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:
AR
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Data
L
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Ma
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Pre
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s
STL
F
T
im
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T
h
is i
s
a
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rticle
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d
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CC B
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li
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C
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p
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A
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:
T
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alaiv
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ev
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Su
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Dep
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tm
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lectr
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g
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St.
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s
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I
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d
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ail: su
d
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ak
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td
@
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tjo
s
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s
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ac
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in
1.
I
NT
RO
D
UCT
I
O
N
Sh
o
r
t
–
ter
m
lo
ad
f
o
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ec
asti
n
g
is
ess
en
tial
f
o
r
th
e
elec
tr
ical
s
y
s
tem
to
o
p
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ate
in
a
r
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way
.
W
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m
ak
in
g
d
ec
is
io
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s
ab
o
u
t
u
n
i
t
d
ed
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,
co
s
t
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f
ec
tiv
e
s
u
p
p
ly
,
au
t
o
m
ated
g
en
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r
atin
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co
n
tr
o
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s
af
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ev
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,
p
lan
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m
ain
ten
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ce
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d
is
tr
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u
tio
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g
y
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t
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p
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we
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tem
m
u
s
t
tak
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f
u
tu
r
e
l
o
ad
b
eh
av
io
r
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n
to
ac
co
u
n
t
[
1
]
.
I
n
m
ar
k
ets
wh
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e
p
o
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is
co
m
p
etitiv
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lo
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ates
ar
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cr
u
cial
f
or
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elec
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tr
an
s
ac
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[
2
]
.
I
n
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to
m
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co
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tan
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u
p
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p
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t
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r
e,
th
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f
in
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d
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ag
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f
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atly
im
p
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f
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r
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T
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ig
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ate
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o
r
t
o
d
ay
'
s
p
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wer
s
y
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tem
s
.
[
3
]
,
[
4
]
.
I
n
r
e
c
e
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t
d
ec
a
d
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s
,
r
e
s
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a
r
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h
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f
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d
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as
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t
–
t
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r
m
l
o
a
d
f
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c
a
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ti
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(
S
T
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)
.
T
h
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m
a
j
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m
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s
r
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a
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ass
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h
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m
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a
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c
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q
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s
[
1
]
,
[3
]
-
[
6
]
.
T
h
e
t
i
m
e
c
u
m
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l
a
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2
4
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p
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r
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m
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a
v
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(
AR
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m
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l
f
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r
S
T
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F
[
7
]
-
[
1
0
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
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8
7
7
6
E
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a
d
fo
r
ec
a
s
tin
g
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s
in
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A
R
I
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mo
d
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r
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s
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ta
(
B
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s
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b
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ma
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B
elsh
a
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)
831
Statis
t
ical
m
o
d
els
em
p
lo
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in
tr
icate
co
m
p
u
tatio
n
s
to
f
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s
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p
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f
a
v
ar
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tili
zin
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m
ath
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atica
l
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eg
ates
o
f
th
e
v
ar
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s
h
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to
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ical
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alu
es.
T
h
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ar
ticle'
s
g
o
al
is
to
p
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esen
t
an
AR
I
MA
m
o
d
el
[
1
1
]
-
[
1
7
]
f
o
r
STL
F
th
at,
m
o
s
t
ly
u
s
in
g
tim
e
–
cu
m
u
lativ
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esti
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ates,
c
o
m
p
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tes
2
4
l
o
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p
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tio
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f
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ay
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m
o
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n
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er
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d
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m
in
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m
in
im
u
m
MA
PE
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co
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s
eq
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p
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m
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A
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s
er
ies lo
ad
p
atter
n
s
a
r
e
s
tu
d
ied
u
s
in
g
t
h
is
m
eth
o
d
.
2.
M
ACH
I
N
E
L
E
AR
NING
T
h
e
s
cien
tific
s
tu
d
y
o
f
s
tatis
ti
ca
l
m
o
d
els
an
d
m
eth
o
d
s
th
at
co
m
p
u
ter
s
y
s
tem
s
em
p
lo
y
to
d
o
ce
r
tain
task
s
with
o
u
t
ex
p
licit
p
r
o
g
r
am
m
in
g
is
k
n
o
wn
as
m
ac
h
i
n
e
lear
n
in
g
,
o
r
ML
.
Ma
n
y
d
o
m
ain
s
u
s
e
ML
tech
n
iq
u
es,
s
u
ch
as
p
r
ed
ictiv
e
an
aly
tics
,
d
ata
m
in
in
g
,
i
m
ag
e
p
r
o
ce
s
s
in
g
,
a
n
d
m
an
y
m
o
r
e.
A
lear
n
in
g
alg
o
r
ith
m
th
at
h
as f
o
u
n
d
o
u
t h
o
w
to
r
an
k
web
p
ag
es is
o
n
e
o
f
th
e
r
ea
s
o
n
s
wh
y
an
o
n
lin
e
s
ea
r
ch
en
g
in
e
lik
e
th
e
in
ter
n
et
wo
r
k
s
s
o
ef
f
icien
tly
e
ac
h
tim
e
it
is
u
s
ed
to
s
ea
r
ch
th
e
in
ter
n
et
.
