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alu
ate
th
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
tr
ai
n
e
d
m
o
d
el.
A
NN
lear
n
s
f
r
o
m
e
x
a
m
p
le
s
;
th
er
e
f
o
r
e,
s
m
al
l d
ata
s
ets
n
o
r
m
all
y
cr
ea
tes in
ac
c
u
r
ate
r
es
u
l
ts
a
n
d
p
r
o
d
u
ce
a
lar
g
e
tr
ain
in
g
er
r
o
r
.
A
f
e
w
r
esear
c
h
er
s
r
ep
o
r
ted
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d
ex
p
lo
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ed
s
ev
er
al
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esa
m
p
li
n
g
tech
n
iq
u
es
to
d
ea
l
w
it
h
s
m
all
d
ata
s
ize
in
ANN
ap
p
licatio
n
[5
-
8]
.
T
h
is
s
t
u
d
y
f
o
cu
s
es
o
n
th
e
d
ev
elo
p
m
en
t
o
f
b
aseli
n
e
e
n
e
r
g
y
m
o
d
el
a
n
d
t
h
e
in
te
g
r
atio
n
o
f
A
NN
w
it
h
C
r
o
s
s
Valid
atio
n
(
C
V)
an
d
B
o
o
ts
tr
ap
(
B
S)
to
g
et
a
b
etter
ac
cu
r
ac
y
o
f
A
NN
p
r
ed
ictio
n
.
T
h
is
m
et
h
o
d
m
a
y
av
o
id
an
y
o
v
er
f
itti
n
g
o
f
t
h
e
d
ata.
Ov
er
f
itt
in
g
cr
ea
tes
th
e
n
e
t
w
o
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k
to
m
e
m
o
r
ize
tr
ain
i
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g
p
atter
n
s
,
b
u
t t
h
e
y
ca
n
n
o
t g
e
n
er
alize
w
ell
to
n
e
w
d
at
a
(
test
in
g
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et)
an
d
g
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ate
s
p
o
o
r
ac
cu
r
ac
y
.
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h
e
s
tr
u
ct
u
r
e
o
f
t
h
is
p
ap
er
is
o
r
g
an
ized
a
s
f
o
llo
w
s
:
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o
n
2
b
r
ief
l
y
ex
p
lai
n
s
th
e
m
e
th
o
d
o
lo
g
y
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cl
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d
in
g
d
ata
co
llectio
n
,
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as
elin
e
e
n
er
g
y
m
o
d
el
d
ev
elo
p
m
en
t,
an
d
p
er
f
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an
ce
ev
al
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ati
o
n
.
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h
is
is
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o
llo
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y
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p
r
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tio
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o
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t
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d
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s
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io
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o
f
t
h
e
p
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o
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ed
m
et
h
o
d
s
.
Fi
n
all
y
,
t
h
e
co
n
cl
u
s
io
n
is
s
u
m
m
ar
ized
in
s
ec
tio
n
4
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
T
h
is
s
ec
tio
n
d
escr
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es
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h
e
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p
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eth
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o
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ev
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m
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t
o
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el
f
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P
MV
P
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p
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C
s
m
all
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et
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lu
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tr
ated
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F
ig
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.
S
T
A
R
T
D
a
t
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t
i
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r
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o
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l
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e
t
h
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d
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N
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n
p
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s
:
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o
r
k
i
n
g
d
a
y
,
C
l
a
s
s
d
a
y
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C
D
D
A
N
N
o
u
t
p
u
t
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t
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l
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C
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p
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p
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n
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N
D
A
N
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-
B
S
C
V
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e
t
h
o
d
Fig
u
r
e
1
.
P
r
o
p
o
s
ed
I
P
MV
P Op
tio
n
C
B
aseli
n
e
E
n
er
g
y
Mo
d
el
2
.
1
.
Da
t
a
Co
llect
io
n
T
h
e
b
aselin
e
d
ata
is
p
r
o
v
id
ed
b
y
t
h
e
Faci
lit
y
Ma
n
ag
e
m
e
n
t
O
f
f
ice,
U
n
i
v
er
s
it
i
T
ek
n
o
l
o
g
i
Ma
r
a
(
UiT
M)
,
Sh
ah
Ala
m
,
Sela
n
g
o
r
,
Ma
la
y
s
ia
f
o
r
a
2
3
m
o
n
t
h
s
p
e
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io
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.
