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Vo
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20
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p
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5
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5
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I
SS
N:
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-
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7
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,
DOI
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1
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v
15
i
6
.
pp
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t
c
h
a
l
len
g
e
s
in
p
re
d
ictin
g
e
lec
tri
c
it
y
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a
d
.
To
a
d
d
re
ss
th
is,
a
c
o
m
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a
ti
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ig
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ted
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l
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F
M
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b
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se
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i
n
d
i
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a
l
p
re
d
ictio
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m
o
d
e
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r
o
p
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se
d
.
Th
e
a
rti
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c
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lo
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a
l
g
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rit
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m
is
u
se
d
to
o
p
ti
m
ize
t
h
e
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ig
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ted
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o
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n
ts.
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e
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te
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m
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a
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e
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n
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.
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u
sin
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m
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tag
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AP
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a
n
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t
m
e
a
n
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u
a
re
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rro
r
(RM
S
E).
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e
e
x
p
e
rime
n
tal
re
su
lt
s
in
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ica
te
th
a
t
t
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sin
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m
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d
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m
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imp
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d
a
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ra
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y
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d
b
e
tt
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c
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se
a
so
n
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l
v
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riatio
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s
in
e
lec
tri
c
it
y
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o
a
d
.
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e
ABC
a
l
g
o
rit
h
m
h
e
lp
s
in
fi
n
d
i
n
g
t
h
e
o
p
ti
m
a
l
c
o
m
b
in
a
ti
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n
s
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m
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v
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ly
to
d
iffere
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t
f
o
re
c
a
stin
g
sc
e
n
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rio
s.
K
ey
w
o
r
d
s
:
Ar
tific
ial
b
ee
co
lo
n
y
Au
to
r
eg
r
ess
iv
e
in
teg
r
ated
m
o
v
in
g
av
er
ag
e
Gr
ey
m
o
d
el
L
o
ad
f
o
r
ec
asti
n
g
Su
p
p
o
r
t
v
ec
to
r
r
e
g
r
ess
io
n
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
An
i Sh
ab
r
i
Dep
ar
tm
en
t o
f
Ma
th
em
atica
l
Scien
ce
,
Facu
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f
Scien
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,
U
n
iv
er
s
ity
T
ek
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lo
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alay
s
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8
1
3
0
0
J
o
h
o
r
,
Ma
lay
s
ia
E
m
ail: a
n
i@
u
tm
.
m
y
1.
I
NT
RO
D
UCT
I
O
N
Acc
u
r
ate
elec
tr
ical
lo
ad
f
o
r
ec
asti
n
g
is
v
ital
f
o
r
th
e
en
er
g
y
s
ec
to
r
,
f
ac
ilit
atin
g
ef
f
icien
t
p
lan
n
in
g
an
d
o
p
er
atio
n
o
f
p
o
wer
s
y
s
tem
s
.
R
eliab
le
lo
ad
p
r
ed
ictio
n
s
e
n
ab
le
o
p
er
ato
r
s
to
m
an
a
g
e
e
n
er
g
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s
to
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ag
e
a
n
d
alter
n
ativ
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s
o
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ce
s
m
o
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e
ef
f
ec
tiv
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,
en
s
u
r
in
g
a
b
alan
ce
d
s
u
p
p
ly
.
T
h
is
also
en
h
an
ce
s
th
e
o
v
er
all
r
eliab
ilit
y
o
f
th
e
elec
tr
ical
g
r
id
b
y
allo
win
g
f
o
r
p
r
o
ac
tiv
e
id
en
tific
atio
n
an
d
r
eso
lu
tio
n
o
f
p
o
te
n
tial
is
s
u
e
s
s
u
ch
as
o
v
er
lo
ad
s
o
r
s
u
p
p
ly
s
h
o
r
tag
es
[
1
]
.
As
s
y
s
tem
s
ev
o
lv
e
r
a
p
id
ly
an
d
ar
e
in
f
lu
e
n
ce
d
b
y
in
cr
ea
s
in
g
ly
co
m
p
lex
f
ac
to
r
s
,
ac
h
iev
in
g
ac
c
u
r
ate
f
o
r
ec
asts
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o
m
es
m
o
r
e
ch
allen
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in
g
,
p
ar
ticu
lar
ly
d
u
e
to
s
ea
s
o
n
ality
an
d
u
n
ce
r
tain
ty
.
Seaso
n
al
d
ata
o
f
ten
d
is
p
lay
s
im
ilar
ities
b
etwe
en
d
if
f
er
e
n
t
cy
cle
p
er
io
d
s
b
u
t
also
e
x
h
i
b
it
f
lu
ctu
atio
n
s
an
d
r
an
d
o
m
n
ess
,
co
m
p
licatin
g
p
r
e
cise p
r
ed
ictio
n
s
.
C
u
r
r
en
tly
,
m
eth
o
d
s
f
o
r
p
r
e
d
i
ctin
g
s
ea
s
o
n
al
elec
tr
icity
tim
e
s
er
ies
in
th
e
liter
atu
r
e
ca
n
b
e
b
r
o
ad
ly
class
if
ied
in
to
th
r
ee
ca
te
g
o
r
i
es:
s
tatis
tical
ec
o
n
o
m
etr
ic
m
o
d
els,
ar
tific
ial
in
tellig
en
ce
m
o
d
els,
an
d
g
r
ey
m
o
d
els.
T
h
e
au
to
r
eg
r
ess
iv
e
in
teg
r
ated
m
o
v
in
g
av
er
ag
e
(
AR
I
MA
)
m
o
d
el,
a
k
ey
s
tatis
tica
l
e
co
n
o
m
etr
ic
to
o
l,
is
ex
ten
s
iv
ely
u
tili
ze
d
f
o
r
s
im
u
latin
g
an
d
f
o
r
ec
asti
n
g
s
ea
s
o
n
al
elec
tr
icity
tim
e
s
er
ies.
Kn
o
wn
f
o
r
its
r
o
b
u
s
t
f
o
r
ec
asti
n
g
ca
p
ab
ilit
ies,
th
e
AR
I
MA
m
o
d
el
d
em
o
n
s
tr
ates
h
ig
h
p
r
ec
is
io
n
in
p
r
ed
ictin
g
ele
ctr
icity
tim
e
s
er
ies
[
2
]
,
[
3
]
.
I
t
aid
s
in
u
n
d
er
s
tan
d
in
g
th
e
d
ata
d
y
n
am
ics
wi
th
in
a
s
p
ec
if
ic
ap
p
licatio
n
[
2
]
.
W
h
ile
AR
I
MA
ef
f
ec
tiv
ely
m
o
d
els
lin
ea
r
p
atter
n
s
in
tim
e
s
er
ies,
it
f
alls
s
h
o
r
t
in
ca
p
tu
r
in
g
n
o
n
lin
ea
r
p
atter
n
s
[
3
]
.
R
eg
r
ess
io
n
m
o
d
els
ar
e
also
co
m
m
o
n
l
y
em
p
lo
y
ed
in
tim
e
s
er
ies
p
r
ed
i
ctio
n
,
p
ar
ticu
lar
ly
f
o
r
s
er
ies
with
clea
r
tr
en
d
s
.
Ho
wev
er
,
tr
ad
itio
n
al
r
eg
r
ess
io
n
m
o
d
els
h
av
e
lim
itatio
n
s
,
s
u
ch
as
f
ewe
r
v
ar
iab
le
p
ar
am
eter
s
an
d
d
if
f
icu
lty
ad
ap
tin
g
to
tim
e
s
er
ies p
r
ed
ict
io
n
[
2
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
I
mp
r
o
vin
g
elec
tr
ica
l lo
a
d
fo
r
ec
a
s
tin
g
b
y
in
teg
r
a
tin
g
a
w
eig
h
ted
…
(
A
n
i
S
h
a
b
r
i
)
5855
Ar
tific
ial
in
tellig
en
ce
p
r
ed
ictio
n
m
o
d
els
p
r
im
a
r
ily
f
o
c
u
s
o
n
ar
tific
ial
n
eu
r
al
n
etwo
r
k
s
(
A
NNs)
an
d
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
es
(
SVMs).
R
ec
en
tly
,
ANNs
h
av
e
b
ee
n
wid
ely
ad
o
p
te
d
in
elec
tr
icity
f
o
r
ec
asti
n
g
[
2
]
.