T
h
e
ca
p
ac
ity
o
f
alg
o
r
ith
m
s
to
d
o
task
s
au
to
m
atica
lly
o
n
c
e
th
ey
h
av
e
lear
n
t
h
o
w
to
h
a
n
d
le
d
ata
is
t
h
e
m
ain
ad
v
a
n
tag
e
o
f
ML
.
Alg
o
r
ith
m
s
in
ML
ar
e
cr
ea
ted
to
ass
is
t
co
m
p
u
ter
s
in
lear
n
in
g
f
r
o
m
th
eir
e
x
p
er
i
en
ce
s
b
y
tr
ain
in
g
th
em
t
o
g
en
er
ate
a
r
esu
lt
b
y
u
tili
zin
g
a
s
et
o
f
in
p
u
t
d
ata.
ML
alg
o
r
ith
m
s
ar
e
tr
ai
n
ed
o
n
d
atasets
an
d
s
u
b
s
eq
u
en
tly
p
r
o
v
id
e
o
u
tp
u
t
f
o
r
test
d
ata.
T
h
ese
m
eth
o
d
s
m
ak
e
u
s
e
o
f
co
m
p
u
tatio
n
al
tech
n
iq
u
es
to
tr
ai
n
th
e
m
o
d
el
an
d
e
x
tr
ac
t
in
f
o
r
m
atio
n
f
r
o
m
th
e
p
r
e
–
e
x
is
tin
g
d
ata.
As
th
e
n
u
m
b
e
r
o
f
s
am
p
les
in
cr
ea
s
es,
alg
o
r
ith
m
p
er
f
o
r
m
a
n
ce
im
p
r
o
v
es.
Sev
er
al
m
eth
o
d
s
an
d
s
tr
ateg
ies
ar
e
u
s
ed
in
th
is
iter
ativ
e
lear
n
in
g
p
r
o
ce
s
s
,
s
u
ch
as
r
ein
f
o
r
ce
m
en
t
lear
n
in
g
,
u
n
s
u
p
er
v
is
ed
lear
n
in
g
,
a
n
d
s
u
p
er
v
is
ed
lear
n
in
g
.
Alg
o
r
it
h
m
s
th
at
ar
e
tr
ain
ed
o
n
lab
eled
d
atasets
u
s
in
g
s
u
p
er
v
is
ed
lear
n
i
n
g
m
ap
i
n
p
u
t
d
ata
to
co
r
r
esp
o
n
d
in
g
o
u
tp
u
t
lab
els.
W
h
ile
r
ein
f
o
r
ce
m
en
t
le
ar
n
in
g
f
o
cu
s
es
o
n
d
ec
is
io
n
–
m
ak
in
g
th
r
o
u
g
h
in
te
r
ac
tin
g
with
an
e
n
v
ir
o
n
m
en
t
an
d
g
ettin
g
f
ee
d
b
ac
k
in
t
h
e
f
o
r
m
o
f
r
ewa
r
d
s
o
r
p
en
alties,
u
n
s
u
p
er
v
is
ed
lear
n
in
g
f
o
c
u
s
es
o
n
f
in
d
in
g
p
atte
r
n
s
an
d
s
tr
u
ctu
r
es
with
in
u
n
lab
eled
d
ata.
T
h
e
ef
f
icac
y
o
f
ML
is
co
n
tin
g
en
t
u
p
o
n
th
e
al
g
o
r
ith
m
s
'
ca
p
ac
ity
to
ex
t
r
ap
o
late
p
atter
n
s
an
d
co
r
r
elatio
n
s
f
r
o
m
tr
ain
in
g
d
ata
in
o
r
d
er
to
g
e
n
er
ate
p
r
ec
is
e
p
r
ed
ictio
n
s
o
r
ju
d
g
m
en
ts
o
n
n
o
v
el
d
ata.
Ad
d
itio
n
ally
,
m
ajo
r
d
ev
elo
p
m
e
n
ts
in
task
s
lik
e
i
m
ag
e
r
ec
o
g
n
itio
n
,
n
atu
r
al
lan
g
u
ag
e
p
r
o
ce
s
s
in
g
,
a
n
d
a
u
to
n
o
m
o
u
s
s
y
s
tem
s
h
av
e
b
ee
n
m
a
d
e
p
o
s
s
ib
le
b
y
ad
v
an
ce
s
in
ML
tec
h
n
iq
u
es,
s
u
ch
a
s
d
ee
p
lear
n
in
g
n
e
u
r
al
n
etwo
r
k
s
[
1
8
]
,
[
1
9
]
.
All
th
in
g
s
co
n
s
id
er
ed
,
ML
g
i
v
es
co
m
p
u
ter
s
th
e
ab
ilit
y
to
le
ar
n
an
d
ad
ap
t
o
n
th
ei
r
o
wn
,
wh
ich
ad
v
an
ce
s
au
to
m
atio
n
,
ef
f
icien
c
y
,
an
d
d
ec
is
io
n
–
m
ak
in
g
in
a
v
ar
iety
o
f
f
ield
s
a
n
d
ap
p
licatio
n
s
.