T
h
e
in
p
u
t
v
ar
iab
les
ar
e
UiT
M
w
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n
g
d
a
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s
(
W
D)
,
Ui
T
M
class
d
ay
s
(
C
D)
an
d
C
o
o
lin
g
De
g
r
ee
Da
y
s
(
C
DD)
.
T
h
ese
th
r
ee
v
ar
iab
les
ar
e
ass
ig
n
ed
as
A
NN
in
p
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n
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tar
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o
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m
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th
l
y
elec
tr
icit
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co
n
s
u
m
p
tio
n
,
k
W
h
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B
aselin
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s
tati
s
tical
d
ata;
m
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m
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m
a
x
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n
d
m
ea
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-
r
etr
o
f
it
ar
e
s
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o
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in
T
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2
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I
P
M
VP
O
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B
a
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M
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del D
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m
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tech
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ed
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h
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NN
to
d
ev
elo
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ac
cu
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ate
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aseli
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o
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el.
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n
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tio
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w
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r
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s
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A
NN
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B
S
C
V)
m
o
d
el
ar
e
d
ev
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p
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A
N
N
s
tr
u
ct
u
r
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an
d
p
ar
a
m
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ter
s
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d
to
b
e
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eter
m
i
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ed
to
d
ev
elo
p
th
e
m
o
d
el.
Feed
-
f
o
r
w
ar
d
m
u
ltil
a
y
er
p
er
ce
p
tr
o
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it
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m
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t p
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N
ar
ch
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r
e
[
9
]
.
T
h
e
tr
ain
i
n
g
alg
o
r
it
h
m
u
s
ed
an
d
r
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o
m
m
en
d
ed
b
y
t
h
e
M
A
T
L
A
B
a
n
d
m
o
s
tl
y
u
s
ed
to
tr
ain
t
h
e
n
et
w
o
r
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i
s
tr
ain
l
m
(
L
e
v
e
n
b
er
g
-
Ma
r
q
u
ar
d
t)
[
1
0
-
11]
.
T
h
e
s
el
ec
ted
tr
an
s
f
er
f
u
n
ctio
n
s
ar
e
lo
g
s
i
g
f
o
r
h
id
d
en
la
y
er
an
d
p
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r
elin
f
o
r
th
e
o
u
tp
u
t
la
y
er
.
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h
e
2
3
s
ets
o
f
d
ata
ar
e
d
iv
id
ed
in
to
a
tr
ai
n
in
g
s
et,
v
al
id
atio
n
s
et
an
d
test
s
et.
T
h
e
tr
ain
i
n
g
s
et
i
s
u
s
ed
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ain
th
e
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et
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d
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t
s
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d
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h
e
v
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d
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n
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et
is
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v
alid
ate
t
h
e
tr
ai
n
i
n
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ce
s
s
an
d
to
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n
tr
o
l
o
v
er
f
itti
n
g
t
h
r
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u
g
h
ea
r
l
y
s
to
p
p
in
g
.
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h
e
tes
tin
g
s
et
is
u
s
ed
t
o
ev
alu
a
te
t
h
e
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ce
o
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e
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ai
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ed
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t
h
e
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et
i
s
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o
t
d
ir
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tl
y
i
n
v
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l
v
ed
i
n
t
h
e
tr
ai
n
i
n
g
p
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ce
s
s
.
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n
A
NN
-
C
V
a
n
d
A
N
N
-
B
SC
V,
t
h
e
n
et
w
o
r
k
s
ar
e
r
u
n
f
o
r
s
ev
er
al
ti
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es
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d
a
v
er
ag
e
v
al
u
es
o
f
all
f
o
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r
R
s
ar
e
r
ec
o
r
d
ed
.
