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ice
o
f
elec
tr
icity
[
4
]
–
[
6
]
.
Acc
u
r
ate
p
r
icin
g
p
r
e
d
ictio
n
with
m
an
y
m
o
r
e
lim
itatio
n
s
is
m
ad
e
p
o
s
s
ib
le
b
y
th
e
d
ev
elo
p
m
en
t
o
f
co
n
tem
p
o
r
ar
y
a
p
p
r
o
ac
h
es.
2.
RE
L
AT
E
D
WO
RK
S
Am
o
n
g
th
e
to
o
ls
n
o
w
av
ailab
le
f
o
r
p
r
e
d
ictio
n
ass
ig
n
m
en
ts
,
ar
tific
ial
n
e
u
r
al
n
etwo
r
k
s
(
ANN)
ar
e
a
well
-
k
n
o
wn
tec
h
n
iq
u
e
th
at
h
a
s
d
r
awn
in
c
r
ea
s
in
g
in
ter
est.
T
h
e
r
ea
s
o
n
is
th
at
it
p
er
f
o
r
m
s
well,
is
s
im
p
le
to
im
p
lem
en
t,
an
d
h
as
a
clea
r
m
o
d
elin
g
tec
h
n
iq
u
e
[
7
]
–
[
9
]
.
T
h
is
m
eth
o
d
u
s
es
p
ast
d
ata
to
id
en
tify
th
e
ch
ar
ac
ter
is
tics
th
at
wo
u
ld
s
u
it
a
p
r
ed
eter
m
i
n
ed
m
at
h
em
atica
l
f
o
r
m
u
la
a
n
d
th
e
n
u
s
es
th
e
g
en
er
ated
m
o
d
els
to
p
r
o
ject
f
u
tu
r
e
en
er
g
y
p
r
ices.
T
h
e
ac
tu
al
in
p
u
ts
f
ed
to
t
h
e
m
o
d
el
ar
e
g
o
i
n
g
t
o
d
eter
m
in
e
th
e
f
o
r
ec
ast.
Alth
o
u
g
h
th
is
ap
p
r
o
ac
h
is
r
elativ
ely
s
im
p
le
to
u
s
e,
it
is
u
n
a
b
le
to
ac
c
o
u
n
t
f
o
r
te
m
p
o
r
al
d
if
f
e
r
en
ce
s
s
u
ch
as
co
n
g
esti
o
n
an
d
co
n
tin
g
en
c
y
[
1
0
]
,
[
1
1
]
.
I
n
th
e
p
u
r
s
u
it
o
f
ac
cu
r
ate
ele
ctr
icity
p
r
ice
f
o
r
ec
asti
n
g
f
o
r
a
m
in
im
al
tim
e,
a
n
o
v
el
ad
ap
tiv
e
h
y
b
r
id
m
o
d
el
h
as b
ee
n
p
r
o
p
o
s
ed
b
y
c
o
m
b
in
in
g
v
ar
iatio
n
al
m
o
d
e
d
e
co
m
p
o
s
itio
n
(
VM
D)
,
s
elf
-
a
d
a
p
tiv
e
PS
O,
s
ea
s
o
n
al
au
to
r
eg
r
ess
iv
e
in
teg
r
ated
m
o
v
in
g
av
er
a
g
e
(
SAR
I
MA
)
,
an
d
d
ee
p
b
elief
n
etwo
r
k
(
DB
N)
alg
o
r
ith
m
s
an
d
f
o
r
ec
asted
r
esu
lts
wer
e
an
aly
ze
d
in
Z
h
an
g
et
a
l.
[
1
2
]
.
E
m
p
ir
ical
ev
alu
atio
n
s
d
em
o
n
s
tr
ate
th
at
th
is
in
teg
r
ated
ap
p
r
o
ac
h
s
ig
n
if
ican
tly
en
h
an
ce
s
f
o
r
ec
asti
n
g
ac
cu
r
ac
y
an
d
s
tab
ilit
y
.
C
o
n
cu
r
r
en
tly
,
W
a
n
g
et
a
l.
[
1
3
]
h
as
d
ev
elo
p
e
d
an
in
n
o
v
ativ
e
o
u
tli
er
-
r
o
b
u
s
t
n
eu
r
al
n
etwo
r
k
m
o
d
el
f
o
r
elec
tr
icity
p
r
ice
f
o
r
ec
as
tin
g
(
E
PF
)
,
wh
ic
h
s
y
n
er
g
izes
a
r
o
b
u
s
t
f
o
r
ec
asti
n
g
en
g
in
e
b
ased
o
n
a
n
o
u
tlier
-
r
esis
tan
t
ex
tr
em
e
lear
n
in
g
m
ac
h
in
e
(
E
L
M)
m
o
d
el
with
th
r
ee
n
o
v
el
alg
o
r
ith
m
s
.
A
k
ey
co
m
p
o
n
en
t
o
f
th
is
m
o
d
el
is
a
n
ewly
f
o
r
m
u
lated
s
in
e
co
s
in
e
alg
o
r
ith
m
(
SC
A)
to
o
p
tim
ize
th
e
s
elec
te
d
v
ar
iab
les
f
o
r
p
h
ase
s
p
ac
e
r
e
co
n
s
tr
u
ctio
n
.
Ad
d
itio
n
ally
,
th
e
au
th
o
r
s
d
is
cu
s
s
ed
th
e
n
ew
f
ea
tu
r
e
s
elec
tio
n
tech
n
iq
u
e
th
at
f
ac
ilit
ates
to
cr
ea
tio
n
o
f
th
e
m
o
s
t
r
elev
an
t
f
ea
tu
r
e
s
et
f
o
r
m
o
d
elin
g
elec
tr
icity
p
r
ices a
cc
u
r
ately
.