A
s
alg
o
r
ith
m
s
ad
v
an
ce
an
d
ch
an
g
e
th
r
o
u
g
h
o
u
t
tim
e,
t
h
er
e
is
s
till
a
lo
t
o
f
p
r
o
m
is
e
in
u
s
in
g
ML
to
tack
le
ch
allen
g
i
n
g
is
s
u
es
an
d
cr
ea
te
n
ew
p
o
s
s
ib
ilit
ies.
I
n
ML
,
a
s
tatis
tical
m
o
d
el
is
a
f
r
am
ewo
r
k
t
h
at
is
u
s
ed
t
o
d
ep
ict
th
e
c
o
n
n
ec
ti
o
n
b
etwe
en
th
e
g
o
al
v
ar
iab
l
e
an
d
th
e
in
p
u
t
d
ata
with
o
u
t
d
ir
ec
tly
s
tatin
g
a
m
ath
em
atica
l
ex
p
r
ess
io
n
.
As
an
al
ter
n
ativ
e,
s
tatis
tical
m
o
d
els
ca
p
tu
r
e
tr
e
n
d
s
an
d
r
el
atio
n
s
h
ip
s
f
o
u
n
d
in
th
e
d
ata,
en
ab
lin
g
th
e
c
r
ea
tio
n
o
f
f
o
r
ec
asts
o
r
co
n
clu
s
io
n
s
b
ased
o
n
f
r
esh
,
u
n
f
o
r
eseen
in
f
o
r
m
atio
n
.
Fu
n
d
a
m
en
tally
,
a
s
tatis
tical
m
o
d
el
o
u
tlin
es
a
s
er
ies
o
f
p
r
esu
m
p
tio
n
s
r
eg
ar
d
in
g
th
e
g
en
er
atio
n
o
f
d
a
ta
an
d
th
e
r
elatio
n
s
h
ip
s
b
etwe
en
v
ar
iab
les.
T
h
e
m
o
d
el
u
s
es
t
h
ese
p
r
esu
m
p
tio
n
s
to
in
f
o
r
m
its
lear
n
in
g
f
r
o
m
th
e
d
ata
an
d
to
h
elp
it
f
o
r
m
u
late
p
r
ed
ictio
n
s
an
d
co
n
c
lu
s
io
n
s
.
D
if
f
er
en
t
ty
p
es
o
f
d
ata
an
d
ac
tiv
ities
r
eq
u
ir
e
d
if
f
er
en
t
ty
p
es
o
f
s
tatis
tical
m
o
d
els,
wh
ich
ca
n
v
ar
y
f
r
o
m
b
asi
c
lin
ea
r
m
o
d
els
to
in
tr
icate
n
o
n
–
li
n
ea
r
m
o
d
els.
A
s
tatis
tica
l
m
o
d
el
u
s
es
v
ar
iab
le
s
o
r
ch
ar
ac
ter
is
tics
f
r
o
m
th
e
in
p
u
t
d
ata
to
p
r
ed
ict
th
e
tar
g
et
v
ar
iab
le.
B
y
esti
m
at
in
g
p
a
r
am
eter
s
th
at
m
o
s
t
ac
c
u
r
ately
ch
ar
ac
te
r
ize
th
e
lin
k
b
et
wee
n
attr
ib
u
tes
a
n
d
th
e
o
b
jectiv
e,
th
e
m
o
d
el
g
ain
s
k
n
o
wled
g
e
f
r
o
m
p
ast
d
ata.
Af
ter
b
ein
g
tr
ain
ed
,
th
e
m
o
d
el
m
ay
b
e
u
s
ed
to
f
r
esh
d
ata
to
f
o
r
ec
ast
o
u
tco
m
es
o
r
l
ea
r
n
m
o
r
e
a
b
o
u
t
th
e
u
n
d
e
r
ly
in
g
p
r
o
ce
s
s
es
th
at
p
r
o
d
u
ce
d
th
e
d
ata.
Sig
n
if
ican
tly
,
s
tatis
t
ical
m
o
d
els
o
f
f
er
m
etr
i
cs
o
f
u
n
ce
r
tain
t
y
in
ad
d
itio
n
to
p
r
e
d
ictio
n
s
.
B
y
p
u
ttin
g
a
n
u
m
b
e
r
o
n
th
e
u
n
ce
r
tain
ty
ar
o
u
n
d
f
o
r
ec
asts
,
th
ey
en
ab
le
u
s
er
s
to
ass
es
s
t
h
e
p
r
ed
ictab
ilit
y
o
f
th
e
m
o
d
e
l's
o
u
tp
u
t
an
d
b
ase
th
eir
ju
d
g
m
en
ts
o
n
th
e
d
eg
r
ee
o
f
tr
u
s
t in
th
e
p
r
ed
ictio
n
s
.
All
th
in
g
s
co
n
s
id
er
ed
,
s
tatis
tical
m
o
d
els ar
e
ef
f
ec
tiv
e
ML
to
o
ls
th
at
p
r
o
v
id
e
a
v
er
s
atile
f
r
am
ewo
r
k
f
o
r
i
d
en
tify
i
n
g
p
atter
n
s
in
d
ata,
g
en
e
r
atin
g
p
r
e
d
ictio
n
s
,
a
n
d
co
m
in
g
to
in
s
ig
h
tf
u
l
c
o
n
clu
s
io
n
s
.