T
w
o
t
y
p
es
o
f
C
V
ar
e
i
m
p
le
m
en
ted
in
A
NN
-
C
V
m
et
h
o
d
s
as
illu
s
tr
ated
in
Fi
g
u
r
e
2
,
1
)
K
-
Fo
ld
C
r
o
s
s
Valid
atio
n
(
KF
C
V)
:
th
e
d
ata
ar
e
p
ar
titi
o
n
s
i
n
to
k
-
s
a
m
p
le
s
an
d
2
)
R
a
n
d
o
m
Sa
m
p
li
n
g
C
r
o
s
s
Valid
atio
n
(
R
SC
V)
:
ea
ch
d
ata
s
p
lits
r
an
d
o
m
l
y
i
n
to
k
-
s
u
b
s
a
m
p
les.
T
r
ain
in
g
a
n
d
v
al
id
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n
s
et
ar
e
d
iv
id
ed
in
to
k
-
s
u
b
s
a
m
p
le
an
d
k
=5
is
s
elec
te
d
in
th
i
s
s
t
u
d
y
.
I
n
f
ir
s
t
iter
atio
n
,
k
-
1
s
u
b
s
a
m
p
le
s
ar
e
d
ed
icate
d
to
tr
ain
in
g
s
et
to
tr
ain
th
e
d
ata
m
ea
n
w
h
ile
t
h
e
r
e
m
ai
n
in
g
s
u
b
s
a
m
p
le
is
u
s
ed
to
v
alid
ate
th
e
d
ate
an
d
th
e
p
r
o
ce
s
s
is
r
ep
ea
ted
f
o
r
k
iter
atio
n
s
.
T
h
e
n
u
m
b
er
o
f
n
eu
r
o
n
s
i
n
th
e
h
id
d
en
la
y
er
is
s
et
b
et
w
ee
n
6
an
d
2
0
n
eu
r
o
n
s
o
n
l
y
[
9
]
.
T
h
e
g
r
ap
h
ical
ill
u
s
tr
atio
n
o
f
A
N
N
_
C
V
is
p
r
esen
ted
i
n
Fi
g
u
r
e
2
.
T
h
e
b
est
s
tr
u
ctu
r
e
o
f
ANN
-
C
V
is
u
s
ed
to
d
ev
elo
p
ed
A
NN
-
B
SC
V
m
o
d
el.
T
h
e
p
u
r
p
o
s
e
o
f
b
o
o
ts
tr
ap
r
esa
m
p
li
n
g
i
s
to
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d
el,
all
f
o
u
r
R
s
n
ee
d
to
b
e
co
n
s
id
er
ed
to
av
o
id
o
v
er
f
itti
n
g
.
T
h
ese
r
esu
lt
s
i
n
d
icate
t
h
at
a
ll
th
e
p
r
o
p
o
s
ed
m
o
d
els
h
av
e
a
h
ig
h
q
u
ali
t
y
o
f
p
r
ed
ictio
n
a
n
d
ca
p
ab
le
to
tr
ain
t
h
e
n
et
w
o
r
k
.
T
h
e
ANN
-
B
SC
V
m
et
h
o
d
ev
id
en
tl
y
o
u
tp
er
f
o
r
m
s
A
NN
-
C
V
m
et
h
o
d
.
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r
th
er
co
m
p
ar
is
o
n
o
f
all
s
tr
u
ct
u
r
es i
s
m
ad
e
a
n
d
clea
r
l
y
s
h
o
w
s
t
h
at
t
h
e
m
o
d
el
o
f
ANN
-
B
S
C
V
w
i
th
6
n
eu
r
o
n
s
in
h
id
d
en
la
y
er
h
as
t
h
e
h
ig
h
e
s
t
v
al
u
e
o
f
R
_
all
an
d
is
n
o
m
i
n
ated
as
t
h
e
b
est
p
er
f
o
r
m
an
ce
.
I
t
is
b
ased
o
n
t
h
e
h
ig
h
er
v
alu
es
o
f
all
f
o
u
r
Rs
w
h
ic
h
ar
e
clo
s
e
to
u
n
it
y
an
d
th
er
ef
o
r
e
th
e
m
o
s
t a
cc
u
r
ate.
T
ab
le
2
.
