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Adv
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Vo
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310
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iv
er
s
e
in
p
u
t
s
en
s
o
r
s
th
at
im
p
ac
t
in
d
o
o
r
en
er
g
y
u
s
ag
e
[
2
4
]
,
[
2
5
]
.
E
n
e
r
g
y
co
n
s
u
m
p
tio
n
r
ef
er
s
t
o
th
e
q
u
an
tity
o
f
en
er
g
y
th
at
a
b
u
ild
in
g
n
ee
d
s
to
p
r
o
v
id
e
at
an
y
p
ar
ticu
la
r
m
o
m
e
n
t.
T
h
e
o
v
er
co
n
s
u
m
p
tio
n
o
f
elec
tr
ic
al
en
er
g
y
h
as
th
e
p
o
ten
tial
to
ex
ac
er
b
ate
e
n
er
g
y
wastag
e
an
d
h
av
e
a
d
v
er
s
e
ef
f
ec
ts
o
n
th
e
en
v
ir
o
n
m
e
n
t
[
2
6
]
,
[
2
7
]
.
T
h
e
an
ticip
atio
n
o
f
b
u
ild
in
g
en
er
g
y
co
n
s
u
m
p
tio
n
is
a
s
ig
n
if
ican
t
m
eth
o
d
o
lo
g
y
in
th
e
r
ea
lm
o
f
en
e
r
g
y
co
n
s
er
v
atio
n
,
y
ield
in
g
a
d
v
an
t
ag
es
f
o
r
b
o
t
h
in
d
iv
id
u
als
a
n
d
s
o
ciety
b
y
f
ac
ilit
atin
g
m
o
r
e
p
r
u
d
en
t
co
n
s
tr
u
ctio
n
o
f
n
ew
s
tr
u
ctu
r
es.
Acc
u
r
ate
f
o
r
ec
asti
n
g
o
f
en
er
g
y
u
s
ag
e
in
b
u
ild
in
g
s
is
cr
u
cial
f
o
r
en
h
an
cin
g
en
er
g
y
ef
f
icien
cy
,
to
attain
en
e
r
g
y
co
n
s
er
v
atio
n
,
a
n
d
m
in
im
ize
ec
o
l
o
g
ical
co
n
s
eq
u
e
n
ce
s
[
2
8
]
–
[
3
2
]
.
T
h
e
m
is
m
an
ag
e
m
en
t
o
f
e
n
er
g
y
u
s
e
h
as
th
e
p
o
ten
tial
to
n
eg
ativ
ely
af
f
ec
t
th
e
e
f
f
icien
c
y
o
f
en
er
g
y
u
s
ag
e,
r
esu
ltin
g
in
th
e
wastag
e
o
f
p
o
wer
[
3
3
]
.
T
h
e
p
r
esen
t
s
tu
d
y
aim
ed
to
co
n
s
tr
u
ct
a
p
r
ed
ictio
n
m
o
d
el
f
o
r
en
er
g
y
co
n
s
u
m
p
tio
n
in
th
e
L
ab
o
r
ato
r
y
o
f
E
lectr
ical
an
d
P
o
wer
E
n
g
in
ee
r
i
n
g
(
L
E
PE)
at
Un
iv
er
s
itas
Su
ltan
Ag
en
g
T
ir
tay
asa.
T
h
e
e
n
v
ir
o
n
m
en
t
h
as
a
co
n
s
id
er
ab
le
im
p
a
ct
o
n
en
er
g
y
co
n
s
u
m
p
tio
n
in
co
lleg
es
[
3
4
]
,
[
3
5
]
.
Gu
an
g
Do
n
g
Un
i
v
er
s
ity
h
as
d
o
n
e
r
esear
ch
th
at
d
em
o
n
s
tr
ates
th
e
s
u
b
s
tan
tial
en
er
g
y
s
av
in
g
s
ac
h
iev
ed
v
ia
th
e
d
ev
elo
p
m
e
n
t
o
f
a
co
n
s
er
v
at
io
n
-
o
r
ie
n
ted
ca
m
p
u
s
.
Me
asu
r
in
g
in
d
ices
o
f
en
e
r
g
y
u
s
e
h
as
s
h
o
wn
to
b
e
ch
allen
g
in
g
in
th
is
s
tu
d
y
[
3
6
]
,
[
3
7
]
.