Fro
m
th
e
liter
atu
r
e,
it
is
f
o
u
n
d
th
at
ANNs
ar
e
p
o
p
u
lar
ly
a
d
o
p
ted
f
o
r
p
r
ice
p
r
ed
ictio
n
o
f
elec
tr
icity
s
in
ce
th
ey
wo
r
k
ef
f
ec
tiv
ely
f
o
r
n
o
n
lin
ea
r
r
elatio
n
s
h
ip
p
r
o
b
le
m
s
.
Ho
wev
er
,
c
o
n
v
e
n
tio
n
al
m
eth
o
d
s
s
u
ch
as
t
h
e
b
ac
k
-
p
r
o
p
a
g
atio
n
(
B
P)
alg
o
r
it
h
m
u
s
ed
f
o
r
tr
ain
in
g
ANNs
s
u
f
f
er
f
r
o
m
th
e
p
r
o
b
lem
o
f
s
lo
w
co
n
v
er
g
en
ce
r
ates
an
d
th
e
p
o
ten
tial
to
b
ec
o
m
e
tr
ap
p
ed
in
lo
ca
l
o
p
tim
a.
Ad
d
r
ess
in
g
th
ese
lim
itatio
n
s
,
C
h
en
et
a
l.
[
1
4
]
h
as
in
tr
o
d
u
ce
d
a
q
u
ick
p
r
ice
p
r
e
d
i
ctio
n
m
eth
o
d
f
o
r
th
e
elec
tr
icit
y
m
ar
k
et
u
s
in
g
E
L
M,
a
r
ec
e
n
tly
em
er
g
ed
lea
r
n
in
g
alg
o
r
ith
m
f
o
r
s
in
g
le
-
lay
er
f
ee
d
f
o
r
war
d
n
eu
r
al
n
etwo
r
k
(
SL
FN)
,
wh
ich
o
v
er
co
m
es
th
e
in
h
er
en
t
d
r
awb
ac
k
s
o
f
th
e
B
P a
lg
o
r
ith
m
.
Mir
jalili
[
1
5
]
p
r
o
p
o
s
ed
a
SC
A
wh
ich
wo
r
k
s
ef
f
icien
tly
o
n
co
m
p
licated
o
p
tim
izatio
n
p
r
o
b
lem
s
.
I
t
in
itializes
n
u
m
er
o
u
s
r
a
n
d
o
m
v
ar
iab
les
an
d
attain
s
th
e
s
o
lu
tio
n
to
war
d
th
e
g
lo
b
al
o
p
t
im
u
m
th
r
o
u
g
h
th
e
f
u
n
ctio
n
s
b
ased
o
n
s
in
e
an
d
co
s
in
e
eq
u
atio
n
s
.
E
m
p
i
r
ical
r
esu
lts
an
d
p
er
f
o
r
m
a
n
ce
m
etr
i
cs
d
em
o
n
s
tr
ate
th
e
SC
A
's
ab
ilit
y
to
e
x
p
lo
r
e
d
iv
e
r
s
e
r
eg
io
n
s
o
f
t
h
e
s
ea
r
ch
s
p
ac
e
ef
f
ec
tiv
ely
,
a
v
o
id
p
r
e
m
atu
r
e
c
o
n
v
er
g
en
ce
t
o
lo
ca
l
o
p
tim
a,
an
d
ef
f
icien
tl
y
ex
p
l
o
it
p
r
o
m
is
in
g
r
eg
io
n
s
,
m
ak
in
g
it
a
wid
ely
ad
o
p
ted
o
p
tim
iz
atio
n
tech
n
iq
u
e
in
v
ar
io
u
s
r
esear
ch
d
o
m
ain
s
in
R
izk
-
Allah
an
d
Hass
an
ien
[
1
6
]
.
An
SC
A
alg
o
r
ith
m
with
a
m
u
lti
-
m
ec
h
an
is
m
v
ar
ian
t th
at
ca
n
s
o
lv
e
th
e
m
u
lt
id
im
en
s
io
n
al
p
r
o
b
lem
is
d
ev
el
o
p
ed
to
a
d
d
r
ess
th
e
p
r
e
m
atu
r
e
co
n
v
er
g
en
ce
wh
ile
d
er
iv
in
g
a
s
o
lu
tio
n
f
o
r
s
ix
co
n
s
tr
ain
ed
n
o
n
lin
ea
r
p
r
o
b
lem
s
.
T
h
e
r
esu
lts
ar
e
co
m
p
ar
ed
wi
th
r
esu
lts
f
r
o
m
th
e
ex
p
er
im
en
tal
s
etu
p
to
ch
ec
k
th
e
q
u
ality
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
i
n
Yan
g
et
a
l.
[
1
7
]
.
A
h
y
b
r
id
m
o
d
el
is
d
esig
n
ed
b
y
co
m
b
in
in
g
th
e
SC
A
an
d
h
ill
clim
b
in
g
o
p
tim
izer
to
f
i
n
d
lo
a
d
d
is
p
atch
p
att
er
n
s
.
T
h
e
p
r
o
p
o
s
ed
h
y
b
r
id
m
o
d
el
im
p
r
o
v
es
its
ex
p
lo
itatio
n
ab
ilit
y
f
o
r
SC
A
to
ef
f
ec
tiv
ely
s
o
lv
e
th
e
lo
ad
d
is
p
atch
p
r
o
b
lem
,
th
is
m
o
d
el
is
test
ed
with
v
a
r
io
u
s
r
ea
l
-
tim
e
p
r
o
b
lem
s
an
d
r
esu
lts
wer
e
co
m
p
ar
ed
in
Al
-
B
etar
et
a
l.
[
1
8
]
.
A
h
y
b
r
id
n
etwo
r
k
b
y
co
m
b
in
in
g
th
e
SC
A
an
d
m
ar
in
e
p
r
ed
ato
r
alg
o
r
ith
m
s
is
b
u
ilt
to
c
h
o
o
s
e
th
e
b
est
-
s
u
ited
v
alu
e
o
f
th
e
p
ar
am
eter
s
f
o
r
h
y
b
r
i
d
ac
tiv
e
p
o
wer
f
ilter
s
.