R
esu
lt s
u
m
m
ar
izat
io
n
M
e
t
h
o
d
N
u
mb
e
r
o
f
h
i
d
d
e
n
n
e
u
r
o
n
s
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t
r
a
i
n
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v
a
l
i
d
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t
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st
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a
l
l
A
N
N
6
0
.
9
1
9
1
5
0
.
3
4
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1
4
0
.
8
8
5
0
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0
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8
9
8
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9
9
0
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9
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2
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2
0
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2
5
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2
7
0
.
9
1
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3
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0
.
8
8
1
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0
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9
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1
4
8
0
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1
7
8
0
4
0
.
8
8
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7
9
0
.
8
6
9
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5
A
N
N
-
CV
6
0
.
9
3
3
2
2
0
.
8
7
1
9
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0
.
9
6
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9
0
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N
N
-
B
S
C
V
6
0
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6
8
0
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9
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5
2
0
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8
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0
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4
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7
9
9
0
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0
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9
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8
0
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8
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0
.
9
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0
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9
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0
.
9
3
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1
7
0
.
8
3
5
3
2
0
.
9
3
9
2
8
4.
CO
NCLU
SI
O
N
T
h
e
m
ain
o
b
j
ec
tiv
e
o
f
th
is
p
ap
er
is
to
d
ev
elo
p
b
aseli
n
e
en
er
g
y
m
o
d
el
f
o
r
Op
tio
n
C
I
P
MV
P
f
o
r
s
m
all
d
ataset
av
ailab
le.
I
n
t
h
is
p
ap
er
,
a
co
m
b
i
n
atio
n
o
f
ANN
w
it
h
r
esa
m
p
li
n
g
tec
h
n
iq
u
e
s
,
B
S a
n
d
C
V
ar
e
p
r
esen
ted
.
T
w
o
m
et
h
o
d
s
,
A
NN
-
C
V
an
d
A
N
N
-
B
S
C
V
w
i
th
s
e
v
er
al
s
tr
u
ctu
r
es
ar
e
p
r
o
p
o
s
ed
in
t
h
is
p
a
p
er
.
I
t
is
f
o
u
n
d
t
h
at
th
e
p
r
o
p
o
s
ed
tech
n
iq
u
es
to
i
n
t
eg
r
ate
A
NN
w
it
h
r
esa
m
p
l
in
g
t
ec
h
n
iq
u
es
i
m
p
r
o
v
ed
th
e
p
r
ed
ictio
n
p
er
f
o
r
m
an
ce
.
T
h
e
A
NN
i
n
p
u
t
v
ar
iab
les
co
n
s
i
s
ted
o
f
w
o
r
k
i
n
g
d
a
y
s
,
cla
s
s
d
a
y
s
a
n
d
C
o
o
li
n
g
De
g
r
ee
Da
y
s
a
n
d
tar
g
eted
o
u
tp
u
t
i
s
m
o
n
t
h
l
y
elec
tr
icit
y
co
n
s
u
m
p
tio
n
,
k
W
h
.
B
ased
o
n
R
_
all,
R
_
tr
ain
,
R
_
v
alid
a
n
d
R
_
test
,
a
s
i
g
n
i
f
ica
n
t
i
m
p
r
o
v
e
m
en
t
i
s
o
b
s
er
v
ed
in
th
e
co
m
p
ar
is
o
n
o
f
t
h
e
r
esu
lts
o
f
b
o
th
m
o
d
els
an
d
it
is
f
o
u
n
d
th
at
th
e
ANN
-
B
S
C
V
m
o
d
el
w
it
h
3
-
6
-
1
s
tr
u
ct
u
r
e
g
av
e
b
etter
ac
cu
r
ac
y
b
ased
o
n
h
i
g
h
est
v
al
u
e
s
o
f
a
ll
f
o
u
r
R
s
t
h
a
n
t
h
e
o
t
h
er
s
tr
u
ct
u
r
es.
T
h
is
s
tr
u
ct
u
r
e
is
s
el
ec
ted
as th
e
b
aseli
n
e
e
n
er
g
y
m
o
d
el
to
p
r
e
d
ict
en
er
g
y
co
n
s
u
m
p
tio
n
f
o
r
Op
tio
n
C
I
P
MV
P
.