ANNs h
av
e
a
lo
n
g
h
is
to
r
y
in
p
r
ed
ictio
n
an
d
h
a
v
e
m
ad
e
s
ig
n
i
f
ican
t c
o
n
tr
ib
u
tio
n
s
to
f
o
r
ec
asti
n
g
,
p
ar
ticu
lar
ly
in
id
en
tify
in
g
n
o
n
lin
ea
r
r
elatio
n
s
h
ip
s
b
etwe
en
in
p
u
ts
an
d
o
u
t
p
u
ts
,
ev
en
wh
en
th
er
e
is
in
s
u
f
f
icien
t
in
f
o
r
m
atio
n
ab
o
u
t
th
eir
r
elatio
n
s
h
ip
[
3
]
.
A
NNs
ar
e
f
av
o
r
ed
f
o
r
f
o
r
ec
asti
n
g
co
m
p
lex
n
o
n
lin
ea
r
s
y
s
tem
s
an
d
ca
n
r
ea
lize
an
y
co
m
p
lex
n
o
n
lin
ea
r
m
a
p
p
in
g
f
u
n
ctio
n
,
as m
ath
em
atica
lly
p
r
o
v
en
[
4
]
.
Ho
wev
er
,
ANNs a
r
e
p
r
o
n
e
to
f
allin
g
in
to
lo
ca
l m
in
im
a
an
d
o
f
ten
ex
h
ib
it
o
v
er
f
itti
n
g
[
5
]
.
Su
p
p
o
r
t
v
ec
to
r
r
eg
r
ess
io
n
(
SVR
)
h
as
g
ain
ed
co
n
s
id
er
ab
le
atten
tio
n
in
th
e
r
ea
lm
o
f
elec
tr
icity
lo
ad
f
o
r
ec
asti
n
g
d
u
e
to
its
s
o
lid
th
e
o
r
etica
l a
n
d
m
ath
em
atica
l u
n
d
er
p
in
n
in
g
s
.
SVR
p
er
f
o
r
m
s
r
o
b
u
s
t,
n
o
is
e
-
r
esis
tan
t,
an
d
n
o
n
lin
ea
r
r
e
g
r
ess
io
n
b
as
ed
o
n
th
e
p
r
in
ci
p
le
o
f
s
tr
u
c
tu
r
al
er
r
o
r
m
in
im
izatio
n
[
6
]
.
I
t
co
n
s
tr
u
cts
th
e
r
eg
r
ess
io
n
m
o
d
el
u
s
in
g
th
e
tr
ain
in
g
d
ataset
an
d
th
en
p
r
ed
icts
o
u
tco
m
es
f
r
o
m
th
e
te
s
t
d
ataset.
SVR
’
s
g
en
er
aliza
tio
n
ca
p
a
b
ilit
y
s
u
r
p
ass
es
th
at
o
f
n
eu
r
al
n
etwo
r
k
s
,
an
d
th
e
alg
o
r
ith
m
e
n
s
u
r
es
g
lo
b
al
o
p
tim
ality
[
7
]
.
Ad
d
itio
n
ally
,
v
ar
i
o
u
s
o
p
tim
iz
atio
n
tech
n
iq
u
es
ar
e
o
f
ten
e
m
p
lo
y
ed
t
o
en
h
a
n
ce
SVR
le
ar
n
in
g
.
Desp
ite
its
ab
ilit
y
to
p
r
o
d
u
ce
h
ig
h
ly
ac
c
u
r
ate
r
esu
lts
,
SVR
h
as
ce
r
tai
n
lim
itatio
n
s
.
Fo
r
in
s
tan
ce
,
s
elec
tin
g
n
u
m
er
o
u
s
p
ar
am
eter
s
th
r
o
u
g
h
t
r
ial
an
d
er
r
o
r
ca
n
b
e
c
h
allen
g
in
g
an
d
r
eq
u
ir
es
co
m
p
lex
ca
lcu
latio
n
s
to
ac
h
iev
e
o
p
tim
al
f
o
r
ec
asti
n
g
ac
cu
r
ac
y
[
8
]
.
Gr
ey
s
y
s
tem
th
eo
r
y
o
f
f
er
s
a
r
eliab
le
r
esear
ch
m
eth
o
d
f
o
r
s
i
tu
atio
n
s
with
lim
ited
d
ata.
Gr
ey
m
o
d
els
h
av
e
b
ee
n
e
f
f
ec
tiv
ely
a
p
p
lied
in
v
ar
io
u
s
f
ield
s
,
in
clu
d
in
g
n
atu
r
al
g
as,
elec
tr
icity
,
n
u
cle
ar
en
er
g
y
,
o
il,
an
d
o
v
er
all
en
er
g
y
c
o
n
s
u
m
p
tio
n
[
9
]
.
Ho
wev
er
,
th
ese
ap
p
licatio
n
s
ty
p
ically
in
v
o
l
v
e
an
n
u
al
ti
m
e
s
er
ies
with
an
u
p
war
d
tr
en
d
an
d
ar
e
less
f
r
eq
u
en
tly
u
s
ed
f
o
r
m
o
n
th
ly
o
r
q
u
ar
ter
ly
s
ea
s
o
n
al
d
ata
ch
ar
ac
te
r
ized
b
y
p
er
i
o
d
icity
.
T
o
ad
d
r
ess
th
ese
lim
itatio
n
s
,
W
an
g
[
1
0
]
p
r
o
p
o
s
ed
a
s
ea
s
o
n
al
g
r
ey
m
o
d
el
(
SGM
(
1
,
1
)
)
th
at
u
tili
ze
s
ac
cu
m
u
latio
n
o
p
e
r
ato
r
s
g
e
n
e
r
ated
b
y
s
ea
s
o
n
al
f
ac
to
r
s
t
o
f
o
r
ec
ast
elec
tr
icity
co
n
s
u
m
p
tio
n
i
n
p
r
im
ar
y
ec
o
n
o
m
ic
s
ec
to
r
s
.
Nu
m
er
o
u
s
u
p
d
ated
v
ar
ian
ts
o
f
th
e
SGM(
1
,
1
)
m
o
d
el,
s
u
ch
as
SF
GM
(
1
,
1
)
,
DT
GM
(
1
,
1
)
,
an
d
SNGB
M(
1
,
1
)
,
h
av
e
b
ee
n
d
ev
elo
p
ed
t
o
e
n
h
an
ce
th
e
ab
ilit
y
o
f
g
r
e
y
m
o
d
els
to
p
r
ed
ict
s
ea
s
o
n
al
tim
e
s
er
ies
[
9
]
–
[
1
1
]
.
E
ac
h
s
in
g
le
p
r
ed
ictio
n
m
o
d
el
h
as
u
n
i
q
u
e
i
n
f
o
r
m
atio
n
al
c
h
ar
ac
ter
is
tics
an
d
is
s
u
itab
le
f
o
r
s
p
ec
if
ic
co
n
d
itio
n
s
.
I
n
p
r
ac
tice,
it
is
co
m
m
o
n
f
o
r
o
n
e
f
o
r
ec
asti
n
g
m
o
d
el
to
p
e
r
f
o
r
m
well
d
u
r
in
g
ce
r
tain
p
er
io
d
s
,
wh
ile
o
th
er
s
ex
ce
l
at
d
if
f
er
e
n
t
tim
e
s
.
Du
e
to
th
e
in
h
e
r
en
t
r
a
n
d
o
m
n
ess
,
s
ea
s
o
n
ality
,
an
d
tr
en
d
s
in
elec
tr
icity
lo
ad
,
p
r
ed
ictio
n
s
f
r
o
m
a
s
in
g
le
m
o
d
el
o
f
ten
f
ail
to
f
u
lly
ca
p
tu
r
e
th
e
co
m
p
lex
ity
,
lead
in
g
to
l
o
wer
ac
cu
r
ac
y
.
I
t
is
d
if
f
icu
lt
to
f
in
d
a
f
o
r
ec
ast
m
o
d
el
th
at
o
u
tp
er
f
o
r
m
s
all
co
m
p
etin
g
m
o
d
els.
I
t
was
g
en
er
ally
co
n
clu
d
e
d
th
at
n
o
s
in
g
le
p
r
ed
icto
r
s
ca
n
b
e
a
p
p
r
o
p
r
iate
f
o
r
in
all
asp
ec
ts
o
f
m
o
d
ellin
g
b
ec
au
s
e
o
f
th
eir
lim
itatio
n
s
an
d
th
e
r
e
was
n
o
in
d
iv
i
d
u
al
in
tellig
en
t
ap
p
r
o
ac
h
ap
p
r
o
p
r
iate
f
o
r
all
s
p
ec
if
ic
p
r
o
b
lem
s
.
T
o
f
u
lly
lev
e
r
a
g
e
th
e
s
tr
en
g
th
s
an
d
u
n
iq
u
e
in
f
o
r
m
atio
n
o
f
ea
ch
in
d
iv
id
u
al
f
o
r
ec
ast
m
o
d
el,
co
m
b
in
atio
n
f
o
r
ec
asti
n
g
is
an
ef
f
ec
t
iv
e
ap
p
r
o
ac
h
.