T
h
e
p
er
f
o
r
m
an
ce
ev
alu
atio
n
o
f
th
e
d
ev
elo
p
ed
n
etwo
r
k
is
an
aly
ze
d
with
r
esu
lts
o
f
alr
ea
d
y
p
r
o
v
en
n
etwo
r
k
m
o
d
els in
Ali
et
a
l.
[
1
9
]
.
Similar
ly
,
d
u
r
in
g
th
e
p
ast
y
ea
r
s
,
r
esear
ch
er
s
h
a
v
e
m
e
r
g
ed
a
n
d
ap
p
lied
a
v
ar
iety
o
f
n
eu
r
al
n
etwo
r
k
an
d
o
p
tim
izatio
n
alg
o
r
ith
m
s
f
o
r
f
o
r
ec
asti
n
g
an
d
p
r
e
d
ictio
n
ap
p
licatio
n
s
in
Ud
aiy
ak
u
m
ar
an
d
Victo
r
ie
[
2
0
]
.
A
h
y
b
r
id
m
o
d
el
with
m
u
ltil
ay
er
p
er
ce
p
tr
o
n
wh
ich
is
tr
ain
ed
b
y
E
L
M
an
d
o
p
tim
ized
b
y
P
SO
is
r
ec
o
m
m
en
d
ed
f
o
r
p
r
e
d
ictin
g
th
e
co
s
t
o
f
elec
tr
icity
,
its
r
esu
lt
s
wer
e
co
m
p
ar
ed
with
v
ar
io
u
s
o
th
e
r
f
o
r
e
ca
s
tin
g
m
eth
o
d
s
in
Ud
aiy
ak
u
m
ar
et
a
l.
[
2
1
]
.
Fo
r
t
h
e
p
r
ed
ictio
n
o
f
I
r
an
’
s
d
aily
el
ec
tr
icity
p
r
ice,
a
n
ew
alg
o
r
ith
m
b
y
co
m
b
in
in
g
th
e
co
n
v
o
l
u
tio
n
al
n
eu
r
al
n
etwo
r
k
an
d
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
n
etwo
r
k
is
p
r
o
p
o
s
ed
an
d
f
o
u
n
d
to
p
r
o
d
u
ce
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
.
5
,
Octo
b
e
r
20
25
:
4
3
6
6
-
4
3
7
5
4368
ac
cu
r
ate
p
r
ed
ictio
n
in
Heid
ar
p
an
ah
[
2
2
]
.
I
n
th
is
p
ap
er
,
w
e
ar
e
g
o
in
g
to
d
is
cu
s
s
th
e
m
o
d
elin
g
o
f
a
n
o
v
el
alg
o
r
ith
m
t
h
at
is
d
e
v
elo
p
e
d
b
y
c
o
m
b
in
in
g
th
e
E
L
M
an
d
SC
A
f
o
r
elec
tr
icity
p
r
ice
f
o
r
ec
asti
n
g
,
b
o
th
a
r
e
s
elec
ted
d
u
e
to
th
eir
s
im
p
licity
,
h
ig
h
p
r
o
b
lem
-
s
o
lv
in
g
ab
ilit
y
an
d
n
u
m
b
er
o
f
v
ar
io
u
s
m
o
d
els av
ailab
ilit
y
.
3.
T
H
E
O
R
E
T
I
CA
L
B
ACK
G
R
O
UND
T
h
e
n
etwo
r
k
m
o
d
el
wh
ich
is
f
ee
d
-
f
o
r
war
d
[
2
3
]
co
n
s
is
ts
o
f
s
ix
in
p
u
t
lay
er
s
o
f
n
e
u
r
o
n
s
,
th
r
ee
h
id
d
e
n
lay
er
s
ea
ch
with
f
if
teen
n
eu
r
o
n
s
,
an
d
o
n
e
n
eu
r
o
n
as
an
o
u
tp
u
t
lay
er
is
m
o
d
eled
to
s
o
l
v
e
th
e
co
n
s
id
er
ed
p
r
o
b
lem
s
tatem
en
t
o
f
elec
tr
ici
ty
f
o
r
ec
asti
n
g
.
Fig
u
r
e
1
d
ep
ict
s
th
e
p
r
o
p
o
s
ed
n
e
u
r
al
n
etwo
r
k
'
s
to
p
o
lo
g
y
.
A
n
ew
m
o
d
el
is
d
ev
elo
p
ed
b
y
co
m
b
i
n
in
g
an
E
L
M
an
d
SC
A
to
tr
ai
n
th
e
n
etwo
r
k
.
T
h
is
s
ec
tio
n
d
escr
ib
es
in
d
etail
th
e
wo
r
k
in
g
o
f
E
L
M
a
n
d
SC
A.
E
L
M
is
s
e
lecte
d
b
ec
au
s
e
o
f
it
s
s
im
p
le
y
et
p
o
wer
f
u
l
g
en
er
aliza
tio
n
ca
p
ab
ilit
y
also
th
e
tr
ain
in
g
s
p
ee
d
is
v
er
y
h
ig
h
wh
en
co
m
p
a
r
ed
with
m
o
s
t
o
f
th
e
n
eu
r
al
n
etw
o
r
k
tr
ai
n
in
g
alg
o
r
ith
m
s
.
E
L
M
g
iv
es
f
lex
i
b
ilit
y
in
ch
o
o
s
in
g
th
e
weig
h
ts
an
d
b
ias
o
f
ex
p
ec
tin
g
t
h
e
f
in
al
h
id
d
e
n
lay
er
;
to
ef
f
ec
tiv
ely
o
p
tim
ize
th
ese
weig
h
ts
an
d
th
e
b
ias
o
p
tim
izatio
n
alg
o
r
it
h
m
co
m
es
in
t
o
p
lay
.