T
h
e
r
esu
lts
o
b
tain
ed
f
r
o
m
t
h
i
s
p
ap
er
s
h
o
w
t
h
at
t
h
e
C
V
an
d
B
S
m
et
h
o
d
ca
p
ab
le
to
tr
ain
n
e
u
r
al
n
et
w
o
r
k
s
,
av
o
id
o
v
er
f
it
tin
g
a
n
d
cr
ea
te
m
o
d
el
d
iv
er
s
it
y
w
it
h
li
m
i
ted
d
ataset.
Fo
r
f
u
t
u
r
e
w
o
r
k
s
,
o
p
ti
m
izatio
n
tech
n
iq
u
e
n
ee
d
s
to
b
e
em
b
ed
d
ed
w
ith
r
esa
m
p
li
n
g
tech
n
iq
u
es
to
o
b
tain
b
etter
A
NN
p
er
f
o
r
m
an
ce
ac
c
u
r
ac
y
i
n
d
ev
elo
p
in
g
b
asel
in
e
e
n
er
g
y
m
o
d
el
.
RE
F
E
R
E
NC
E
S
[1
]
.
M
in
istry
Co
o
rd
in
a
to
r
o
f
S
trate
g
ic S
e
c
to
rs,
“
Na
ti
o
n
a
l
En
e
rg
y
Ba
lan
c
e
2
0
1
4
,
”
2
0
1
4
.
[2
]
.
Eff
icie
n
c
y
V
a
lu
a
ti
o
n
Org
a
n
iza
ti
o
n
,
“
In
tern
a
ti
o
n
a
l
P
e
rf
o
rm
a
n
c
e
M
e
a
su
re
m
e
n
t
a
n
d
V
e
rif
ica
ti
o
n
P
r
o
to
c
o
l
(IP
M
VP),
”
2
0
1
2
.
[3
]
.
O.
A
k
in
so
o
to
,
D.
De
Ca
n
h
a
,
a
n
d
J.
H.
C.
P
re
t
o
riu
s,
“
En
e
rg
y
sa
v
i
n
g
s
re
p
o
rti
n
g
a
n
d
u
n
c
e
rtain
ty
in
M
e
a
su
re
m
e
n
t
&
V
e
rif
ica
ti
o
n
,
”
in
Au
str
a
la
si
a
n
Un
ive
rs
it
ies
Po
we
r E
n
g
in
e
e
rin
g
Co
n
fer
e
n
c
e
,
AUPE
C
2
0
1
4
,
Cu
rtin
U
n
ive
rs
it
y
,
Per
th
,
Au
stra
li
a
,
2
0
1
4
,
n
o
.
Oc
to
b
e
r,
p
p
.
1
–
5.
[4
]
.
S
.
M
.
A
ris,
N.
Y.
D
a
h
lan
,
M
.
N.
M
.
Na
w
i,
T
.
A
.
Ni
z
a
m
,
a
n
d
M
.
Z
.
T
a
h
ir,
“
Qu
a
n
ti
fy
in
g
e
n
e
rg
y
sa
v
i
n
g
s
f
o
r
r
e
tro
f
it
c
e
n
tralize
d
h
v
a
c
s
y
ste
m
s at S
e
lan
g
o
r
sta
te se
c
re
tar
y
c
o
m
p
le
x
,
”
J
.
T
e
k
n
o
l.
,
v
o
l
.
7
7
,
n
o
.
5
,
p
p
.
9
3
–
1
0
0
,
2
0
1
5
.
[5
]
.
A
.
P
a
sin
i,
“
A
rti
f
icia
l
n
e
u
ra
l
n
e
tw
o
rk
s f
o
r
s
m
a
ll
d
a
tas
e
t
a
n
a
l
y
sis,”
J
.
T
h
o
ra
c
.
Dis.
,
v
o
l.
7
,
n
o
.
5
,
p
p
.
9
5
3
–
9
6
0
,
2
0
1
5
.
[6
]
.
K.
G
ro
li
n
g
e
r,
A
.
L
’H
e
u
re
u
x
,
M
.