T
h
is
m
eth
o
d
h
as b
ec
o
m
e
m
ai
n
s
tr
ea
m
in
f
o
r
ec
asti
n
g
an
d
is
in
cr
ea
s
in
g
ly
ad
o
p
ted
b
y
s
ch
o
lar
s
[
2
]
,
[
3
]
.
C
o
m
b
in
atio
n
f
o
r
ec
asti
n
g
ca
n
ac
h
iev
e
h
ig
h
er
ac
c
u
r
ac
y
an
d
m
o
r
e
r
eliab
le
r
esu
lts
.
T
h
e
p
r
im
a
r
y
r
ea
s
o
n
s
ar
e
two
f
o
ld
:
d
if
f
e
r
en
t
m
eth
o
d
s
ca
n
ca
p
tu
r
e
d
iv
e
r
s
e
ef
f
ec
tiv
e
in
f
o
r
m
atio
n
f
r
o
m
p
o
wer
lo
a
d
d
ata,
a
n
d
th
ey
ca
n
co
m
p
lem
en
t
ea
ch
o
th
e
r
.
I
t
is
i
m
p
o
r
tan
t
to
n
o
te
th
at
wh
ile
th
e
ac
cu
r
ac
y
o
f
co
m
b
i
n
ed
f
o
r
ec
asti
n
g
is
n
o
t
alwa
y
s
s
u
p
er
io
r
to
t
h
at
o
f
i
n
d
iv
id
u
al
m
o
d
els,
th
e
r
esu
lts
ar
e
o
f
ten
m
o
r
e
r
eliab
le
[
1
2
]
,
[
1
3
]
.
T
h
e
b
en
ef
its
o
f
f
o
r
ec
ast
co
m
b
in
atio
n
s
d
ep
en
d
n
o
t
o
n
ly
o
n
th
e
q
u
ality
o
f
th
e
in
d
i
v
id
u
al
f
o
r
ec
asts
b
u
t
also
o
n
th
e
esti
m
atio
n
o
f
th
e
c
o
m
b
in
atio
n
weig
h
ts
ass
ig
n
ed
to
ea
ch
f
o
r
ec
ast.
Nu
m
er
o
u
s
s
tu
d
ies
h
av
e
ex
p
lo
r
e
d
co
m
b
in
atio
n
m
eth
o
d
s
,
r
an
g
in
g
f
r
o
m
s
im
p
le
ap
p
r
o
ac
h
es
[
1
4
]
–
[
1
6
]
to
m
o
r
e
s
o
p
h
is
ticated
tech
n
iq
u
es
[
1
7
]
–
[
2
1
]
.
Desp
ite
th
e
co
m
p
lex
ity
o
f
s
o
m
e
co
m
b
in
atio
n
ap
p
r
o
ac
h
es
an
d
ad
v
an
ce
d
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
,
s
im
p
le
co
m
b
in
atio
n
s
r
em
ain
co
m
p
et
itiv
e.
T
h
e
r
ec
en
t
M
4
co
m
p
etitio
n
d
em
o
n
s
tr
ated
th
at
s
im
p
le
co
m
b
in
atio
n
s
co
n
tin
u
e
to
d
eliv
er
r
elativ
ely
g
o
o
d
f
o
r
ec
asti
n
g
p
e
r
f
o
r
m
an
ce
[
2
2
]
.
T
h
is
f
in
d
in
g
alig
n
s
with
p
r
ev
io
u
s
r
esear
ch
,
wh
ich
s
h
o
ws
th
at
s
im
p
le
co
m
b
in
atio
n
r
u
les
ar
e
o
f
te
n
p
r
e
f
er
r
ed
b
y
r
esear
ch
er
s
an
d
p
r
ac
titi
o
n
er
s
an
d
s
er
v
e
as
a
b
en
ch
m
ar
k
f
o
r
ev
alu
atin
g
n
ew
weig
h
ted
f
o
r
ec
ast co
m
b
in
atio
n
alg
o
r
ith
m
s
[
1
9
]
,
[
2
1
]
–
[
2
4
]
.
Alth
o
u
g
h
s
im
p
le
c
o
m
b
in
atio
n
s
ch
em
es
ar
e
s
tr
aig
h
tf
o
r
war
d
t
o
im
p
lem
en
t,
t
h
eir
s
u
cc
ess
h
e
av
ily
r
elies
o
n
th
e
s
elec
tio
n
o
f
th
e
weig
h
ted
f
o
r
ec
asts
to
b
e
co
m
b
i
n
e
d
.
T
h
e
cr
itical
asp
ec
t
o
f
an
ef
f
ec
tiv
e
co
m
b
in
e
d
m
eth
o
d
is
d
eter
m
in
in
g
th
e
a
p
p
r
o
p
r
iate
weig
h
t
co
ef
f
icien
t
.
I
f
th
e
weig
h
t
co
ef
f
icien
t
is
well
-
ch
o
s
en
,
th
e
co
m
b
in
ed
m
o
d
el
ca
n
y
ield
b
etter
p
r
ed
ictio
n
r
esu
lts
;
o
th
er
wis
e,
th
e
r
esu
lts
m
ay
b
e
s
u
b
o
p
tim
al.
T
h
er
e
ar
e
r
elativ
ely
f
ew
s
tu
d
ies o
n
th
e
m
eth
o
d
s
f
o
r
d
eter
m
i
n
in
g
weig
h
t c
o
ef
f
icien
ts
.
T
h
is
p
ap
er
ex
p
lo
r
es
th
e
p
o
ten
tial
o
f
co
m
b
in
in
g
f
o
r
ec
a
s
ts
u
s
in
g
b
asic
m
eth
o
d
s
o
f
weig
h
ted
co
m
b
in
atio
n
f
o
r
ec
asti
n
g
m
o
d
e
l
(
W
C
FM)
,
wh
ich
ef
f
ec
tiv
ely
h
ar
n
ess
es
th
e
s
tr
en
g
th
s
o
f
ea
c
h
in
d
iv
id
u
al
m
o
d
el
to
im
p
r
o
v
e
f
o
r
ec
asti
n
g
ac
c
u
r
ac
y
.
T
h
e
s
u
cc
ess
o
f
f
o
r
ec
a
s
t
co
m
b
in
atio
n
s
d
e
p
en
d
s
s
i
g
n
if
ican
tly
o
n
t
h
e
d
eter
m
in
atio
n
o
f
co
m
b
in
atio
n
weig
h
ts
.
T
o
o
p
tim
ize
th
e
m
o
d
el’
s
weig
h
ts
,
we
em
p
lo
y
th
e
AB
C
alg
o
r
ith
m
,
wh
ich
m
ax
im
izes
th
e
u
n
iq
u
e
ch
ar
ac
ter
is
tics
o
f
ea
ch
m
o
d
e
l.
T
h
e
AB
C
alg
o
r
ith
m
,
in
s
p
ir
ed
b
y
t
h
e
f
o
r
ag
in
g
b
eh
av
io
r
o
f
h
o
n
ey
b
ee
s
,
is
an
o
p
tim
izatio
n
tech
n
iq
u
e
th
at
h
as
b
ee
n
s
u
cc
ess
f
u
lly
ap
p
lied
to
v
ar
io
u
s
p
r
ac
tical
p
r
o
b
lem
s
[
2
4
]
.
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
8
5
4
-
5
8
6
2
5856
2.
M
E
T
H
O
D
Fo
u
r
p
r
e
d
ictio
n
m
o
d
els th
e
A
R
I
MA
,
SVM,
SGM
an
d
W
eig
h
ted
co
m
b
in
atio
n
f
o
r
ec
asti
n
g
m
o
d
els ar
e
d
is
cu
s
s
ed
in
th
is
s
ec
tio
n
.
T
h
e
f
o
llo
win
g
is
an
ex
p
lan
atio
n
o
f
ea
ch
m
o
d
el:
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
m
o
del
T
h
e
AR
I
MA
m
o
d
el,
in
tr
o
d
u
c
ed
b
y
B
o
x
an
d
J
en
k
in
s
in
1
9
7
0
[
2
5
]
,
is
a
wid
ely
a
d
o
p
te
d
m
eth
o
d
f
o
r
f
o
r
ec
asti
n
g
tim
e
s
er
ies
d
ata.
I
t
o
p
e
r
ates
b
y
u
s
in
g
a
li
n
ea
r
co
m
b
i
n
atio
n
o
f
its
p
ast
v
alu
es
an
d
t
h
e
lag
s
o
f
f
o
r
ec
ast er
r
o
r
s
(
r
an
d
o
m
s
h
o
c
k
s
)
.