I
n
th
i
s
p
ap
er
Sin
e
C
o
s
in
e
Alg
o
r
ith
m
is
u
s
ed
as
an
o
p
tim
izatio
n
al
g
o
r
ith
m
b
ec
a
u
s
e
o
f
its
h
ig
h
er
v
a
r
iab
le
h
an
d
lin
g
ca
p
ac
ity
,
f
aster
co
n
v
er
g
en
ce
a
n
d
s
im
p
le
m
eth
o
d
o
l
o
g
y
.
Fig
u
r
e
1
.
Neu
r
al
n
etwo
r
k
m
o
d
el
f
o
r
E
PF
3
.
1
.
E
x
t
re
m
e
lea
rning
ma
chine
T
h
e
f
o
llo
win
g
is
t
h
e
eq
u
atio
n
o
f
th
e
o
u
tp
u
t
lay
er
f
u
n
ctio
n
with
th
e
in
p
u
t
v
ar
iab
le
b
e
,
b
e
th
e
n
etwo
r
k
'
s
f
in
al
o
u
tp
u
t f
o
r
ℎ
n
u
m
b
er
o
f
h
id
d
en
n
e
u
r
o
n
s
,
=
(
+
∑
ℎ
=
1
)
(
1
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t
h
e
v
ar
iab
le
,
,
(
.
)
d
en
o
tes
th
e
we
ig
h
t
ass
ig
n
ed
b
etwe
en
th
e
in
p
u
t
lay
er
an
d
h
id
d
e
n
lay
er
,
h
id
d
en
lay
er
b
ias
an
d
ac
tiv
atio
n
f
u
n
ctio
n
r
esp
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tiv
ely
f
o
r
n
n
u
m
b
er
o
f
in
p
u
t
v
ar
iab
les.
I
n
ad
d
itio
n
,
n
ew
v
ar
iab
le
is
ass
ig
n
ed
f
o
r
th
e
weig
h
t
o
f
th
e
co
n
n
ec
tio
n
s
b
etwe
en
th
e
h
id
d
en
lay
er
an
d
o
u
tp
u
t
lay
er
[
2
4
]
.
T
h
e
eq
u
atio
n
o
f
th
e
h
id
d
e
n
lay
er
n
eu
r
o
n
s
o
u
tp
u
t is g
iv
en
as
(
2
)
,
=
(
+
∑
=
1
)
(
2
)
T
h
e
ac
tiv
atio
n
f
u
n
ctio
n
o
f
th
e
o
u
tp
u
t
n
eu
r
o
n
is
g
iv
e
n
b
y
th
e
(
3
)
an
d
t
h
e
v
ec
to
r
f
o
r
m
o
f
t
h
e
(
3
)
is
g
iv
e
n
b
y
th
e
(
4
)
.
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
A
h
yb
r
id
ex
tr
eme
lea
r
n
in
g
ma
ch
in
e
a
n
d
s
in
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a
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ith
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mo
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…
(
Ud
a
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k
u
ma
r
S
a
mb
a
th
k
u
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r
)
4369
=
∑
ℎ
=
1
(
3
)
=
(
)
(
4
)
T
h
e
n
etwo
r
k
o
u
tp
u
t
v
ec
to
r
is
=
[
(
1
)
,
(
2
)
,
⋯
,
(
)
]
an
d
th
e
weig
h
t
v
ec
to
r
is
=
[
1
,
2
,
⋯
,
ℎ
]
,
T
h
e
m
atr
ix
o
f
th
e
h
id
d
en
lay
er
o
u
t
p
u
t
is
g
iv
e
n
b
y
(
5
)
a
n
d
th
e
I
n
p
u
t
weig
h
t
an
d
b
ias
m
atr
ix
ar
e
f
r
am
ed
as
s
h
o
wn
in
(
6
)
.
=
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1
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1
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1
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)
⋮
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1
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(
2
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⋯
ℎ
(
)
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(
5
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=
[
1
2
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ℎ
11
12
⋯
1
ℎ
⋮
⋮
⋱
⋮
1
2
⋯
ℎ
]
(
6
)
Ou
tp
u
t
weig
h
t
is
esti
m
ated
as
s
h
o
wn
in
(
7
)
,
wh
er
e
†
th
e
g
en
er
alize
d
in
v
er
s
e
o
f
th
e
o
u
tp
u
t
m
atr
i
x
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d
ev
elo
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e
d
b
y
th
e
Mo
o
r
e
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Pen
r
o
s
e
an
d
=
[
(
1
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(
2
)
,
⋯
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(
)
]
is
th
e
d
esire
d
o
u
tp
u
t a
n
d
it
is
g
iv
e
n
b
y
(
8
)
.
0
=
†
(
7
)
†
=
(
)
−
1
(
8
)
B
y
s
u
b
s
titu
tin
g
(
7
)
in
(
6
)
0
is
esti
m
ated
b
y
least
-
s
q
u
ar
es so
lu
ti
o
n
.
0
=
(
)
−
1
(
9
)
3
.
2
.
Sin
e
co
s
ine a
lg
o
rit
hm
A
n
ew
p
o
p
u
latio
n
-
b
ased
alg
o
r
ith
m
ca
lled
as
s
in
e
co
s
i
n
e
alg
o
r
ith
m
(
SC
A)
is
d
ev
elo
p
ed
b
y
in
co
r
p
o
r
atin
g
th
e
co
n
ce
p
t
o
f
b
asic
wav
ef
o
r
m
s
o
f
s
in
e
an
d
co
s
f
u
n
ctio
n
s
.
T
h
is
alg
o
r
ith
m
is
wid
ely
u
s
ed
b
y
r
esear
ch
er
s
d
u
e
it
its
s
im
p
licity
an
d
its
p
o
wer
f
u
l
o
p
tim
izatio
n
[
2
5
]
–
[
2
7
]
.