Un
iv
er
s
ity
b
u
ild
in
g
s
in
C
h
in
a
im
p
lem
en
t
s
p
ec
if
ic
en
er
g
y
e
f
f
icien
cy
m
ea
s
u
r
es
tailo
r
ed
to
lo
ca
l
r
eq
u
ir
em
en
ts
,
tak
in
g
in
to
ac
c
o
u
n
t
elem
en
ts
s
u
ch
as
th
e
p
r
esen
ce
o
f
s
ev
er
a
l
ca
m
p
u
s
es
an
d
clim
ate
co
n
d
itio
n
s
,
wh
ich
p
o
s
e
ch
allen
g
es
in
ac
h
iev
in
g
en
er
g
y
ef
f
icien
c
y
in
th
e
b
u
ild
in
g
s
[
3
8
]
.
E
f
f
icien
t
en
e
r
g
y
co
n
s
u
m
p
tio
n
in
a
b
u
ild
i
n
g
was
ac
h
iev
ed
b
y
ag
g
r
eg
atin
g
h
is
to
r
ical
d
ata
o
n
d
aily
elec
tr
icity
u
s
e
in
two
b
u
ild
i
n
g
s
.
T
h
is
ag
g
r
eg
atio
n
was
p
er
f
o
r
m
e
d
u
s
in
g
n
o
r
m
alize
d
d
ata
f
r
o
m
s
ix
in
p
u
t
v
a
r
iab
les
[
3
9
]
.
T
h
e
an
aly
s
is
o
f
th
r
ee
ca
m
p
u
s
b
u
ild
in
g
s
in
T
ia
n
jin
in
d
icate
s
th
at
th
e
av
er
a
g
e
elec
tr
icity
u
s
ag
e
p
er
i
n
h
ab
itan
t
f
lu
ctu
ates
b
ased
o
n
th
e
b
u
ild
i
n
g
'
s
p
u
r
p
o
s
e
an
d
th
e
m
eth
o
d
o
f
co
n
tr
o
llin
g
elec
tr
ical
eq
u
ip
m
en
t
[
4
0
]
.
Statis
tical
r
eg
r
ess
io
n
m
eth
o
d
s
ar
e
e
m
p
l
o
y
ed
t
o
co
m
p
r
eh
e
n
d
t
h
e
co
r
r
elatio
n
b
etwe
en
in
d
iv
id
u
al
v
a
r
iab
les
an
d
e
n
er
g
y
co
n
s
u
m
p
tio
n
[
4
1
]
.
Pre
v
io
u
s
r
esear
ch
m
eth
o
d
s
h
av
e
d
em
o
n
s
tr
ated
th
e
ap
p
licab
ilit
y
o
f
en
h
an
ce
d
m
o
d
el
in
g
in
o
th
er
ty
p
es
o
f
b
u
ild
in
g
s
,
p
r
o
v
id
ed
th
at
it
in
c
o
r
p
o
r
ates
an
en
er
g
y
c
o
n
s
u
m
p
tio
n
m
o
n
ito
r
i
n
g
p
latf
o
r
m
,
r
ath
er
th
an
b
ein
g
r
estricte
d
to
ca
m
p
u
s
b
u
ild
in
g
s
[
4
2
]
.
T
h
e
b
e
h
av
io
r
o
f
r
o
o
m
u
s
er
s
i
s
o
n
e
o
f
t
h
e
in
p
u
t
c
h
ar
ac
ter
is
tics
in
a
b
u
ild
in
g
th
at
in
f
lu
en
c
es
en
er
g
y
u
s
e;
th
is
b
eh
av
io
r
h
as
a
s
ig
n
if
ican
t
im
p
ac
t
o
n
h
o
w
th
e
s
p
ac
e
is
u
s
ed
an
d
co
m
p
licates
th
e
ca
lcu
latio
n
o
f
th
e
r
eq
u
ir
ed
en
er
g
y
co
n
s
u
m
p
tio
n
[
4
3
]
.
W
h
ile
f
ea
tu
r
es
ar
e
d
ir
ec
tly
u
s
ed
as
in
p
u
t
in
th
e
p
r
ed
ic
tio
n
s
tep
o
f
ea
r
lier
en
er
g
y
co
n
s
u
m
p
tio
n
p
r
e
d
ictio
n
m
o
d
els
[
4
4
]
–
[
4
9
]
,
in
t
h
is
s
tu
d
y
,
f
ea
tu
r
e
s
elec
tio
n
was
d
o
n
e
p
r
io
r
to
th
e
p
r
ed
ictio
n
s
tag
e.