T
h
e
f
o
r
m
u
la
f
o
r
an
AR
I
MA
(
,
,
)
(
,
,
)
s
m
o
d
el
is
Φ
(
)
(
)
(
1
−
)
(
1
−
)
=
(
)
Θ
(
)
(
1
)
w
h
e
r
e
i
s
t
h
e
o
r
i
g
i
n
a
l
v
al
u
e
,
a
r
e
e
r
r
o
r
,
Φ
(
)
a
n
d
(
)
a
r
e
t
h
e
s
ea
s
o
n
a
l
a
n
d
n
o
n
-
s
e
as
o
n
a
l
a
u
t
o
r
e
g
r
e
s
s
i
v
e
(
AR
)
p
o
l
y
n
o
m
i
a
l
s
,
(
1
−
)
a
n
d
(
1
−
)
a
r
e
t
h
e
s
e
as
o
n
a
l
a
n
d
n
o
n
-
s
e
a
s
o
n
a
l
d
i
f
f
e
r
e
n
c
i
n
g
,
Θ
(
)
a
n
d
(
)
a
r
e
t
h
e
s
e
a
s
o
n
a
l
a
n
d
n
o
n
-
s
e
a
s
o
n
a
l
m
o
v
i
n
g
a
v
e
r
a
g
e
(
M
A
)
p
o
l
y
n
o
m
i
a
l
s
.
T
h
e
B
o
x
-
J
e
n
k
i
n
s
m
e
t
h
o
d
o
l
o
g
y
,
e
s
s
e
n
ti
a
l
f
o
r
d
e
v
e
l
o
p
i
n
g
A
R
I
MA
m
o
d
e
ls
,
i
n
v
o
lv
e
s
f
i
v
e
k
e
y
s
t
e
p
s
:
a.
Statio
n
ar
ity
ch
ec
k
:
Dete
r
m
in
e
if
m
ee
ts
th
e
s
tatio
n
ar
y
tim
e
s
er
ies
co
n
d
itio
n
.
I
f
th
e
s
er
ies
is
n
o
n
-
s
tatio
n
ar
y
,
d
if
f
e
r
en
tiate
th
e
o
r
ig
in
al
tim
e
s
er
ies
to
ac
h
iev
e
s
t
atio
n
ar
ity
.
b.
Mo
d
el
id
en
tific
atio
n
:
Use
th
e
au
to
co
r
r
elatio
n
f
u
n
ctio
n
(
AC
F)
an
d
p
ar
tial
a
u
to
co
r
r
elatio
n
f
u
n
ctio
n
(
PAC
F)
o
f
th
e
s
tatio
n
ar
y
s
er
ies to
s
elec
t a
p
p
r
o
p
r
iate
AR
MA
m
o
d
els.
c.
Par
am
eter
esti
m
atio
n
:
E
s
tim
ate
th
e
m
o
d
el’
s
p
ar
am
eter
s
.
E
x
clu
d
e
lag
o
r
d
er
s
f
r
o
m
t
h
e
m
o
d
el
if
an
y
p
ar
am
eter
s
ar
e
n
o
t sig
n
if
ican
t
(
s
ig
n
if
ican
ce
lev
el
less
th
an
5
%).
d.
R
esid
u
al
d
iag
n
o
s
tics
:
T
est
th
e
m
o
d
el’
s
r
esid
u
als
to
ch
ec
k
if
th
ey
ar
e
wh
ite
n
o
is
e.
L
ju
n
g
an
d
B
o
x
[
2
6
]
p
r
o
p
o
s
ed
th
e
Q
s
tatis
tic
f
o
r
th
is
h
y
p
o
th
esis
test
.
I
f
th
e
r
es
id
u
al
s
eq
u
en
ce
is
n
o
t
wh
ite
n
o
is
e,
th
e
m
o
d
el
n
ee
d
s
r
ev
is
io
n
.
e.
Mo
d
el
s
elec
tio
n
:
C
h
o
o
s
e
th
e
o
p
tim
al
AR
I
MA
m
o
d
el
b
ased
o
n
th
e
lo
west
co
r
r
ec
ted
Ak
ai
k
e
in
f
o
r
m
ati
o
n
cr
iter
io
n
(
AI
C
c)
.
2
.
2
.
Su
pp
o
rt
v
ec
t
o
r
ma
chines
m
o
del
Su
p
p
o
r
t
v
ec
t
o
r
m
ac
h
in
es
(
SV
M)
in
tr
o
d
u
ce
d
b
y
Vap
n
ik
[
6
]
,
ar
e
b
ased
o
n
s
tatis
tical
lear
n
i
n
g
th
eo
r
y
an
d
t
h
e
p
r
in
cip
le
o
f
s
tr
u
ctu
r
al
r
is
k
m
i
n
im
izatio
n
.
T
h
e
f
u
n
d
a
m
en
tal
p
r
i
n
cip
le
o
f
SVM
f
o
r
r
eg
r
ess
io
n
in
v
o
lv
es
tr
an
s
f
o
r
m
in
g
th
e
in
p
u
t
d
ata
in
to
a
h
ig
h
-
d
im
en
s
io
n
al
f
ea
tu
r
e
s
p
ac
e
th
r
o
u
g
h
n
o
n
lin
ea
r
m
a
p
p
in
g
T
h
e
r
eg
r
ess
io
n
f
u
n
ctio
n
f
o
r
SVM
is
ex
p
r
ess
ed
as (
2
)
:
(
)
=
∑
(
)
+
=
1
(
2
)
T
h
e
co
ef
f
icien
t
[
]
ar
e
d
ete
r
m
in
e
d
b
y
s
o
lv
i
n
g
th
e
f
o
llo
win
g
q
u
a
d
r
atic
p
r
o
g
r
am
m
in
g
p
r
o
b
lem
:
min
,
,
,
∗
1
2
‖
‖
2
+
∑
(
+
∗
)
=
1
.
{
−
−
≤
+
〈
,
(
)
〉
−
+
≤
+
∗
≥
0
,
∗
≥
0
,
=
1
,
2
,
…
,
(
3
)
B
y
s
o
lv
in
g
th
is
o
p
tim
izatio
n
p
r
o
b
lem
,
th
e
esti
m
atio
n
f
u
n
ctio
n
is
o
b
tain
ed
as (
4
)
:
(
,
,
∗
)
=
∑
(
−
∗
)
〈
(
)
(
)
〉
+
=
1
=
∑
(
−
∗
)
(
,
)
+
=
1
(
4
)
wh
er
e
[
(
)
]
ar
e
th
e
f
ea
tu
r
es,
an
d
is
th
e
b
ias
ter
m
,
∗
ar
e
s
lack
v
ar
iab
les,
an
d
>
0
is
a
co
n
s
tan
t
th
at
d
eter
m
in
es
p
en
alties.
I
n
th
e
eq
u
atio
n
,
∑
(
−
∗
)
=
0
,
=
1
0
≤
,
∗
≥
)
,
an
d
(
,
)
is
th
e
k
er
n
el
f
u
n
ctio
n
.
Am
o
n
g
v
ar
io
u
s
k
er
n
el
f
u
n
ctio
n
s
,
th
e
r
ad
ial
b
asi
s
f
u
n
ctio
n
(
R
B
F)
is
th
e
m
o
s
t
co
m
m
o
n
ly
u
s
ed
,
d
ef
in
ed
as (
5
)
:
(
,
)
=
(
−
‖
−
‖
2
2
2
)
(
5
)
wh
er
e
is
wid
th
o
f
th
e
R
B
F.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
I
mp
r
o
vin
g
elec
tr
ica
l lo
a
d
fo
r
ec
a
s
tin
g
b
y
in
teg
r
a
tin
g
a
w
eig
h
ted
…
(
A
n
i
S
h
a
b
r
i
)
5857
2
.
2
.
Sea
s
o
na
l
g
re
y
m
o
del
Su
p
p
o
r
t
let’
s
co
n
s
id
er
th
e
ti
m
e
s
er
ies
(
0
)
=
{
1
(
0
)
,
2
(
0
)
,
…
,
(
0
)
,
…
,
(
0
)
}
.
T
h
e
f
ir
s
t
-
o
r
d
er
s
ea
s
o
n
al
ac
cu
m
u
latin
g
g
en
er
atio
n
o
p
er
ato
r
(
1
-
SAGO)
is
d
en
o
ted
as
(
1
)
.
