilit
y
to
ch
o
o
s
e
th
e
m
o
d
el
th
at
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est
m
ee
ts
s
p
ec
if
ic
p
er
f
o
r
m
an
ce
cr
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RMS
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R
M
S
E
38
.
1
2
3
4
5
5
5
4.
CO
NCLU
SI
O
N
T
h
is
s
tu
d
y
p
r
esen
ts
a
c
o
m
p
r
eh
en
s
iv
e
an
aly
s
is
aim
ed
at
o
p
tim
izin
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s
h
o
r
t
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ter
m
en
er
g
y
d
em
a
n
d
f
o
r
ec
asti
n
g
u
s
in
g
th
e
AR
I
MA
m
eth
o
d
with
in
th
e
B
o
x
-
J
en
k
in
s
f
r
am
ewo
r
k
.
B
y
lev
er
a
g
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g
d
aily
p
ea
k
lo
ad
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ata
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r
o
m
So
u
th
Su
lawe
s
i,
th
e
AR
I
MA
m
o
d
el
was
s
u
cc
ess
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u
lly
a
p
p
lied
to
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r
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d
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ce
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ea
lis
tic
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d
ac
cu
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ate
f
o
r
ec
asts
th
at
s
u
p
p
o
r
t
n
o
t
o
n
ly
d
aily
o
p
er
atio
n
al
p
lan
n
in
g
o
f
p
o
wer
p
lan
ts
b
u
t
also
in
v
estme
n
t
an
d
d
ev
el
o
p
m
en
t
p
lan
n
in
g
in
th
e
elec
tr
icity
s
ec
t
o
r
.
T
h
r
o
u
g
h
au
t
o
m
ated
m
o
d
el
s
elec
tio
n
u
s
in
g
p
md
a
r
ima
a
n
d
AI
C
,
th
e
o
p
tim
al
AR
I
MA
co
n
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ig
u
r
atio
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e
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tifie
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o
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el
c
o
m
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lex
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d
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o
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asti
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g
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cu
r
ac
y
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h
e
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al
m
o
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el
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MA
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o
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1
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d
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1
2
3
,
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e
m
o
n
s
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atin
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its
r
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u
s
tn
ess
in
c
ap
tu
r
in
g
s
h
o
r
t
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ter
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lo
ad
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atter
n
s
an
d
f
lu
ct
u
atio
n
s
.
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h
is
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tu
d
y
f
ills
a
c
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itical
r
e
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ea
r
ch
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ap
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y
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r
o
v
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d
in
g
a
s
i
m
p
le
y
et
ef
f
ec
tiv
e
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I
MA
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ased
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o
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el
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r
o
s
s
d
if
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er
en
t
d
ata
s
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an
s
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o
f
f
er
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g
p
r
ac
tical
v
alu
e
f
o
r
d
aily
o
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er
atio
n
al
d
ec
is
io
n
-
m
ak
in
g
.
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h
e
r
esu
lts
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ig
h
lig
h
t
th
e
ca
p
ab
ilit
y
o
f
AR
I
MA
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b
ased
f
o
r
ec
asti
n
g
to
r
ed
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ce
d
ev
iatio
n
s
b
etwe
en
p
r
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jecte
d
an
d
ac
tu
al
p
ea
k
lo
ad
s
,
th
e
r
eb
y
c
o
n
t
r
ib
u
tin
g
to
im
p
r
o
v
ed
o
p
er
at
io
n
al
m
an
ag
e
m
en
t,
en
h
an
ce
d
i
n
v
estme
n
t
ef
f
icien
cy
,
an
d
c
o
s
t
s
av
in
g
s
in
en
er
g
y
m
an
a
g
em
en
t.