T
h
e
g
o
al
o
f
f
ea
tu
r
e
s
elec
tio
n
is
to
id
en
tify
a
f
ew
ch
ar
ac
ter
is
tics
th
at
h
av
e
th
e
b
ig
g
est
im
p
ac
t
o
n
en
er
g
y
u
s
ag
e
[
5
0
]
.
I
t
is
an
t
icip
ated
th
at
th
e
s
elec
tio
n
o
f
f
ea
tu
r
es
will
lead
to
a
m
o
r
e
ac
cu
r
ate
an
d
ef
f
icie
n
t
p
r
ed
ictio
n
s
tag
e.
T
h
e
aim
o
f
t
h
e
f
ea
tu
r
e
s
elec
tio
n
a
p
p
r
o
ac
h
is
to
r
ed
u
ce
th
e
s
et
b
y
elim
in
a
tin
g
ce
r
tain
f
ea
tu
r
es
th
at
ar
e
d
ee
m
ed
u
n
n
ec
ess
ar
y
f
o
r
tex
t
s
en
tim
en
t
cla
s
s
if
icat
i
o
n
.
T
h
is
will
en
h
an
ce
class
if
i
ca
tio
n
ac
cu
r
ac
y
an
d
s
h
o
r
ten
th
e
tr
ain
in
g
tim
e
o
f
m
ac
h
in
e
lear
n
in
g
m
o
d
els
[
5
1
]
.
T
o
g
et
a
tr
u
s
two
r
th
y
tr
a
n
s
f
o
r
m
atio
n
,
attr
ib
u
te
s
elec
tio
n
h
as
th
e
d
r
awb
ac
k
o
f
r
eq
u
ir
in
g
tr
ain
i
n
g
o
n
a
b
ig
d
ata
s
et
[
5
2
]
.
Featu
r
e
s
elec
tio
n
is
o
n
e
m
eth
o
d
f
o
r
g
ettin
g
ar
o
u
n
d
th
e
ex
ce
s
s
iv
e
d
im
en
s
io
n
s
o
f
f
ea
tu
r
es.
R
ed
u
c
in
g
v
ec
to
r
d
im
e
n
s
io
n
s
h
as
b
ee
n
ac
co
m
p
lis
h
ed
b
y
u
s
in
g
in
f
o
r
m
atio
n
g
ain
[
5
3
]
,
o
n
e
o
f
th
e
f
ea
tu
r
e
s
elec
tio
n
m
eth
o
d
s
[
5
4
]
.
Dim
en
s
io
n
r
ed
u
ctio
n
is
an
a
d
d
itio
n
al
s
tr
ateg
y
th
at
ca
n
b
e
em
p
lo
y
ed
to
a
d
d
r
ess
th
e
is
s
u
e
o
f
h
i
g
h
f
ea
tu
r
e
d
im
e
n
s
io
n
s
,
alo
n
g
s
id
e
f
ea
tu
r
e
s
elec
tio
n
tech
n
iq
u
e
s
.
T
h
e
d
im
e
n
s
io
n
r
e
d
u
ctio
n
tech
n
iq
u
e
ai
m
s
to
ac
q
u
ir
e
n
o
v
el
d
ata
r
e
p
r
esen
tat
io
n
s
th
at
ar
e
ef
f
ec
tiv
ely
r
e
d
u
c
ed
in
s
ize
[
5
5
]
.
T
h
e
d
im
e
n
s
io
n
al
r
e
d
u
ctio
n
lin
ea
r
m
o
d
el
co
m
p
r
is
es
th
e
s
in
g
u
lar
v
alu
e
d
ec
o
m
p
o
s
itio
n
(
SVD)
m
o
d
el
an
d
th
e
PC
A
m
o
d
el
[
5
6
]
.
Nev
er
th
eless
,
th
e
lin
ea
r
m
o
d
el
o
f
d
im
en
s
io
n
al
r
ed
u
ctio
n
h
as
a
d
r
aw
b
ac
k
in
th
at
it
g
en
er
ates
a
lin
ea
r
co
m
b
in
atio
n
o
f
all
f
ea
tu
r
es,
wh
ich
ca
n
b
e
i
n
f
lu
en
ce
d
b
y
n
o
is
e
an
d
d
im
in
is
h
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
class
if
icatio
n
m
o
d
el.
Ad
d
itio
n
ally
,
th
e
lin
ea
r
m
o
d
el
o
f
d
im
en
s
io
n
al
r
ed
u
ctio
n
e
n
co
u
n
ter
s
ch
allen
g
es
wh
en
d
ea
lin
g
with
n
o
n
-
lin
ea
r
d
ata
[
5
7
]
.