Usi
n
g
th
is
o
p
e
r
ato
r
,
a
s
ea
s
o
n
ally
-
af
f
ec
ted
o
r
ig
in
al
s
er
ies ca
n
b
e
d
ef
in
ed
as
(
6
)
[
1
0
]
:
(
1
)
=
(
0
)
=
{
1
(
1
)
,
2
(
1
)
,
…
,
(
1
)
}
(
6
)
with
(
1
)
=
∑
(
0
)
/
(
)
=
1
,
=
1
,
2
,
…
,
.
wh
er
e
(
)
is
th
e
s
ea
s
o
n
al
f
ac
to
r
in
th
e
o
r
ig
in
al
s
er
ies
o
cc
u
r
r
in
g
at
th
e
i
th
p
o
in
t
in
tim
e.
T
h
e
(
)
co
u
ld
b
e
d
eter
m
in
ed
v
ia
(
7
)
,
(
)
=
̅
(
0
)
(
)
̅
(
0
)
(
)
(
7
)
wh
er
e
M
is
th
e
n
u
m
b
er
o
f
s
ea
s
o
n
s
in
a
y
ea
r
,
an
d
N
th
e
tim
e
p
o
in
t'
s
i
th
y
ea
r
.
T
h
e
̅
(
0
)
(
)
is
th
e
av
er
ag
e
v
alu
e
o
f
th
e
s
er
ies
o
v
er
t
h
e
s
ea
s
o
n
al
c
y
cle
an
d
th
e
an
d
̅
(
0
)
(
)
is
to
tal
av
er
ag
e
v
alu
e
f
o
r
all
s
ea
s
o
n
s
o
r
m
o
n
th
s
.
T
h
e
b
ac
k
g
r
o
u
n
d
v
alu
e
is
ca
lcu
late
d
u
s
in
g
(
8
)
.
(
1
)
(
)
=
0
.
5
(
1
)
(
)
+
0
.
5
(
1
)
(
−
1
)
,
∀
=
2
,
3
,
…
,
.
(
8
)
T
h
e
f
o
llo
win
g
is
th
e
SGM(
1
,
1
)
eq
u
atio
n
:
(
1
)
(
)
−
(
1
)
(
−
1
)
+
(
1
)
(
)
=
(9
)
T
h
e
least
-
s
q
u
ar
e
a
p
p
r
o
ac
h
is
u
s
ed
to
esti
m
ate
th
e
m
o
d
el'
s
p
ar
am
eter
s
in
(
1
2
)
.
T
h
e
p
ar
a
m
eter
s
o
f
t
h
e
m
o
d
el
ar
e
co
m
p
u
ted
as f
o
llo
ws:
[
]
=
(
)
−
1
wh
er
e
=
(
1
(
2
)
−
1
(
1
)
1
(
3
)
−
1
(
2
)
⋮
1
(
)
−
1
(
−
1
)
)
an
d
=
(
−
(
1
)
(
2
)
1
−
(
1
)
(
3
)
1
⋮
⋮
−
(
1
)
(
)
1
)
(
1
0
)
E
q
u
atio
n
(
1
0
)
is
s
o
lv
ed
as
(
1
1
)
:
̂
(
1
)
(
)
=
(
1
(
0
)
/
(
1
)
−
)
−
+
(
1
1
)
Usi
n
g
th
e
in
v
er
s
e
1
-
SAGO,
th
e
p
r
ed
icted
v
alu
e
o
f
SGM(
1
,
1
)
ca
n
b
e
d
eter
m
in
ed
as
(
1
2
)
:
̂
(
0
)
(
)
=
(
)
(
̂
(
1
)
(
)
−
̂
(
1
)
(
−
1
)
)
,
=
2
,
3
,
…
,
(
1
2
)
2
.
4
.
W
eig
hte
d
co
m
bin
a
t
io
n f
o
re
ca
s
t
ing
m
o
del
T
o
en
h
an
ce
f
o
r
ec
asti
n
g
q
u
alit
y
,
th
is
s
tu
d
y
s
elec
ted
th
r
ee
in
d
iv
id
u
al
m
o
d
els
-
AR
I
MA
,
SVM,
an
d
SGM(
1
,
1
)
as
b
ase
p
r
ed
icto
r
s
.
Du
r
in
g
th
e
f
o
r
ec
asti
n
g
p
r
o
ce
s
s
,
th
e
AR
I
MA
m
o
d
el
a
d
d
r
ess
es
lin
ea
r
p
r
o
b
lem
s
,
wh
ile
SVM
an
d
SGM(
1
,
1
)
h
an
d
le
n
o
n
lin
ea
r
s
er
ies
f
o
r
ec
asti
n
g
.
Giv
en
th
at
elec
tr
icity
lo
ad
d
ata
ex
h
ib
its
s
ea
s
o
n
ality
an
d
n
o
n
lin
ea
r
it
y
,
b
u
t
s
o
m
etim
es
s
h
o
ws
lin
ea
r
f
ea
tu
r
es,
th
ese
th
r
ee
m
o
d
els
co
llectiv
ely
ad
d
r
ess
b
o
th
n
o
n
lin
ea
r
an
d
lin
ea
r
f
o
r
e
ca
s
tin
g
ch
allen
g
es.
H
o
wev
er
,
th
e
k
ey
to
co
m
b
i
n
in
g
f
o
r
ec
ast
in
g
m
o
d
els
lies
in
o
p
tim
ally
ch
o
o
s
in
g
th
e
co
m
b
in
atio
n
weig
h
ts
.
I
n
th
is
s
tu
d
y
,
th
e
W
C
FM
is
u
s
ed
to
in
teg
r
ate
all
f
o
r
ec
asted
co
m
p
o
n
en
ts
in
to
a
n
e
n
s
em
b
le
f
o
r
th
e
f
in
al
f
o
r
ec
ast.
T
h
e
weig
h
ts
in
W
C
FM
s
ig
n
if
ican
tly
im
p
ac
t
th
e
r
esu
lts
an
d
ar
e
ch
allen
g
in
g
to
d
ete
r
m
in
e.
T
h
er
ef
o
r
e,
th
e
weig
h
ts
in
W
C
FM
wer
e
o
p
tim
ized
u
s
in
g
t
h
e
AB
C
alg
o
r
ith
m
,
w
h
ich
m
ar
k
ed
ly
im
p
r
o
v
e
d
ac
cu
r
ac
y
.
E
x
p
er
im
e
n
tal
r
esu
lts
in
d
icate
th
at
th
e
p
r
o
p
o
s
ed
co
m
b
in
e
d
m
o
d
el
o
u
tp
er
f
o
r
m
s
in
d
iv
i
d
u
al
m
o
d
els an
d
s
ig
n
if
ica
n
tly
s
u
r
p
a
s
s
es th
e
b
asic
W
C
F
M.
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
8
5
4
-
5
8
6
2
5858
L
et
b
e
th
e
tr
u
e
v
alu
e
o
f
t
h
e
s
er
ies
at
tim
e
t
a
n
d
b
e
th
e
f
o
r
ec
as
t
m
ad
e
b
y
th
e
k
-
th
m
o
d
el
at
tim
e
t
.
T
h
e
W
C
FM
i
s
g
iv
en
b
y
(
1
3
)
,
=
∑
=
1
(
1
3
)
is
th
e
weig
h
t c
o
ef
f
icien
t
o
f
th
e
k
-
th
m
o
d
el
at
tim
e
t
,
s
atis
f
y
in
g
:
∑
=
1
=
1
an
d
0
≤
≤
1
,
=
1
,
2
,
…
,
.
(
1
4
)
T
h
e
AB
C
alg
o
r
ith
m
is
u
s
ed
to
f
in
d
th
e
o
p
tim
al
v
alu
es
f
o
r
(
w
)
to
s
o
lv
e
th
e
n
o
n
lin
ea
r
o
p
tim
izatio
n
p
r
o
b
lem
.
T
h
e
s
u
m
o
f
s
q
u
ar
e
d
er
r
o
r
s
(
SS
E
)
is
em
p
lo
y
ed
as
th
e
f
itn
ess
f
u
n
ctio
n
f
o
r
th
e
AB
C
alg
o
r
ith
m
.
T
h
e
co
n
s
tr
ain
e
d
o
p
tim
izatio
n
p
r
o
b
lem
t
o
o
b
tai
n
th
e
o
p
tim
al
weig
h
ts
ca
n
b
e
d
escr
ib
ed
as f
o
llo
ws:
min
=
∑
(
−
)
2
=
1
,
s
u
b
ject
to
∑
=
1
an
d
0
≤
≤
1
T
h
e
AB
C
alg
o
r
ith
m
is
r
elati
v
ely
s
im
p
le,
f
lex
ib
le,
r
eliab
le
,
an
d
r
e
q
u
ir
es
f
ewe
r
tu
n
in
g
p
ar
am
eter
s
[
2
4
]
.