M
o
r
eo
v
e
r
,
t
h
is
r
esear
ch
lay
s
a
m
eth
o
d
o
l
o
g
ical
f
o
u
n
d
atio
n
f
o
r
f
u
t
u
r
e
ad
v
an
ce
m
en
ts
,
in
cl
u
d
in
g
t
h
e
in
teg
r
atio
n
o
f
AR
I
MA
with
m
ac
h
in
e
lear
n
in
g
tec
h
n
iq
u
es
a
n
d
th
e
a
p
p
licatio
n
o
f
lo
n
g
er
h
is
to
r
ical
d
atasets
to
f
u
r
th
er
e
n
h
an
ce
f
o
r
ec
asti
n
g
ac
cu
r
ac
y
.
T
h
e
f
in
d
in
g
s
a
r
e
ex
p
ec
ted
t
o
p
r
o
v
id
e
v
alu
a
b
le
in
s
ig
h
ts
f
o
r
r
ef
in
i
n
g
R
UPTL
p
la
n
n
i
n
g
,
s
u
p
p
o
r
tin
g
th
e
d
ev
elo
p
m
e
n
t
o
f
s
m
ar
t
g
r
id
s
y
s
tem
s
,
an
d
s
tr
en
g
th
en
i
n
g
s
tr
ateg
ic
en
er
g
y
m
an
ag
e
m
en
t
at
b
o
th
r
e
g
io
n
al
an
d
n
atio
n
al
lev
els.
ACK
NO
WL
E
DG
M
E
N
T
S
T
h
e
au
th
o
r
s
wo
u
ld
lik
e
to
ex
p
r
ess
th
eir
d
ee
p
est
g
r
atitu
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e
an
d
ap
p
r
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n
to
all
co
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a
u
th
o
r
s
f
o
r
th
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le
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n
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o
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th
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n
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ir
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th
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RE
F
E
R
E
NC
E
S
[
1
]
B
.
M
.
B
u
c
h
h
o
l
z
a
n
d
Z.
S
t
y
c
z
y
n
s
k
i
,
S
m
a
r
t
g
ri
d
s
–
f
u
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e
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p
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e
r
l
i
n
H
e
i
d
e
l
b
e
r
g
,
2
0
1
4
.
[
2
]
S
.
El
-
H
a
g
g
a
r
a
n
d
A
.
S
a
ma
h
a
,
R
o
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o
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n
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b
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l
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0
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9
.
[
3
]
N
.
T
.
W
a
t
s
o
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E
.
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e
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c
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c
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s,
2
0
2
0
.
[
4
]
O
.
P
.
D
i
m
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t
r
i
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v
a
n
d
O
.
P
.
D
i
m
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t
r
i
e
v
,
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Th
e
r
mo
m
e
c
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o
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/
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r
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0
2
3
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0
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4
7
7
2
.
[
5
]
I
.
D
.
A
p
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y
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n
t
i
,
D
.
B
.
N
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l
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d
,
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To
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f
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m
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d
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sa
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t
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k
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g
a
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a
(
P
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)
,
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g
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Re
s
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a
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&
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o
c
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l
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.
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r
ss
.
2
0
2
4
.
1
0
3
7
9
7
.
[
6
]
M
.
M
a
u
l
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d
i
a
,
P
.
D
a
r
g
u
sc
h
,
P
.
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sh
w
o
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t
h
,
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n
d
F
.
A
r
d
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a
n
sy
a
h
,
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R
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t
h
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k
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n
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w
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g
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t
a
r
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s a
n
d
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l
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c
t
r
i
c
i
t
y
sec
t
o
r
r
e
f
o
r
m
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n
I
n
d
o
n
e
si
a
:
a
p
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v
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t
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e
c
t
o
r
p
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s
p
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c
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,
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n
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w
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b
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S
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.
r
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.
[
7
]
T.
T
u
mi
r
a
n
e
t
a
l
.
,
“
P
o
w
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s
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m,
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)
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0
.
3
3
9
0
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1
4
1
4
8
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.
[
8
]
Y
.
S
u
n
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t
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y
o
so
,
J
.
P
.
M
a
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