T
h
ey
ar
e
ex
te
n
s
iv
ely
u
s
ed
to
g
ath
er
in
f
o
r
m
atio
n
a
n
d
g
u
id
e
d
ec
is
io
n
–
m
ak
in
g
p
r
o
ce
s
s
es in
a
v
ar
iety
o
f
in
d
u
s
tr
ies,
in
clu
d
in
g
as m
a
r
k
etin
g
,
f
in
a
n
ce
,
h
ea
lt
h
ca
r
e,
an
d
m
o
r
e.
2
.
1
.
Aut
o
re
g
re
s
s
iv
e
inte
g
ra
t
ed
m
o
v
ing
a
v
er
a
g
e
A
g
en
er
aliza
tio
n
o
f
a
n
a
u
to
r
eg
r
ess
iv
e
m
o
v
in
g
a
v
er
ag
e
m
o
d
el
is
th
e
au
to
r
e
g
r
ess
iv
e
in
teg
r
ate
d
m
o
v
in
g
av
er
ag
e
m
o
d
el.
T
o
g
et
a
d
ee
p
er
u
n
d
er
s
tan
d
in
g
o
f
t
h
e
d
ata
o
r
f
o
r
ec
ast
f
u
tu
r
e
s
er
ies
p
o
i
n
ts
,
th
ese
two
m
o
d
els
ar
e
f
itted
to
tim
e
s
er
ies
d
at
a.
I
f
s
tatis
tic
s
s
h
o
w
th
at
th
e
m
ea
n
f
u
n
ctio
n
is
n
o
n
–
s
tatio
n
ar
is
tic,
th
e
n
o
n
–
s
tatio
n
ar
ity
ca
n
b
e
elim
i
n
ated
f
r
o
m
t
h
e
m
ea
n
f
u
n
ctio
n
b
y
i
m
p
lem
en
tin
g
a
n
in
itial
d
if
f
er
e
n
cin
g
s
tep
o
n
ce
o
r
m
o
r
e
tim
es.
AR
I
MA
m
o
d
els
ar
e
em
p
lo
y
ed
in
th
ese
cir
cu
m
s
tan
ce
s
.
W
h
en
th
e
s
ea
s
o
n
al
co
m
p
o
n
e
n
t
ap
p
ea
r
s
in
a
tim
e
s
er
ies,
s
ea
s
o
n
al
–
d
if
f
er
en
cin
g
ca
n
b
e
em
p
lo
y
ed
to
elim
in
ate
it.
T
h
e
W
o
ld
'
s
d
ec
o
m
p
o
s
itio
n
th
eo
r
em
,
f
o
r
ex
am
p
le,
ass
er
ts
th
at
th
e
AR
I
MA
m
o
d
el
is
th
eo
r
etica
lly
s
u
f
f
icien
t
to
ex
p
lain
a
r
eg
u
lar
wi
d
e
–
s
en
s
e
s
tatio
n
ar
y
tim
e
s
er
ies,
wh
ich
m
o
tiv
ates u
s
t
o
m
ak
e
s
tatio
n
ar
y
a
n
o
n
–
s
tatio
n
ar
y
tim
e
s
er
ies [
2
0
]
-
[
2
5
]
.
T
h
e
AR
I
MA
m
o
d
el
ca
n
b
e
u
n
d
er
s
to
o
d
b
y
o
u
tlin
in
g
all
its
co
m
p
o
n
e
n
ts
as f
o
llo
ws:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
14
,
No
.
3
,
Dec
em
b
er
20
25
:
830
-
8
3
6
832
a)
Au
to
r
eg
r
ess
io
n
:
t
h
is
m
eth
o
d
e
x
p
lain
s
a
m
o
d
el
th
at
d
is
p
lay
s
th
e
r
eg
r
ess
io
n
o
f
a
v
ar
iab
le
v
e
r
s
u
s
its
lag
o
r
p
r
ev
io
u
s
v
al
u
es.
b)
I
n
teg
r
ated
:
it
r
ep
r
esen
ts
th
e
p
r
o
ce
s
s
o
f
tak
in
g
r
aw
o
b
s
er
v
at
io
n
s
an
d
s
tab
ilizin
g
th
e
tim
e
s
er
ies
(
i.e
.
,
b
y
r
ep
lacin
g
th
e
d
ata
v
alu
es with
th
e
p
r
io
r
v
alu
es).
c)
Mo
v
in
g
av
er
a
g
e:
th
is
tech
n
iq
u
e
co
m
p
r
is
es th
e
co
r
r
elatio
n
b
etwe
en
th
e
o
b
s
er
v
atio
n
a
n
d
th
e
r
esid
u
al
er
r
o
r
o
f
th
e
m
o
v
in
g
av
er
a
g
e
m
o
d
el
u
s
ed
to
th
e
d
ela
y
ed
o
b
s
er
v
atio
n
.