T
h
e
AB
C
alg
o
r
ith
m
is
co
m
p
o
s
ed
o
f
f
o
u
r
m
ain
elem
en
ts
as e
x
p
lain
ed
as f
o
llo
ws:
a.
I
n
itializatio
n
: T
h
e
alg
o
r
ith
m
s
tar
ts
b
y
r
an
d
o
m
ly
g
en
er
atin
g
a
n
in
itial p
o
p
u
latio
n
o
f
s
o
lu
tio
n
s
.
b.
E
m
p
lo
y
ed
B
ee
s
:
T
h
ese
b
ee
s
ex
p
lo
r
e
th
e
v
icin
ity
o
f
th
eir
cu
r
r
en
t
f
o
o
d
s
o
u
r
ce
(
s
o
lu
tio
n
)
t
o
d
is
co
v
er
n
ew,
p
o
ten
tially
b
etter
s
o
lu
tio
n
s
.
c.
On
lo
o
k
er
B
ee
s
:
T
h
ese
b
ee
s
ass
ess
th
e
q
u
ality
o
f
t
h
e
f
o
o
d
s
o
u
r
ce
s
f
o
u
n
d
b
y
t
h
e
em
p
l
o
y
ed
b
ee
s
an
d
s
elec
t
th
e
b
est o
n
es b
ased
o
n
a
p
r
o
b
a
b
ilit
y
r
elate
d
to
th
eir
q
u
ality
.
d.
Sco
u
t
B
ee
s
:
W
h
en
a
f
o
o
d
s
o
u
r
ce
is
ab
an
d
o
n
ed
(
i.e
.
,
it
n
o
lo
n
g
er
y
ield
s
b
etter
s
o
lu
tio
n
s
)
,
s
co
u
t
b
ee
s
s
ea
r
ch
f
o
r
n
ew
r
an
d
o
m
f
o
o
d
s
o
u
r
ce
s
to
ex
p
lo
r
e.
T
h
i
s
it
e
r
a
t
i
v
e
p
r
o
c
e
s
s
c
o
n
t
i
n
u
e
s
u
n
t
i
l
a
t
e
r
m
i
n
a
t
i
o
n
c
r
it
e
r
i
o
n
i
s
m
et
,
s
u
c
h
as
r
e
a
c
h
i
n
g
a
m
a
x
i
m
u
m
n
u
m
b
e
r
o
f
c
y
c
l
e
s
o
r
a
c
h
ie
v
i
n
g
a
s
at
is
f
a
c
t
o
r
y
s
o
l
u
t
i
o
n
q
u
a
l
i
t
y
.
M
o
r
e
d
e
t
a
ils
a
b
o
u
t
t
h
e
e
n
ti
r
e
p
r
o
c
e
d
u
r
e
c
an
b
e
f
o
u
n
d
i
n
[
2
4
]
.
3.
E
VA
L
UA
T
I
O
N
M
E
T
RIC
S
E
v
alu
atio
n
cr
iter
ia
ar
e
cr
u
cial
f
o
r
ass
ess
in
g
th
e
s
im
u
latio
n
an
d
p
r
e
d
ictio
n
ac
cu
r
ac
y
o
f
d
if
f
er
en
t
m
o
d
els.
I
n
th
is
s
tu
d
y
,
th
e
r
o
o
t
m
ea
n
s
q
u
a
r
e
er
r
o
r
(
R
MSE
)
a
n
d
th
e
m
ea
n
a
b
s
o
lu
te
p
er
ce
n
t
er
r
o
r
(
MA
PE)
ar
e
em
p
lo
y
ed
as
s
tan
d
ar
d
m
etr
ics to
ev
alu
ate
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
p
r
o
p
o
s
ed
m
o
d
el.
T
h
e
R
MSE
an
d
MA
PE
ar
e
ex
p
r
ess
ed
as f
o
llo
ws:
=
√
1
∑
(
−
)
2
=
1
an
d
=
1
∑
|
−
|
×
100%
=
1
T
h
e
s
y
m
b
o
ls
is
th
e
ac
tu
al,
is
p
r
ed
icted
v
alu
es,
a
n
d
n
is
th
e
n
u
m
b
er
o
f
o
b
s
er
v
atio
n
s
.
T
h
ese
m
etr
ics
p
r
o
v
id
e
a
c
o
m
p
r
eh
en
s
iv
e
ev
a
lu
atio
n
o
f
th
e
m
o
d
el’
s
ac
cu
r
a
cy
b
y
m
ea
s
u
r
i
n
g
t
h
e
av
e
r
ag
e
m
ag
n
itu
d
e
o
f
t
h
e
er
r
o
r
s
in
th
e
p
r
ed
ictio
n
s
.
R
MSE
g
iv
es
a
h
ig
h
er
weig
h
t
to
l
ar
g
er
er
r
o
r
s
,
m
ak
in
g
it
s
en
s
iti
v
e
to
o
u
tlier
s
,
wh
ile
MA
PE
ex
p
r
ess
es th
e
er
r
o
r
as
a
p
er
ce
n
tag
e,
p
r
o
v
id
in
g
a
n
o
r
m
alize
d
m
ea
s
u
r
e
o
f
p
r
e
d
ictio
n
ac
cu
r
ac
y
.
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
I
n
th
is
s
ec
tio
n
,
we
p
r
esen
t
two
r
ea
l
-
wo
r
ld
ca
s
e
s
tu
d
i
es
to
v
alid
ate
th
e
ef
f
ec
tiv
en
ess
an
d
g
en
er
aliza
b
ilit
y
o
f
th
e
n
ewly
p
r
o
p
o
s
ed
m
eth
o
d
.
T
h
e
s
tu
d
y
u
tili
ze
s
h
is
to
r
ical
m
o
n
th
ly
e
lectr
icity
lo
ad
d
ata
f
r
o
m
J
an
u
ar
y
2
0
1
1
to
Dec
em
b
er
2
0
2
1
f
o
r
Ma
lay
s
ia
an
d
T
h
ailan
d
,
as
s
h
o
wn
i
n
Fig
u
r
e
1
.
T
h
e
d
ataset
co
m
p
r
is
es
1
4
4
tim
e
p
o
i
n
ts
in
to
tal.
B
o
th
co
u
n
tr
ies’
d
ata
d
i
s
p
lay
a
wav
e
-
lik
e
p
atter
n
with
d
is
tin
ct
s
ea
s
o
n
al
v
ar
iatio
n
s
,
m
ak
in
g
it
ess
en
tial
to
ca
p
tu
r
e
b
o
th
tr
en
d
an
d
s
ea
s
o
n
ality
f
o
r
ac
cu
r
ate
f
o
r
ec
asti
n
g
o
f
c
o
m
p
lex
tim
e
s
er
ies.
T
o
en
s
u
r
e
h
ig
h
p
r
e
d
i
ctio
n
ac
cu
r
ac
y
,
t
h
e
d
ata
wer
e
d
iv
id
ed
in
to
tr
ain
i
n
g
(
s
im
u
latio
n
)
an
d
test
in
g
(
p
r
ed
ictio
n
)
s
u
b
s
ets.
T
h
e
m
o
n
th
ly
lo
ad
d
ata
f
r
o
m
J
an
u
ar
y
2
0
1
1
t
o
Dec
em
b
er
2
0
2
0
we
r
e
u
s
ed
as
th
e
tr
ain
i
n
g
s
et,
wh
ile
th
e
f
in
al
1
2
d
ata
p
o
in
ts
f
r
o
m
J
an
u
ar
y
2
0
2
1
to
De
ce
m
b
er
2
0
2
1
s
er
v
e
d
as
th
e
test
s
et
to
ev
alu
ate
th
e
m
o
d
el’
s
p
r
e
d
ictio
n
p
e
r
f
o
r
m
an
ce
.
T
h
e
AR
I
MA
an
d
SVM
m
o
d
els
u
tili
ze
ac
tu
al
d
ata
v
alu
es
to
esti
m
ate
p
ar
am
eter
s
an
d
g
en
er
ate
f
o
r
ec
asts
.
T
h
e
SVM
m
o
d
el
is
im
p
lem
en
ted
in
R
u
s
in
g
th
e
e1
0
7
1
p
ac
k
ag
e,
wh
ile
AR
I
MA
is
im
p
lem
en
ted
u
s
in
g
th
e
f
o
r
ec
ast
p
ac
k
ag
e
to
p
r
ed
ict
elec
tr
icity
lo
a
d
.