SC
A
is
ca
r
ef
u
lly
d
esig
n
ed
to
a
d
ju
s
t
th
e
weig
h
ts
o
f
t
h
e
co
n
n
ec
tio
n
estab
lis
h
ed
b
etwe
en
in
p
u
t
an
d
h
id
d
en
lay
er
s
to
im
p
r
o
v
e
th
e
f
o
r
ec
asti
n
g
ca
p
ab
ilit
y
o
f
th
e
p
r
o
p
o
s
ed
h
y
b
r
id
m
o
d
el.
I
n
th
is
tech
n
iq
u
e,
t
h
e
r
an
d
o
m
ly
s
elec
ted
p
ar
ticles
tr
av
el
to
war
d
s
th
e
b
est p
o
s
s
ib
le
s
o
lu
tio
n
.
T
h
e
f
o
l
lo
win
g
eq
u
atio
n
is
u
s
ed
to
r
a
n
d
o
m
ly
in
itialize
th
e
ap
p
r
o
p
r
iat
e
p
o
p
u
latio
n
s
ize.
=
+
(
−
)
×
(
1
0
)
W
h
er
e
R
kl
is
v
a
r
iab
le,
LL
is
t
h
e
lo
wer
lim
it
an
d
UL
u
p
p
er
lim
it.
r
is
ass
ig
n
ed
as
th
e
r
an
d
o
m
n
u
m
b
er
wh
o
s
e
v
alu
e
lies
b
etwe
en
0
an
d
1
.
E
ac
h
v
ar
ia
b
le
is
in
itialized
a
n
d
th
e
s
am
e
is
u
s
ed
to
ca
lcu
l
ate
th
e
o
u
tp
u
t
f
r
o
m
wh
ich
th
e
b
est
v
ar
iab
le
is
s
el
ec
ted
.
T
h
en
ea
ch
v
ar
ia
b
le
is
u
p
d
ated
u
s
in
g
th
e
s
in
e
co
s
in
e
f
u
n
ctio
n
wh
ich
is
s
h
o
wn
in
(
11
)
a
n
d
(
12
)
.
+
1
=
+
1
×
(
2
)
×
|
3
−
|
(
1
1
)
+
1
=
+
1
×
(
2
)
×
|
3
−
|
(
1
2
)
I
n
(
1
1
)
an
d
(
1
2
)
,
R
i
is
th
e
cu
r
r
en
t
iter
atio
n
v
a
r
iab
le,
R
b
is
th
e
o
v
er
all
b
est
v
ar
iab
le,
a
n
d
1
,
2
,
an
d
3
ar
e
r
an
d
o
m
v
ar
iab
les
ch
o
s
en
b
et
wee
n
0
an
d
1
.
Fo
r
th
e
im
p
le
m
en
tatio
n
o
f
a
r
an
d
o
m
s
elec
tio
n
o
f
s
in
o
r
co
s
in
e
f
u
n
ctio
n
,
eq
u
atio
n
s
(
11
)
an
d
(
12
)
ar
e
co
m
b
i
n
ed
as
(
1
3
)
.
+
1
=
{
+
1
×
(
2
)
×
|
3
−
|
,
4
<
0
.
5
+
1
×
(
2
)
×
|
3
−
|
,
4
≥
0
.
5
(
1
3
)
T
h
e
v
alu
e
o
f
4
is
ch
o
s
en
b
etwe
en
0
a
n
d
1
.
T
h
e
u
p
d
ated
v
ar
ia
b
le
is
u
s
ed
in
th
e
n
ex
t
iter
atio
n
an
d
all
th
e
s
tep
s
ar
e
ca
r
r
ied
o
u
t u
n
til a
s
p
ec
if
ied
n
u
m
b
er
o
f
iter
atio
n
s
ar
e
ca
r
r
ied
o
u
t.
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
.
5
,
Octo
b
e
r
20
25
:
4
3
6
6
-
4
3
7
5
4370
4.
P
RO
P
O
SE
D
H
YB
R
I
D
A
L
G
O
RIT
H
M
E
L
M
an
d
SC
A
alg
o
r
ith
m
s
ar
e
co
m
b
in
e
d
in
s
u
ch
a
way
t
o
f
o
r
m
a
h
y
b
r
i
d
f
o
r
ec
asti
n
g
m
o
d
el
with
h
ig
h
ac
cu
r
ac
y
.
B
o
th
alg
o
r
ith
m
s
h
a
v
e
s
p
ec
if
ic
r
o
les
to
p
lay
with
th
eir
h
ig
h
lig
h
ts
.
T
h
e
weig
h
ts
b
etwe
en
th
e
h
id
d
e
n
lay
er
an
d
o
u
tp
u
t la
y
er
ar
e
ca
lc
u
lated
u
s
in
g
th
e
E
L
M
tech
n
iq
u
e
wh
ile
th
e
weig
h
ts
o
f
th
e
lin
k
b
etwe
en
th
e
in
p
u
t
an
d
h
id
d
en
lay
er
s
ar
e
r
an
d
o
m
l
y
g
en
er
ate
d
,
wh
ich
ar
e
o
p
tim
i
ze
d
b
y
SC
A,
an
d
b
y
th
is
b
o
th
E
L
M
an
d
SC
A
ar
e
co
m
b
in
ed
to
f
o
r
m
th
e
p
o
we
r
f
u
l
h
y
b
r
id
f
o
r
ec
asti
n
g
m
o
d
e
l.
T
h
is
ev
alu
atio
n
o
f
th
e
b
e
s
t
v
alu
e
o
f
weig
h
t
b
etwe
en
th
e
h
id
d
en
lay
e
r
an
d
th
e
o
u
tp
u
t
lay
er
is
a
m
in
im
iz
atio
n
p
r
o
b
lem
[
2
8
]
.
T
h
e
E
u
cli
d
ea
n
n
o
r
m
u
s
ed
t
o
f
in
d
th
e
m
i
n
im
u
m
n
o
r
m
is
g
iv
en
b
y
t
h
e
eq
u
atio
n
.