A
.
M
.
Ca
p
re
tz,
a
n
d
L
.
S
e
e
w
a
ld
,
“
En
e
rg
y
f
o
re
c
a
stin
g
f
o
r
e
v
e
n
t
v
e
n
u
e
s:
Big
d
a
ta
a
n
d
p
re
d
ictio
n
a
c
c
u
ra
c
y
,
”
En
e
rg
y
Bu
il
d
.
,
v
o
l
.
1
1
2
,
p
p
.
2
2
2
–
2
3
3
,
2
0
1
6
.
[7
]
.
G
.
S
in
g
h
,
R.
K.
P
a
n
d
a
,
a
n
d
M
.
L
a
m
e
rs,
“
M
o
d
e
li
n
g
o
f
d
a
il
y
ru
n
o
f
f
f
ro
m
a
s
m
a
ll
a
g
ricu
lt
u
ra
l
w
a
t
e
rsh
e
d
u
sin
g
a
rti
f
icia
l
n
e
u
ra
l
n
e
tw
o
rk
w
it
h
re
sa
m
p
li
n
g
tec
h
n
iq
u
e
s,”
J
.
Hy
d
r
o
in
fo
rm
a
ti
c
s
,
v
o
l.
1
7
,
n
o
.
1
,
p
.
5
6
,
2
0
1
5
.
[8
]
.
W
.
N.
W
.
M
.
A
d
n
a
n
,
N.
Y.
Da
h
l
a
n
,
a
n
d
I.
M
u
siri
n
,
“
M
o
d
e
li
n
g
b
a
se
li
n
e
e
lec
tri
c
a
l
e
n
e
rg
y
u
se
o
f
c
h
il
ler
sy
st
e
m
b
y
a
rti
f
icia
l
n
e
u
ra
l
n
e
t
w
o
rk
,
”
in
PE
CON
2
0
1
6
-
2
0
1
6
IE
EE
6
t
h
In
t
e
rn
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
P
o
we
r
a
n
d
E
n
e
rg
y
,
Co
n
fer
e
n
c
e
Pro
c
e
e
d
in
g
,
2
0
1
7
,
p
p
.
5
0
0
–
5
0
5
.
[9
]
.
M
.
Co
sta
e
t
a
l.
,
“
Ne
w
m
u
lt
i
-
obj
e
c
ti
v
e
a
lg
o
rit
h
m
s
f
o
r
n
e
u
ra
l
n
e
tw
o
rk
train
in
g
a
p
p
li
e
d
to
g
e
n
o
m
ic
c
las
si
f
ic
a
ti
o
n
d
a
ta,”
S
tu
d
.
C
o
mp
u
t.
I
n
tell.
,
v
o
l.
2
0
1
,
p
p
.
6
3
–
8
2
,
2
0
0
9
.
[1
0
]
.
T
.
S
.
G
u
n
a
w
a
n
,
I.
Z.
Ya
a
c
o
b
,
a
n
d
M
.
Ka
rti
w
i,
“
A
rti
f
icia
l
Ne
u
ra
l
N
e
tw
o
rk
Ba
se
d
F
a
st E
d
g
e
De
te
c
ti
o
n
A
lg
o
rit
h
m
f
o
r
M
RI
M
e
d
ica
l
Im
a
g
e
s,”
In
d
o
n
e
s.
J
.
El
e
c
tr.
En
g
.
C
o
mp
u
t.
S
c
i.
,
v
o
l.
7
,
n
o
.
1
,
p
p
.
1
2
3
–
1
3
0
,
2
0
1
7
.
[1
1
]
.
N.
T
e
h
lah
,
P
.
Ka
e
w
p
ra
d
it
,
a
n
d
I.
M
.
M
u
jt
a
b
a
,
“
A
rti
f
icia
l
n
e
u
ra
l
n
e
tw
o
rk
b
a
se
d
m
o
d
e
li
n
g
a
n
d
o
p
ti
m
iza
ti
o
n
o
f
re
f
in
e
d
p
a
lm
o
il
p
ro
c
e
ss
,
”
Ne
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Evaluation Warning : The document was created with Spire.PDF for Python.