Fo
r
th
e
SVM
m
o
d
el,
a
g
r
id
s
ea
r
c
h
is
co
n
d
u
cte
d
to
o
p
tim
ize
h
y
p
er
p
ar
am
eter
s
,
s
el
ec
tin
g
th
e
p
en
alty
co
e
f
f
icien
t
γ
f
r
o
m
t
h
e
s
et
(
0
.
0
1
,
0
.
1
,
1
,
1
0
,
1
0
0
,
1
0
0
0
)
an
d
th
e
g
am
m
a
v
alu
e
σ
f
r
o
m
th
e
r
an
g
e
(
1
0
-
3
,
1
0
4
)
,
ap
p
ly
in
g
th
e
Gau
s
s
ian
k
er
n
el.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
I
mp
r
o
vin
g
elec
tr
ica
l lo
a
d
fo
r
ec
a
s
tin
g
b
y
in
teg
r
a
tin
g
a
w
eig
h
ted
…
(
A
n
i
S
h
a
b
r
i
)
5859
Fo
r
th
e
AR
I
MA
m
o
d
el,
th
e
f
o
r
ec
ast
p
ac
k
ag
e’
s
a
u
to
.
AR
I
MA
f
u
n
ctio
n
is
u
s
ed
to
f
in
d
th
e
b
est
AR
I
MA
m
o
d
el
b
ased
o
n
t
h
e
AI
C
c.
B
y
d
ef
au
lt,
t
h
is
p
ac
k
ag
e
s
ets
th
e
m
ax
im
u
m
o
r
d
er
f
o
r
d
,
P
,
an
d
Q
to
2
,
D
to
1
,
an
d
p
an
d
q
to
5
.
T
h
e
SAR
I
MA
m
o
d
el
p
ar
am
eter
s
ar
e
esti
m
ated
u
s
in
g
th
e
m
ax
im
u
m
l
ik
elih
o
o
d
esti
m
ate
(
ML
E
)
,
a
n
d
th
e
L
ju
n
g
-
B
o
x
(
L
B
)
test
is
u
s
ed
to
co
n
f
ir
m
th
e
m
o
d
el’
s
s
u
itab
ilit
y
f
o
r
th
e
d
at
a.
T
h
e
AI
C
is
u
s
ed
to
ch
o
o
s
e
v
alu
es
f
o
r
p
,
P
,
q
,
an
d
Q
,
wh
ile
th
e
Kwiatk
o
wsk
i
-
Ph
illi
p
s
-
Sch
m
id
t
-
Sh
in
(
KPS
S)
test
is
ap
p
lied
to
ch
o
o
s
e
v
alu
es f
o
r
d
a
n
d
D
.
Un
lik
e
o
th
er
m
o
d
els,
th
e
SGM(
1
,
1
)
m
o
d
el
f
o
llo
ws
two
k
e
y
s
tep
s
f
o
r
p
ar
am
eter
esti
m
ati
o
n
:
f
ir
s
t,
th
e
s
ea
s
o
n
al
f
ac
to
r
is
d
er
iv
ed
f
r
o
m
th
e
o
r
ig
i
n
al
d
ata,
an
d
th
en
th
e
p
ar
am
eter
s
ar
e
esti
m
ated
u
s
in
g
th
e
m
o
d
if
ied
d
ata
with
s
ea
s
o
n
ality
r
em
o
v
e
d
.
T
h
e
f
o
r
ec
asti
n
g
m
o
d
els
A
R
I
MA
,
SVM,
an
d
SGM(
1
,
1
)
ar
e
ch
o
s
en
f
o
r
th
e
co
m
b
in
ed
f
o
r
ec
ast
u
s
in
g
W
C
FM.
T
h
e
MA
T
L
AB
to
o
lb
o
x
is
em
p
lo
y
ed
to
d
eter
m
i
n
e
th
e
o
p
tim
al
weig
h
t
co
ef
f
icien
ts
f
o
r
W
C
FM,
wh
ich
ar
e
ca
lcu
lated
u
s
in
g
th
e
AB
C
alg
o
r
ith
m
.
T
ab
le
1
a
n
d
Fig
u
r
e
2
d
is
p
lay
th
e
tr
ain
in
g
an
d
test
in
g
o
u
tco
m
es
an
d
ac
c
u
r
ac
y
lev
els
f
o
r
Ma
lay
s
ia’
s
elec
tr
icity
lo
ad
.
Fig
u
r
e
2
illu
s
t
r
ates
th
at
th
e
f
o
u
r
m
o
d
els
ca
n
ef
f
ec
tiv
ely
ca
p
tu
r
e
s
ea
s
o
n
al
v
ar
iatio
n
s
an
d
alig
n
with
th
e
u
p
war
d
tr
e
n
d
s
o
f
th
e
o
r
ig
in
al
d
ata.
Vis
u
ally
,
as sh
o
wn
in
Fig
u
r
e
2
,
th
e
p
r
o
p
o
s
ed
m
o
d
el’
s
tr
ain
in
g
a
n
d
test
in
g
v
alu
es
ar
e
clo
s
er
to
th
e
ac
tu
al
d
ata,
wh
ile
o
th
e
r
b
en
ch
m
a
r
k
m
o
d
els
ex
h
ib
it
lar
g
er
d
ev
iatio
n
s
,
esp
ec
ially
d
u
r
in
g
t
h
e
test
in
g
p
h
ase.
I
n
ter
m
s
o
f
ac
cu
r
ac
y
,
th
e
W
C
FM
p
er
f
o
r
m
s
ex
ce
p
tio
n
ally
well,
with
MA
PE
v
alu
es o
f
1
.
6
7
% f
o
r
tr
a
in
in
g
an
d
2
.
6
7
% f
o
r
test
in
g
.
Acc
o
r
d
in
g
t
o
T
ab
le
1
,
b
ased
o
n
MA
PE
an
d
R
MSE
d
u
r
in
g
th
e
tr
ai
n
in
g
a
n
d
test
in
g
p
h
ases
,
th
e
AR
I
MA
m
o
d
el
r
an
k
s
s
ec
o
n
d
in
ac
cu
r
ac
y
,
f
o
llo
wed
b
y
SVM.
C
o
n
v
er
s
ely
,
th
e
SGM(
1
,
1
)
m
o
d
el
s
h
o
ws
th
e
p
o
o
r
est
p
e
r
f
o
r
m
an
ce
,
with
th
e
h
ig
h
est
MA
PE
an
d
R
MSE
v
alu
es
d
u
r
in
g
b
o
th
p
h
ases
,
in
d
icatin
g
wea
k
f
o
r
ec
asti
n
g
ab
ilit
y
.
B
o
th
Fig
u
r
e
2
an
d
T
ab
le
1
in
d
icate
th
at
th
e
p
r
o
p
o
s
e
d
m
eth
o
d
p
r
o
v
i
d
es m
o
r
e
p
r
ec
is
e
f
o
r
ec
asts
th
an
th
e
o
th
er
m
o
d
e
ls
.
Ad
d
itio
n
ally
,
T
a
b
le
1
illu
s
tr
at
es
th
e
ac
cu
r
ac
y
o
f
p
r
ed
ictin
g
T
h
ailan
d
’
s
m
o
n
th
ly
elec
tr
icity
lo
ad
u
s
in
g
MA
PE
an
d
R
MSE
m
etr
ic
s
.
T
h
e
tab
le
co
n
f
ir
m
s
th
at
th
e
p
r
o
p
o
s
ed
m
o
d
el
d
eliv
er
s
th
e
b
est
f
o
r
ec
asti
n
g
p
er
f
o
r
m
an
ce
.
T
h
e
n
ew
m
o
d
e
l
h
as
th
e
lo
west
MA
PE
an
d
R
MSE
v
alu
es
d
u
r
in
g
b
o
th
t
r
ain
in
g
an
d
test
in
g
s
tag
es,
wh
ile
th
e
SG
M
m
o
d
el
h
as
th
e
h
ig
h
est.
T
h
e
AR
I
MA
m
o
d
el
r
an
k
s
s
ec
o
n
d
in
ac
c
u
r
ac
y
,
f
o
llo
wed
b
y
SVM,
d
em
o
n
s
tr
atin
g
its
s
tr
o
n
g
f
o
r
ec
asti
n
g
ca
p
ab
ilit
y
.
Fig
u
r
e
1
.
T
h
e
d
ata
r
estar
t p
r
o
c
ess
T
ab
le
1
.