I
n
co
n
clu
s
io
n
,
an
AR
I
MA
m
o
d
el
is
r
ep
r
esen
ted
b
y
th
e
n
o
tatio
n
AR
I
MA
(
p
,
d
,
q
)
,
wh
e
r
e
p
is
th
e
au
to
r
eg
r
ess
iv
e
co
m
p
o
n
e
n
t'
s
o
r
d
er
.
T
h
e
am
o
u
n
t
o
f
d
if
f
er
en
c
in
g
n
ee
d
e
d
to
r
ea
ch
s
ta
tio
n
ar
i
ty
is
d
en
o
ted
b
y
d
.
T
h
e
m
o
v
i
n
g
av
e
r
ag
e
c
o
m
p
o
n
e
n
t'
s
o
r
d
er
is
d
en
o
ted
b
y
q
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
r
esu
lts
ar
e
d
is
cu
s
s
ed
in
th
e
f
o
llo
win
g
s
ec
tio
n
.
T
a
b
le
s
1
an
d
2
s
h
o
ws
th
e
r
aw
d
at
a
an
d
th
e
r
esam
p
led
d
ata
r
esp
ec
tiv
ely
.
Da
ta
ar
e
f
e
tch
ed
f
r
o
m
th
e
K
a
g
g
le
(
d
a
ta
s
ci
en
c
e
lib
r
ar
y
)
an
d
th
e
d
a
ta
ar
e
in
th
e
f
o
r
m
o
f
n
at
io
n
a
l
d
em
an
d
,
d
a
t
e,
tr
a
n
s
m
i
s
s
i
o
n
lo
ad
an
d
th
e
d
ata
c
o
n
t
ain
s
o
f
f
i
v
e
y
ea
r
s
lo
a
d
.
T
ab
l
e
1
p
r
o
v
id
e
s
a
s
am
p
le
s
e
t
o
f
in
p
u
t
r
a
w
d
at
a
.
Fo
r
an
aly
s
is
p
u
r
p
o
s
e,
th
e
d
ate
an
d
n
atio
n
al
d
em
a
n
d
ar
e
co
n
s
id
er
ed
s
in
ce
th
e
d
ata
is
to
b
e
co
n
v
er
ted
in
to
a
tim
e
s
er
ies
f
o
r
to
b
e
f
o
r
ec
asted
.
T
h
e
d
ata
ar
e
in
h
o
u
r
l
y
o
r
d
er
a
n
d
th
e
d
ata
h
as
to
b
e
r
eo
r
d
er
e
d
in
wee
k
ly
o
r
d
er
a
n
d
2
7
3
wee
k
l
y
d
ata
ar
e
o
b
tain
ed
a
f
ter
r
esam
p
le.
A
s
a
m
p
le
s
et
o
f
r
esam
p
led
d
ata
is
g
iv
en
in
T
a
b
le
2
.
T
ab
l
e
1
.
D
at
a
s
am
p
le
D
a
t
e
a
n
d
t
i
me
N
a
t
_
d
e
ma
n
d
T2
M
t
o
c
Q
V
2
M
t
o
c
TQ
Lt
o
c
W
2
M
t
o
c
T2
M
sa
n
1
/
3
/
2
0
1
5
1
:
0
0
9
7
0
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3
4
5
2
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8
6
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2
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8
2
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6
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1
8
9
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T
ab
le
2
.
R
esam
p
led
d
ata
D
a
t
e
(
m
m
\
dd
\
y
y
)
N
a
t
i
o
n
a
l
d
e
ma
n
d
1
/
1
1
/
2
0
1
5
1
8
1
9
1
9
.
6
2
2
4
1
/
1
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2
0
1
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1
8
8
0
8
2
.
3
1
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2
1
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2
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/
2
0
1
5
1
7
9
4
4
8
.
7
1
8
4
2
/
1
/
2
0
1
5
1
8
4
3
9
3
.
4
2
5
6
2
/
8
/
2
0
1
5
1
8
7
2
9
0
.
1
8
4
6
2
/
1
5
/
2
0
1
5
1
8
0
4
6
7
.
2
4
5
8
2
/
2
2
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2
0
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1
7
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4
9
7
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6
4
2
7
3
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9
1
4
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1
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3
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1
1
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6
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5
8
3
/
1
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5
1
9
3
2
8
6
.
7
5
8
3
/
2
2
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2
0
1
5
1
8
4
9
5
0
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9
6
5
3
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2
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0
1
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1
8
6
5
0
3
.
6
5
3
9
4
/
5
/
2
0
1
5
1
7
6
8
2
2
.
2
5
5
9
4
/
1
2
/
2
0
1
5
1
8
8
6
5
4
.
9
9
0
9
3
.
1
.
T
ra
ini
ng
da
t
a
s
et
T
h
e
in
f
o
r
m
atio
n
th
at
th
e
co
m
p
u
ter
u
s
es
to
lea
r
n
h
o
w
to
p
r
o
ce
s
s
in
f
o
r
m
atio
n
is
ca
lled
th
e
tr
ain
in
g
d
ataset.