T
h
e
p
r
esen
t
s
tu
d
y
em
p
lo
y
e
d
th
e
PC
A
m
o
d
el
f
o
r
f
ea
tu
r
e
s
elec
tio
n
,
an
d
th
e
ar
tific
ial
n
eu
r
al
n
et
wo
r
k
(
ANN)
m
o
d
el
f
o
r
th
e
p
r
ed
ictio
n
s
tag
e.
T
h
e
d
ata
u
tili
ze
d
f
o
r
en
er
g
y
co
n
s
u
m
p
tio
n
p
r
ed
ictio
n
e
n
co
m
p
ass
ed
v
ar
io
u
s
p
ar
am
eter
s
,
n
a
m
ely
tem
p
er
at
u
r
e
s
en
s
o
r
DHT
2
2
,
tem
p
er
at
u
r
e
s
en
s
o
r
B
MP1
8
0
,
p
r
ess
u
r
e
,
h
u
m
id
ity
,
v
o
ltag
e,
cu
r
r
en
t,
p
o
wer
,
altitu
d
e,
an
d
li
g
h
t
in
ten
s
ity
.
C
o
n
cu
r
r
en
tly
,
th
e
p
r
ed
ictio
n
m
o
d
el
p
r
o
d
u
ce
s
e
n
er
g
y
co
n
s
u
m
p
tio
n
as its
o
u
tp
u
t.
2.
M
E
T
H
O
D
Fig
u
r
e
1
d
e
p
icts
th
e
f
lo
wch
ar
t
o
f
an
e
n
er
g
y
co
n
s
u
m
p
tio
n
p
r
ed
ictio
n
m
o
d
el
th
at
em
p
l
o
y
s
PC
A
as
a
f
ea
tu
r
e
s
elec
tio
n
tec
h
n
iq
u
e
.
B
ased
o
n
th
e
an
aly
s
is
o
f
h
is
to
r
ical
d
ata
u
tili
ze
d
as
in
p
u
ts
f
o
r
PC
A
m
o
d
els,
it
is
ev
id
en
t
th
at
th
e
in
p
u
t
v
ar
iab
l
es
in
itially
co
m
p
r
is
ed
9
s
en
s
o
r
s
.
Ho
wev
er
,
in
o
r
d
er
to
s
tr
ea
m
lin
e
th
e
d
ata
an
d
en
h
an
ce
its
d
is
p
lay
ef
f
icien
cy
,
th
e
ANN
m
o
d
el
was
em
p
lo
y
ed
,
r
esu
ltin
g
in
a
r
ed
u
ce
d
s
et
o
f
4
in
p
u
t
v
ar
ia
b
les.
Ad
d
itio
n
ally
,
th
e
PC
A
m
o
d
el
s
er
v
es
th
e
f
u
n
ctio
n
o
f
t
r
an
s
f
o
r
m
in
g
th
e
in
itially
co
r
r
elate
d
d
ata
i
n
to
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2252
-
8
8
1
4
I
n
t J Ad
v
Ap
p
l Sci
,
Vo
l.
14
,
No
.
2
,
J
u
n
e
2
0
2
5
:
3
1
0
-
321
312
u
n
co
r
r
elate
d
d
ata.
C
o
n
s
eq
u
en
tly
,
th
e
d
ata
will
b
e
m
o
r
e
v
is
u
ally
p
r
esen
ted
an
d
th
e
s
u
b
s
e
q
u
en
t
s
tag
e
o
f
th
e
ANN
m
o
d
el
will
b
e
c
o
m
p
lete
d
m
o
r
e
q
u
ick
ly
[
2
9
]
.
T
h
e
in
iti
al
s
tep
in
g
ettin
g
th
e
co
v
ar
ia
n
ce
m
atr
ix
n
ee
d
ed
to
ca
lcu
late
th
e
v
alu
es
o
f
eig
en
v
ec
to
r
s
an
d
eig
en
v
alu
es
in
PC
A
is
n
o
r
m
aliza
tio
n
[
5
8
]
.
T
h
e
ass
ig
n
m
en
t
o
f
e
ig
en
v
ec
to
r
s
an
d
eig
e
n
v
alu
es
ca
n
p
r
o
v
id
e
in
s
ig
h
t
in
to
th
e
ex
ten
t
to
wh
ic
h
in
p
u
t
v
ar
ia
b
les
in
f
lu
en
ce
PC
v
ar
iab
les.