Ma
lay
s
ia
an
d
T
h
ailan
d
'
s
lo
ad
f
o
r
ec
asti
n
g
ev
alu
atio
n
m
etr
ics f
o
r
v
a
r
io
u
s
f
o
r
ec
asti
n
g
m
o
d
els
C
o
u
n
t
r
y
M
e
t
r
i
c
S
G
M
(
1
,
1
)
S
V
M
A
R
I
M
A
W
C
F
M
M
a
l
a
y
si
a
M
A
P
E
Tr
a
i
n
i
n
g
3
.
2
0
2
.
7
7
2
.
8
0
2
.
6
8
Te
st
i
n
g
4
.
1
4
4
.
3
8
2
.
9
0
2
.
8
2
R
M
S
E
Tr
a
i
n
i
n
g
5
3
3
.
9
8
4
5
4
.
5
6
4
9
4
.
4
0
4
2
7
.
0
5
Te
st
i
n
g
7
7
8
.
2
2
7
0
3
.
1
6
5
5
6
.
2
8
4
8
2
.
5
5
Th
a
i
l
a
n
d
M
A
P
E
Tr
a
i
n
i
n
g
2
.
6
3
%
1
.
7
4
%
1
.
7
7
%
1
.
6
7
%
Te
st
i
n
g
5
.
6
9
%
3
.
9
4
%
3
.
2
1
%
2
.
6
7
%
R
M
S
E
Tr
a
i
n
i
n
g
4
9
9
.
8
1
3
4
3
.
4
4
3
3
8
.
0
2
3
1
2
.
0
5
Te
st
i
n
g
9
3
3
.
7
9
7
8
3
.
7
6
5
6
8
.
9
2
5
0
2
.
9
8
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
8
5
4
-
5
8
6
2
5860
Fig
u
r
e
2
.
T
h
e
r
ea
l v
al
u
e
cu
r
v
e
s
an
d
f
o
r
ec
asts
f
o
r
Ma
lay
s
ia
an
d
T
h
ailan
d
u
s
in
g
f
o
u
r
d
if
f
e
r
e
n
t m
o
d
els:
AR
I
MA
,
SVM,
SGM(
1
,
1
)
,
an
d
W
C
FM
Ov
er
all,
th
e
W
C
FM
m
o
d
el
ex
h
ib
its
s
u
p
er
io
r
m
o
d
ellin
g
an
d
f
o
r
ec
asti
n
g
ac
cu
r
ac
y
co
m
p
ar
e
d
to
o
th
er
m
o
d
els.
B
y
in
co
r
p
o
r
atin
g
th
e
weig
h
ted
co
e
f
f
icien
ts
o
f
e
ac
h
m
o
d
el,
th
e
o
p
tim
ized
W
C
FM
s
ig
n
if
ican
tly
en
h
an
ce
s
th
e
f
o
r
ec
asti
n
g
ab
ili
ty
o
f
tr
ad
itio
n
al
s
in
g
le
m
o
d
el
s
,
p
r
o
v
in
g
its
s
u
p
er
io
r
ad
a
p
ta
b
ilit
y
in
p
r
ed
ictin
g
m
o
n
th
ly
elec
tr
icity
lo
ad
.
Fig
u
r
e
2
s
h
o
ws
th
e
tr
ain
in
g
a
n
d
test
in
g
v
alu
es
o
f
th
e
f
o
u
r
m
o
d
els
f
o
r
m
o
n
th
ly
elec
tr
icity
lo
ad
,
with
th
e
W
C
FM
v
alu
es c
lo
s
ely
m
atch
in
g
t
h
e
ac
tu
al
d
ata
in
b
o
th
s
tag
es.
T
h
ese
ca
s
e
s
tu
d
ies
d
em
o
n
s
tr
ate
th
at
th
e
W
C
FM
ac
h
iev
es
h
ig
h
er
p
r
ec
is
io
n
in
tr
ain
i
n
g
a
n
d
test
in
g
th
an
o
th
er
m
o
d
els
f
o
r
m
o
n
th
l
y
elec
tr
icity
lo
ad
f
o
r
ec
asti
n
g
.
T
h
e
AR
I
MA
m
o
d
el
r
an
k
s
s
ec
o
n
d
,
f
o
llo
wed
b
y
SVM
an
d
SGM,
in
d
icatin
g
th
at
th
e
p
r
o
p
o
s
ed
m
o
d
el
p
r
o
v
id
es
r
elativ
ely
lo
w
er
r
o
r
a
n
d
r
eliab
le
p
r
ed
ictio
n
ca
p
ab
ilit
y
.
5.
CO
NCLU
SI
O
N
T
h
e
g
l
o
b
al
d
ev
elo
p
m
en
t
o
f
elec
tr
ical
lo
ad
is
a
cc
eler
atin
g
,
p
r
o
m
p
tin
g
ex
te
n
s
iv
e
r
esear
ch
b
y
s
ch
o
lar
s
in
to
f
o
r
ec
asti
n
g
m
et
h
o
d
s
.
W
id
ely
u
s
ed
m
o
d
els
f
o
r
p
r
ed
ict
in
g
elec
tr
ical
lo
ad
d
ata
with
s
ea
s
o
n
al
an
d
tr
en
d
ch
ar
ac
ter
is
tics
in
clu
d
e
s
tatis
ti
ca
l
m
o
d
els,
ar
tific
ial
in
tellig
e
n
ce
,
an
d
g
r
e
y
m
o
d
els.
T
h
is
s
tu
d
y
in
tr
o
d
u
ce
s
a
n
o
v
el
co
m
b
in
ed
W
C
FM
f
o
r
f
o
r
ec
asti
n
g
m
o
n
th
ly
elec
tr
ical
lo
ad
d
ata,
u
tili
zin
g
th
e
AB
C
alg
o
r
ith
m
to
o
p
tim
ize
m
o
d
el
p
ar
a
m
eter
s
an
d
e
n
h
an
c
e
f
o
r
ec
asti
n
g
p
e
r
f
o
r
m
an
ce
.
I
n
o
u
r
ex
p
e
r
im
en
ts
,
th
e
in
n
o
v
ativ
e
W
C
FM,
wh
ich
in
teg
r
ates
th
r
ee
d
is
tin
ct
m
o
d
els,
ef
f
ec
tiv
ely
ad
d
r
ess
es b
o
th
s
ea
s
o
n
al
an
d
lin
ea
r
tr
en
d
f
o
r
ec
asti
n
g
ch
allen
g
es.
C
o
m
p
ar
ed
to
in
d
i
v
id
u
al
m
o
d
els lik
e
AR
I
MA
,
SVM,
an
d
SGM(
1
,
1
)
,
th
e
co
m
b
in
ed
m
o
d
el
d
em
o
n
s
tr
ates
s
ig
n
if
ican
t
im
p
r
o
v
e
m
en
ts
in
ac
cu
r
ac
y
,
s
tab
ilit
y
,
an
d
tr
en
d
p
r
e
d
ictio
n
.
C
o
n
s
eq
u
en
tl
y
,
th
e
W
C
FM,
w
ith
its
s
u
p
er
io
r
ac
cu
r
ac
y
,
s
h
o
ws
g
r
ea
t
p
o
ten
tial
f
o
r
f
u
tu
r
e
ap
p
licatio
n
s
.
Ad
d
itio
n
ally
,
th
is
co
m
b
in
ed
m
o
d
el
ca
n
b
e
a
p
p
lied
to
v
ar
io
u
s
f
ield
s
,
in
cl
u
d
in
g
p
o
wer
l
o
ad
f
o
r
ec
asti
n
g
,
s
to
ck
p
r
ice
f
o
r
ec
a
s
tin
g
,
an
d
tr
af
f
ic
f
lo
w
f
o
r
ec
asti
n
g
.
ACK
NO
WL
E
DG
M
E
N
T
S
T
h
e
au
th
o
r
s
ex
ten
d
th
eir
ap
p
r
ec
iatio
n
to
th
e
Min
is
tr
y
o
f
Hig
h
er
E
d
u
ca
tio
n
Ma
lay
s
ia
an
d
Un
iv
er
s
iti
T
ek
n
o
lo
g
i M
alay
s
ia
f
o
r
f
u
n
d
in
g
th
is
r
esear
ch
.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
T
h
is
r
esear
ch
p
r
o
ject
r
ec
eiv
e
d
f
in
a
n
cial
s
u
p
p
o
r
t
f
r
o
m
th
e
Min
is
tr
y
o
f
Hig
h
er
E
d
u
ca
tio
n
Ma
lay
s
ia
u
n
d
er
t
h
e
Fu
n
d
a
m
en
tal
R
esear
ch
Gr
an
t Sch
em
e
(
FR
GS: PY
/
2
0
1
6
/0
7
2
5
1
)
.
AUTHO
R
CO
NT
RI
B
UT
I
O
NS ST
A
T
E
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