ML
m
ak
es
u
s
e
o
f
alg
o
r
ith
m
s
to
r
ep
licate
th
e
way
s
i
n
wh
ich
th
e
h
u
m
an
b
r
ain
p
r
o
c
ess
es
v
ar
io
u
s
in
p
u
ts
an
d
ev
alu
ates
th
em
to
g
e
n
er
at
e
s
y
n
ap
tic
ac
tiv
atio
n
s
in
in
d
iv
id
u
al
n
eu
r
o
n
s
.
A
lar
g
e
p
o
r
tio
n
o
f
th
is
p
r
o
ce
s
s
is
r
ep
licated
b
y
ar
tific
ial
n
eu
r
o
n
s
u
s
in
g
s
o
f
twar
e,
s
u
ch
as
ML
an
d
n
eu
r
al
n
etwo
r
k
ap
p
licatio
n
s
th
at
o
f
f
er
in
cr
ed
ib
ly
ac
c
u
r
ate
r
ep
r
esen
tatio
n
s
o
f
h
o
w
h
u
m
an
b
r
ain
p
r
o
ce
s
s
es
an
d
o
p
er
ate.
Her
e,
d
ata
s
ets
ar
e
tr
ain
ed
f
o
r
f
iv
e
y
ea
r
s
(
i.e
,
th
e
tr
ain
in
g
d
at
a
is
2
7
3
tr
ain
in
g
wee
k
s
)
.
3
.
2
.
T
est
da
t
a
s
et
Nex
t step
is
to
test
th
e
m
o
d
el
u
s
in
g
th
e
test
d
ataset
o
n
ce
it h
as b
ee
n
tr
ain
ed
u
s
in
g
th
e
tr
ain
i
n
g
d
ataset.
T
h
is
d
ataset
as
s
ess
e
s
th
e
m
o
d
el'
s
p
er
f
o
r
m
an
ce
an
d
v
e
r
if
i
es
th
at
it
ca
n
ef
f
ec
tiv
ely
g
en
er
ali
ze
to
n
ew
o
r
u
n
test
ed
d
atasets
.
T
h
e
lo
ad
d
ata
f
o
r
ec
ast o
cc
u
r
s
in
th
e
in
ter
v
al
b
etwe
en
th
e
d
atetim
e
an
d
th
e
wee
k
ly
lo
ad
.
T
h
e
p
icto
r
al
r
ep
r
esen
tatio
n
o
f
p
r
ed
ictio
n
with
o
u
t
ex
o
g
e
n
o
u
s
v
ar
i
ab
les
in
th
e
AR
I
MA
m
o
d
el
i
s
g
iv
en
in
Fig
u
r
e
1
.
T
h
e
g
r
ap
h
s
h
o
w
th
e
p
r
ed
icti
o
n
o
f
n
ex
t
f
iv
e
y
ea
r
an
d
th
e
p
r
e
d
icted
d
ata
ar
e
n
o
t
ac
cu
r
ate
d
u
e
to
lack
o
f
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
E
lectric lo
a
d
fo
r
ec
a
s
tin
g
u
s
in
g
A
R
I
MA
mo
d
el
fo
r
time
s
eries
d
a
ta
(
B
a
l
a
s
u
b
r
a
ma
n
ia
n
B
elsh
a
n
th
)
833
s
u
f
f
icien
t
d
ata.
T
o
o
v
er
c
o
m
e
i
t
is
n
ec
ess
ar
y
to
in
v
o
lv
e
th
e
e
x
o
g
en
o
u
s
v
a
r
iab
le
lik
e
wee
k
ly
tr
an
s
m
is
s
io
n
lo
ad
d
ata,
y
ea
r
ly
l
o
ad
d
ata
to
g
et
ac
cu
r
ate
r
esu
lts
.
Fig
u
r
e
1
.
T
h
e
p
icto
r
al
r
ep
r
ese
n
tatio
n
o
f
t
r
ain
in
g
d
ata
an
d
p
r
ed
ictio
n
with
o
u
t e
x
o
g
e
n
o
u
s
v
a
r
iab
les
3
.
3
.
E
x
o
g
eno
us
v
a
ria
bles
A
v
ar
iab
le
in
a
m
ath
em
atica
l
o
r
s
tatis
tical
m
o
d
el
th
at
is
in
d
ep
en
d
en
t
o
f
o
th
e
r
v
ar
iab
les
is
ca
lled
an
ex
o
g
en
o
u
s
v
a
r
iab
le.
I
t
is
o
f
te
n
r
ef
er
r
ed
t
o
as
an
in
d
ep
e
n
d
e
n
t
o
r
p
r
ed
icto
r
v
a
r
iab
le
s
in
ce
it
is
u
s
ed
to
f
o
r
ec
ast
th
e
v
alu
es
o
f
o
t
h
er
v
ar
iab
les
i
n
th
e
m
o
d
el.
E
x
o
g
en
o
u
s
v
ar
ia
b
les,
wh
ich
ar
e
f
r
e
q
u
en
tly
em
p
lo
y
ed
to
d
escr
ib
e
th
e
b
eh
av
i
o
r
o
f
th
e
d
e
p
en
d
e
n
t
v
ar
iab
le,
ar
e
g
en
er
ally
s
ee
n
as b
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o
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ts
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e
th
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o
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el'
s
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n
tr
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3
.
4
.