(
‖
−
‖
2
)
(
1
4
)
T
h
e
eq
u
atio
n
(
1
4
)
ca
n
b
e
m
o
d
if
ied
b
y
ad
d
i
n
g
th
e
o
u
t
p
u
t
w
eig
h
t
m
atr
ix
with
t
h
e
r
e
g
u
lar
i
za
tio
n
p
ar
am
ete
r
wh
ich
will b
e
alwa
y
s
g
r
ea
ter
t
h
an
ze
r
o
,
it is
g
iv
en
in
(
15
)
an
d
its
s
o
lu
tio
n
is
g
iv
en
in
(
16
)
.
(
‖
−
‖
2
+
‖
0
‖
2
)
(
1
5
)
0
=
(
+
)
−
1
(
1
6
)
W
h
er
e
is
th
e
id
en
tity
m
atr
ix
.
T
h
e
o
p
tim
izatio
n
p
r
o
b
lem
o
f
t
h
e
p
r
o
p
o
s
ed
SLFN
is
th
e
m
in
im
izatio
n
f
u
n
ctio
n
wh
o
s
e
o
b
jectiv
e
f
u
n
ctio
n
is
as
g
iv
en
in
(
17
)
.
=
(
,
)
(
1
7
)
B
y
esti
m
atin
g
th
e
er
r
o
r
f
u
n
cti
o
n
f
o
r
ac
tu
al
o
u
tp
u
t
(
y
d
)
a
n
d
p
r
ed
icted
o
u
tp
u
t
(
y
)
t
h
e
ac
cu
r
ac
y
o
f
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
f
o
r
p
r
ice
f
o
r
ec
asti
n
g
.
R
o
o
t
m
ea
n
s
q
u
a
r
e
er
r
o
r
(
R
M
SE)
is
ca
lcu
lated
b
y
(
1
8
)
.
(
,
)
=
√
1
∑
[
(
)
−
(
)
]
2
=
1
(
1
8
)
Du
r
in
g
th
e
ex
ec
u
tio
n
o
f
o
p
tim
izatio
n
,
in
d
i
v
id
u
al
p
ar
am
ete
r
s
will
b
e
co
n
s
titu
ted
b
y
(
19
)
in
wh
ich
s
j
is
an
in
teg
er
v
ar
ia
b
le
th
at
d
ef
i
n
es th
e
ac
tiv
atio
n
f
u
n
ctio
n
.
Ou
t
p
u
t l
ay
er
ac
tiv
atio
n
f
u
n
ctio
n
f
j
is
g
iv
en
as
(
1
9
)
.
=
[
11
,
⋯
,
1
ℎ
,
1
,
⋯
,
ℎ
,
1
,
⋯
,
ℎ
,
]
(
1
9
)
(
)
(
)
(
)
(
)
(
)
=
=
+
−
−
=
−
+
−
−
=
−
+
=
=
4
3
)
2
(
1
/
)
2
(
1
2
)
(
)
(
/
)
(
)
(
1
)
(
1
/
1
0
0
)
(
j
j
j
j
j
j
s
if
v
s
if
v
e
v
e
s
if
v
e
v
e
v
e
v
e
s
if
v
e
s
if
v
f
(
2
0
)
T
h
e
h
id
d
en
lay
e
r
ca
n
b
e
ad
j
u
s
ted
b
ased
o
n
th
e
v
alu
e
ass
ig
n
ed
to
th
e
p
ar
a
m
eter
s
j
,
th
e
f
o
llo
win
g
ac
tiv
atio
n
f
u
n
ctio
n
is
s
elec
ted
f
o
r
v
ar
i
o
u
s
v
alu
es
o
f
s
wh
en
s
j
=
0
th
at
p
ar
ticu
lar
n
eu
r
o
n
is
n
o
t
co
n
s
id
er
e
d
,
s
ig
m
o
id
f
u
n
ctio
n
,
tan
g
en
t f
u
n
ctio
n
,
h
y
p
er
b
o
lic
f
u
n
ctio
n
,
a
n
d
lin
ea
r
is
s
elec
ted
as
ac
tiv
atio
n
f
u
n
ctio
n
with
s
j
as
1
,
2
,
3
,
4
r
esp
ec
tiv
ely
.
I
m
p
le
m
en
tatio
n
o
f
th
e
a
d
ju
s
tab
le
h
id
d
en
la
y
er
b
y
f
iv
e
d
if
f
er
en
t
ac
tiv
atio
n
f
u
n
ctio
n
s
an
d
a
co
m
b
in
atio
n
o
f
SC
A
a
n
d
E
L
M
ar
e
th
e
n
o
v
elty
o
f
t
h
is
wo
r
k
.
T
h
e
f
lo
wch
ar
t
o
f
t
h
e
p
r
o
p
o
s
ed
h
y
b
r
i
d
ex
tr
em
e
lear
n
in
g
m
ac
h
i
n
e
–
s
in
e
co
s
in
e
alg
o
r
ith
m
(ELM
–
SC
A)
alg
o
r
ith
m
is
s
h
o
wn
i
n
th
e
F
ig
u
r
e
2.
4
.
1
.
Select
io
n o
f
t
ra
ini
ng
da
t
a
I
n
th
is
p
ap
er
h
y
b
r
id
izatio
n
o
f
SC
A
an
d
E
L
M
alg
o
r
ith
m
s
i
s
u
s
ed
f
o
r
tr
ain
in
g
th
e
p
r
o
p
o
s
ed
n
eu
r
al
n
etwo
r
k
m
o
d
el.
T
h
e
an
n
u
al
el
ec
tr
icity
p
r
ice
p
atter
n
d
ata
s
et
f
o
r
th
e
y
ea
r
2
0
2
2
h
as
b
ee
n
ch
o
s
en
to
v
alid
ate
th
e
p
r
o
p
o
s
ed
n
etwo
r
k
m
o
d
el
an
d
alg
o
r
ith
m
.