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e
a
t
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f
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E
x
o
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s
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les
ar
e
ad
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ed
to
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e
m
o
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el
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ak
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o
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th
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les
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ata
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e
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led
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k
ly
to
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atch
with
th
e
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o
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el.
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n
o
th
er
s
h
an
d
s
th
ese
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e
lik
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s
u
f
f
icien
t
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ata
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o
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s
u
f
f
icien
t
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o
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el
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et
a
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r
p
r
e
d
ictio
n
.
T
h
e
tr
an
s
m
is
s
io
n
s
tatio
n
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ad
(
T
2
M)
an
d
s
u
b
s
tatio
n
lo
ad
(
QV2
M)
ar
e
as
ex
o
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o
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s
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ar
a
m
eter
s
.
3
.
5
.
P
re
dict
io
n
a
f
t
er
a
dd
ing
ex
o
g
eno
us
v
a
ria
ble da
t
a
s
et
T
o
o
v
er
co
m
e
th
e
d
ea
wb
ac
k
s
h
o
wn
in
Fig
u
r
e
1
,
th
e
ex
o
g
en
o
u
s
v
a
r
iab
le
d
ata
s
et
is
ad
d
ed
to
th
e
AR
I
MA
m
o
d
el.
B
y
d
o
in
g
s
o
t
h
e
o
u
t
p
u
t
o
b
tain
ed
co
in
cid
es
with
th
e
tr
ain
in
g
d
ata
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ich
is
s
h
o
wn
in
b
lu
e
a
n
d
th
e
o
u
tp
u
t
o
b
tain
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o
r
th
e
test
d
ata
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g
iv
en
in
o
r
an
g
e.
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h
e
p
icto
r
al
r
ep
r
esen
tatio
n
o
f
p
r
ed
ictio
n
with
ex
o
g
en
o
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s
v
ar
ia
b
les in
clu
d
ed
in
th
e
AR
I
MA
m
o
d
el
is
g
iv
en
in
Fig
u
r
e
2
.
N
o
t
e
:
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h
e
l
i
n
e
s
a
r
e
i
n
b
l
u
e
(
t
r
a
i
n
d
a
t
a
)
i
s
c
o
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n
c
i
d
e
w
i
t
h
l
i
n
e
s a
r
e
i
n
o
r
a
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g
e
(
t
e
st
d
a
t
a
)
Fig
u
r
e
2
.
T
h
e
p
icto
r
al
r
ep
r
ese
n
tatio
n
o
f
t
r
ain
in
g
d
ata
an
d
p
r
ed
ictio
n
with
ex
o
g
en
o
u
s
v
ar
ia
b
les
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
14
,
No
.
3
,
Dec
em
b
er
20
25
:
830
-
8
3
6
834
T
h
e
o
u
tp
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t
f
r
o
m
th
e
tr
ain
in
g
d
ataset
ar
e
m
atch
in
g
with
th
e
test
in
g
d
ataset
wh
ich
i
s
lo
ad
p
r
ed
ictio
n
.
I
n
o
th
e
r
s
h
an
d
,
th
e
l
o
ad
d
ata
f
r
o
m
2
0
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0
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ar
e
r
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le
o
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l
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ata
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h
e
r
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lt
f
r
o
m
th
is
g
r
ap
h
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n
e
d
an
d
r
esem
b
les with
th
e
p
r
ec
io
u
s
y
ea
r
.
4.
CO
NCLU
SI
O
N
W
h
en
u
tili
zin
g
tim
e
s
er
ies
d
ata
f
o
r
s
h
o
r
t
–
ter
m
elec
tr
ic
lo
ad
p
r
o
g
n
o
s
tic,
th
e
AR
I
MA
m
o
d
el
h
as
s
h
o
wn
to
b
e
a
u
s
ef
u
l
to
o
l.
I
t
p
r
o
v
id
es
a
d
ep
e
n
d
ab
le
tec
h
n
i
q
u
e
f
o
r
p
r
o
jectin
g
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wer
d
em
an
d
o
v
er
b
r
ief
tim
e
h
o
r
izo
n
s
b
y
r
ec
o
g
n
izin
g
an
d
f
o
r
ec
asti
n
g
in
tr
icate
p
atter
n
s
in
th
e
d
ata.
T
h
e
AR
I
MA
m
o
d
el
g
iv
es
u
tili
ties
an
d
en
er
g
y
s
u
p
p
lier
s
cr
itical
in
s
ig
h
ts
f
o
r
co
n
tr
o
llin
g
g
r
id
s
tab
ilit
y
,
o
p
tim
izin
g
r
eso
u
r
ce
allo
ca
t
io
n
,
an
d
im
p
r
o
v
in
g
o
p
er
atio
n
al
e
f
f
icien
cy
b
y
u
tili
zin
g
h
is
to
r
ical
lo
ad
d
ata
a
n
d
ad
d
in
g
s
ea
s
o
n
ality
,
tr
en
d
s
,
a
n
d
cy
clica
l
p
atter
n
s
.