T
h
e
p
r
ice
d
ata
f
o
r
1
2
m
o
n
th
s
a
r
e
d
i
v
id
ed
in
to
th
r
ee
s
ets
o
f
d
ata
in
v
o
lv
in
g
s
ea
s
o
n
al
v
ar
iatio
n
s
,
f
o
r
th
e
in
clu
s
io
n
o
f
th
e
r
eg
i
o
n
al
v
ar
iatio
n
I
n
d
ian
elec
tr
icity
m
ar
k
et
p
r
ice
is
s
elec
ted
.
T
h
e
f
ir
s
t
s
et
o
f
d
ata
is
elec
tr
icity
p
r
ices
f
o
r
J
an
u
ar
y
,
Feb
r
u
ar
y
,
an
d
Ma
r
ch
I
n
d
ian
elec
tr
icity
m
ar
k
ed
f
o
r
t
h
e
y
ea
r
2
0
2
2
an
d
it
is
s
h
o
wn
in
Fig
u
r
e
3
.
Similar
ly
,
th
e
s
ec
o
n
d
d
ata
i
n
clu
d
e
th
e
p
r
ic
e
d
ata
s
et
o
f
Ma
y
,
J
u
n
e,
an
d
J
u
ly
an
d
is
s
h
o
wn
in
Fig
u
r
e
4
,
an
d
th
e
p
r
ice
o
f
Sep
tem
b
er
,
Octo
b
er
,
an
d
No
v
e
m
b
er
co
m
p
r
is
es
th
e
th
ir
d
d
ata
s
et
wh
ich
is
s
h
o
wn
in
Fig
u
r
e
4
.
T
h
e
tr
ain
i
n
g
d
ata
will
b
e
th
e
d
ata
f
o
r
th
r
ee
m
o
n
th
s
an
d
p
r
ice
f
o
r
ec
asti
n
g
is
d
o
n
e
f
o
r
n
ex
t m
o
n
th
f
o
r
o
n
e
wee
k
f
o
r
ea
ch
s
et.
I
n
th
is
r
esear
ch
th
r
ee
s
ets o
f
d
ata
ar
e
tr
ain
ed
an
d
s
h
o
wn
in
Fig
u
r
e
s
3
,
4
,
5
r
esp
e
ctiv
ely
.
T
h
e
g
r
ap
h
o
b
tain
ed
s
h
o
ws
th
e
p
atter
n
,
t
h
e
v
a
r
iatio
n
o
f
elec
tr
icity
p
r
ice
with
to
tim
e
in
h
o
u
r
s
f
o
r
a
th
r
e
e
-
m
o
n
th
d
u
r
atio
n
o
f
2
,
1
6
0
h
o
u
r
s
(
2
4
*
(
3
1
+2
8
+3
1
)
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
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p
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n
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N:
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h
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u
r
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2
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Flo
wch
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t
o
f
th
e
p
r
o
p
o
s
ed
h
y
b
r
id
alg
o
r
ith
m
Fig
u
r
e
3.
E
lectr
icity
p
r
ice
d
ata
f
o
r
tr
ain
i
n
g
(
d
ata
s
et
1
)
Fig
u
r
e
4
.
E
lectr
icity
p
r
ice
d
ata
f
o
r
tr
ain
i
n
g
(
d
ata
s
et
2
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
:
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p
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g
,
Vo
l.
15
,
No
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5
,
Octo
b
e
r
20
25
:
4
3
6
6
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4
3
7
5
4372
Fig
u
r
e
5
.
E
lectr
icity
p
r
ice
d
ata
f
o
r
tr
ain
i
n
g
(
d
ata
s
et
3
)
5.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
elec
tr
icity
p
r
ice
f
o
r
ec
asti
n
g
is
d
o
n
e
u
s
in
g
th
e
p
r
o
p
o
s
ed
h
y
b
r
id
m
o
d
el
u
s
in
g
MA
T
L
A
B
R
2
0
2
1
a
in
W
in
d
o
ws
1
0
with
I
n
tel
co
r
e
i5
7
t
h
g
e
n
er
atio
n
an
d
1
6
GB
R
AM
.
Sin
ce
th
e
p
r
o
p
o
s
ed
h
y
b
r
id
n
etwo
r
k
m
o
d
e
l
is
s
im
p
le
an
d
p
o
wer
f
u
l,
m
o
d
er
ate
co
m
p
u
tatio
n
al
d
ev
ices
ar
e
en
o
u
g
h
to
r
u
n
th
e
p
r
o
p
o
s
ed
m
o
d
el
an
d
th
e
d
ev
ice
u
s
ed
s
m
o
o
t
h
ly
r
u
n
s
th
e
p
r
o
p
o
s
ed
m
o
d
el.
On
e
wee
k
o
f
d
ata
is
f
o
r
ec
asted
an
d
v
ar
io
u
s
er
r
o
r
m
etr
ics
ar
e
ca
lcu
lated
b
y
u
s
in
g
th
e
f
o
r
ec
asted
elec
tr
icity
p
r
ice
v
alu
e
a
n
d
o
r
ig
in
al
elec
tr
icity
p
r
ice
v
alu
e.
T
h
ese
m
etr
ics
ar
e
th
e
p
ar
am
eter
s
g
e
n
er
ally
u
s
ed
f
o
r
th
e
ev
alu
atio
n
o
f
th
e
ac
cu
r
ac
y
o
f
f
o
r
ec
asted
p
r
ices.