Alth
o
u
g
h
a
d
d
itio
n
al
in
v
esti
g
atio
n
an
d
e
n
h
an
ce
m
e
n
t
m
ay
b
e
r
eq
u
ir
ed
to
tack
le
ce
r
tain
i
s
s
u
es
an
d
en
h
an
ce
p
r
ec
is
io
n
,
th
e
AR
I
MA
m
o
d
el
r
em
ain
s
a
v
iab
le
s
tr
ateg
y
f
o
r
f
u
lf
illi
n
g
th
e
d
y
n
am
ic
r
e
q
u
ir
em
en
ts
o
f
th
e
co
n
tem
p
o
r
ar
y
e
n
er
g
y
e
n
v
ir
o
n
m
en
t.
T
o
s
u
m
u
p
,
th
e
AR
I
MA
m
o
d
el
h
as
s
h
o
wn
to
b
e
a
u
s
ef
u
l
to
o
l
f
o
r
s
h
o
r
t
–
ter
m
elec
tr
ic
lo
ad
f
o
r
ec
asti
n
g
s
in
ce
it
p
r
o
v
id
es
p
r
ec
is
e
esti
m
ates
b
ased
o
n
ti
m
e
s
er
ies
d
ata.
AR
I
MA
s
h
o
ws
its
ef
f
ec
tiv
en
ess
in
m
o
d
elin
g
a
n
d
p
r
ed
ictin
g
p
o
wer
d
em
a
n
d
b
y
ca
p
tu
r
in
g
b
o
t
h
tr
en
d
an
d
s
ea
s
o
n
al
co
m
p
o
n
en
ts
.
T
h
is
h
elp
s
with
r
eso
u
r
ce
allo
ca
tio
n
,
g
r
id
m
a
n
ag
em
e
n
t,
an
d
d
ec
is
io
n
–
m
ak
in
g
p
r
o
ce
s
s
es
with
in
th
e
en
er
g
y
s
ec
to
r
.
Fu
r
th
er
im
p
r
o
v
em
e
n
t
s
an
d
tin
k
er
in
g
with
AR
I
MA
an
d
o
th
er
f
o
r
ec
asti
n
g
a
p
p
r
o
ac
h
es
p
r
o
m
is
e
to
co
n
s
is
ten
tly
in
cr
ea
s
e
th
e
ac
cu
r
ac
y
an
d
d
ep
en
d
ab
ilit
y
o
f
s
h
o
r
t
–
ter
m
elec
tr
ic
lo
ad
p
r
o
ject
io
n
s
as
tech
n
o
lo
g
y
an
d
m
eth
o
d
o
lo
g
y
ad
v
a
n
ce
,
en
ab
lin
g
a
m
o
r
e
r
o
b
u
s
t
an
d
s
u
s
tain
ab
le
en
er
g
y
in
f
r
astru
ctu
r
e.
I
n
co
n
clu
s
io
n
,
th
er
e
h
av
e
b
ee
n
en
c
o
u
r
a
g
in
g
o
u
tco
m
es
wh
en
s
h
o
r
t
–
ter
m
elec
tr
ic
lo
ad
f
o
r
ec
asti
n
g
h
as
b
ee
n
d
o
n
e
u
s
in
g
th
e
AR
I
MA
m
o
d
el.
T
h
r
o
u
g
h
th
e
u
s
e
o
f
ti
m
e
s
er
ies
d
ata,
AR
I
MA
is
ab
le
to
ac
cu
r
ately
an
ticip
ate
p
o
wer
co
n
s
u
m
p
tio
n
b
y
ca
p
tu
r
in
g
t
r
e
n
d
s
an
d
s
ea
s
o
n
al
p
atter
n
s
.
I
n
th
e
en
er
g
y
s
ec
to
r
,
th
is
s
tr
ateg
y
h
elp
s
with
r
es
o
u
r
ce
m
an
a
g
em
en
t
an
d
d
ec
is
io
n
–
m
ak
in
g
.
AR
I
M
A
an
d
r
elate
d
f
o
r
ec
asti
n
g
a
p
p
r
o
ac
h
es
ar
e
well
–
p
o
s
itio
n
e
d
to
s
ig
n
if
ica
n
tly
im
p
r
o
v
e
th
e
ac
cu
r
ac
y
an
d
d
ep
en
d
a
b
ilit
y
o
f
s
h
o
r
t
–
ter
m
elec
tr
ic
lo
ad
p
r
o
jectio
n
s
as
tech
n
o
lo
g
y
an
d
m
eth
o
d
o
l
o
g
y
co
n
tin
u
e
to
e
v
o
l
v
e,
ass
is
tin
g
in
th
e
cr
ea
tio
n
o
f
an
en
er
g
y
i
n
f
r
astru
ctu
r
e
th
at
is
m
o
r
e
r
o
b
u
s
t
an
d
ef
f
icien
t.
F
UNDING
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DATA AV
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Data
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tu
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
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8
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835
RE
F
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R
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NC
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[
1
]
E.
C
h
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a
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[
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4
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5
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[
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7
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[
8
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.
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[
9
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Li
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a
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1
2
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D
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S
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,
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1
3
]
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.
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,
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[
1
4
]
E.
El
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t
t
a
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,
A
.
M
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d
,
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d
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M
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1
5
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W
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,
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.
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