T
h
e
p
r
ice
f
o
r
ec
asti
n
g
is
o
b
tain
ed
an
d
th
e
ab
o
v
e
-
m
e
n
tio
n
ed
e
r
r
o
r
m
etr
ics
wer
e
ca
lcu
lated
b
ased
o
n
th
e
tr
ain
in
g
s
et
d
ata
o
f
2
1
6
0
h
o
u
r
s
with
th
r
ee
m
eth
o
d
s
n
am
ely
th
e
b
ac
k
-
p
r
o
p
a
g
atio
n
(
B
P)
alg
o
r
ith
m
,
th
e
E
x
tr
e
m
e
lear
n
in
g
m
eth
o
d
(
E
L
M)
,
an
d
also
p
r
o
p
o
s
ed
h
y
b
r
id
m
o
d
el.
T
h
e
co
m
p
ar
is
o
n
p
lo
t
is
s
h
o
wn
in
th
e
Fig
u
r
e
6
f
o
r
Ap
r
il
m
o
n
th
1
6
8
h
o
u
r
s
.
Fro
m
T
ab
le
1
th
e
er
r
o
r
m
etr
ics
MSE
an
d
R
MSE
wer
e
f
o
u
n
d
to
b
e
r
ed
u
ce
d
th
u
s
p
r
o
m
is
in
g
g
o
o
d
ac
cu
r
ac
y
in
p
r
ed
ictio
n
.
T
h
e
p
r
ice
f
o
r
ec
asti
n
g
an
d
er
r
o
r
m
etr
ics
ar
e
ev
alu
ated
b
ased
o
n
th
e
tr
ain
in
g
s
et
d
ata
o
f
th
e
n
ex
t
th
r
ee
m
o
n
th
s
(
Ma
y
,
J
u
n
e
,
an
d
J
u
ly
)
with
th
e
s
am
e
m
o
d
el
as
th
e
p
r
ev
io
u
s
d
ata
s
et.
T
h
e
c
o
m
p
a
r
is
o
n
p
l
o
t
is
s
h
o
wn
in
Fig
u
r
e
7
f
o
r
th
e
f
ir
s
t
wee
k
o
f
Au
g
u
s
t
m
o
n
th
.
Fro
m
T
a
b
le
2
i
t
is
f
o
u
n
d
th
at
f
o
r
t
h
is
p
ar
ticu
l
ar
s
et,
er
r
o
r
m
etr
ics
ar
e
less
th
an
th
e
B
P
alg
o
r
ith
m
b
u
t
s
lig
h
tly
g
r
ea
ter
th
an
E
L
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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
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n
g
,
Vo
l.
15
,
No
.
5
,
Octo
b
e
r
20
25
:
4
3
6
6
-
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4374
6.
CO
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SI
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ar
e
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u
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t
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g
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y
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ested
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k
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p
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e
m
ai
n
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en
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tech
n
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e
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eir
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p
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y
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lcu
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n
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es
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w
th
em
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icie
n
tly
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tr
ac
t
m
o
r
e
p
r
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is
e
r
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lts
f
r
o
m
ex
tr
e
m
ely
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latile
p
r
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g
d
ata
s
ets
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y
cr
ea
tin
g
th
e
v
ar
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h
id
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e
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r
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s
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d
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en
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th
e
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s
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f
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f
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ea
ch
n
e
u
r
o
n
f
o
r
all
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e
h
id
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s
.
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h
e
o
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tc
o
m
es
d
em
o
n
s
tr
ate
th
e
ef
f
icac
y
o
f
th
is
s
u
g
g
ested
m
eth
o
d
f
o
r
ac
cu
r
ate
o
n
lin
e
p
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f
o
r
ec
ast
in
g
in
s
p
o
t
m
ar
k
et
an
aly
s
is
.
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t
is
f
o
u
n
d
th
at
th
e
v
ar
iab
les
in
f
lu
e
n
cin
g
p
r
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p
r
ed
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n
ar
e
tim
e
a
n
d
th
e
p
atter
n
o
f
elec
tr
icity
d
em
an
d
.
T
h
i
s
s
o
r
t
o
f
r
ea
l
-
wo
r
ld
ac
cu
r
ate
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tr
icity
p
r
ice
f
o
r
e
ca
s
tin
g
will
h
elp
th
e
elec
tr
ic
ity
m
ar
k
et
p
ar
ticip
an
ts
in
a
b
etter
b
id
d
in
g
an
d
s
ellin
g
p
r
o
ce
s
s
an
d
f
o
r
th
e
c
o
n
s
u
m
er
as a
r
ed
u
ce
d
elec
tr
icity
b
ill.
RE
F
E
R
E
NC
E
S
[
1
]
E.
D
ü
t
s
c
h
k
e
a
n
d
A
.
G
.
P
a
e
t
z
,
“
D
y
n
a
mi
c
e
l
e
c
t
r
i
c
i
t
y
p
r
i
c
i
n
g
-
W
h
i
c
h
p
r
o
g
r
a
ms
d
o
c
o
n
su
mers
p
r
e
f
e
r
?
,
”
E
n
e
r
g
y
Po
l
i
c
y
,
v
o
l
.
5
9
,
p
p
.
2
2
6
–
2
3
4
,
2
0
1
3
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
n
p
o
l
.
2
0
1
3
.
0
3
.
0
2
5
.
[
2
]
D
.
C
.
M
a
t
i
so
f
f
,
R
.
B
e
p
p
l
e
r
,
G
.
C
h
a
n
,
a
n
d
S
.
C
a
r
l
e
y
,
“
A
r
e
v
i
e
w
o
f
b
a
r
r
i
e
r
s
i
n
i
m
p
l
e
m
e
n
t
i
n
g
d
y
n
a
m
i
c
e
l
e
c
t
r
i
c
i
t
y
p
r
i
c
i
n
g
t
o
a
c
h
i
e
v
e
c
o
s
t
-
c
a
u
sa
l
i
t
y
,
”
En
v
i
r
o
n
m
e
n
t
a
l
Re
s
e
a
rch
L
e
t
t
e
rs
,
v
o
l
.
1
5
,
n
o
.
9
,
2
0
2
0
,
d
o
i
:
1
0
.
1
0
8
8
/
1
7
4
8
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