Fig
u
r
e
1
.
Flo
wch
ar
t
o
f
th
e
PC
A
an
d
ANN
-
b
ased
m
eth
o
d
f
o
r
en
er
g
y
co
n
s
u
m
p
tio
n
p
r
ed
ictio
n
Acc
o
r
d
in
g
to
th
e
d
ata
p
r
esen
t
ed
in
Fig
u
r
e
1
,
it
is
ev
id
en
t
t
h
at
th
e
d
ata
co
llected
is
r
ea
l
-
tim
e
d
ata
o
b
tain
ed
f
r
o
m
th
e
L
E
PE.
T
h
e
d
esig
n
e
m
p
lo
y
s
a
to
tal
o
f
9
s
en
s
o
r
s
th
at
ar
e
in
ter
c
o
n
n
ec
t
ed
with
th
e
d
e
v
ice
u
tili
ze
d
f
o
r
d
ata
r
etr
ie
v
al.
Sen
s
o
r
1
co
m
p
r
is
es
d
ata
p
er
tain
in
g
to
p
r
ess
u
r
e,
wh
er
ea
s
s
en
s
o
r
2
an
d
s
en
s
o
r
7
en
co
m
p
ass
d
ata
p
er
tain
i
n
g
t
o
tem
p
er
atu
r
e.
Sen
s
o
r
3
is
ass
o
ciate
d
with
t
h
e
d
ata
p
e
r
tain
i
n
g
to
th
e
elec
tr
ical
cu
r
r
en
t
with
in
th
e
cir
cu
it.
Sen
s
o
r
4
is
a
v
o
ltag
e
-
co
n
n
ec
ted
d
ata
s
en
s
o
r
.
T
h
e
d
ata
r
etr
iev
al
o
f
s
en
s
o
r
8
p
er
tain
s
to
th
e
altitu
d
e
d
is
tan
ce
,
a
s
it is
d
ir
ec
tly
ass
o
ciate
d
with
th
e
f
o
r
ec
ast o
f
en
er
g
y
co
n
s
u
m
p
tio
n
.
2
.
1
.
F
ea
t
ure
s
elec
t
io
n us
ing
prin
cipa
l c
o
m
po
nent
a
na
ly
s
is
T
h
e
s
tu
d
y
em
p
lo
y
ed
PC
A
to
en
h
an
ce
co
m
p
u
tatio
n
p
r
ec
is
io
n
an
d
d
ec
r
ea
s
e
tr
ain
in
g
tim
e
[
5
9
]
.
PC
A
was
em
p
lo
y
ed
to
r
ed
u
ce
an
d
t
r
an
s
f
o
r
m
th
e
d
ata
u
tili
z
ed
in
th
is
s
tu
d
y
.
T
h
is
in
v
o
lv
ed
e
x
clu
d
in
g
ir
r
elev
a
n
t
d
ata
th
r
o
u
g
h
o
u
t
th
e
PC
A
p
r
o
ce
s
s
,
co
m
p
u
tin
g
th
e
co
v
ar
ian
c
e
m
atr
ix
o
f
th
e
d
ata,
an
d
id
en
tify
in
g
eig
en
v
ec
to
r
s
an
d
eig
en
v
alu
es.
T
h
e
p
r
o
ce
s
s
b
eg
an
with
ca
lcu
latin
g
th
e
co
v
ar
ian
ce
m
atr
ix
t
o
ca
p
tu
r
e
th
e
r
elatio
n
s
h
ip
s
an
d
v
ar
ian
ce
am
o
n
g
all
f
ea
t
u
r
es
in
th
e
d
ataset.
E
ig
en
v
ec
to
r
s
an
d
eig
e
n
v
alu
es
wer
e
s
u
b
s
e
q
u
en
tly
d
er
iv
e
d
to
id
en
tify
th
e
p
r
in
cip
al
c
o
m
p
o
n
en
ts
th
at
ac
co
u
n
t
f
o
r
m
ax
i
m
u
m
v
ar
ia
n
ce
with
in
th
e
d
a
ta.
T
h
ese
p
r
in
cip
al
co
m
p
o
n
en
ts
wer
e
th
e
n
u
tili
ze
d
to
tr
a
n
s
f
o
r
m
th
e
o
r
ig
in
al
d
at
aset
in
to
a
r
e
d
u
ce
d
f
ea
tu
r
e
s
p
a
ce
wh
ile
p
r
eser
v
i
n
g
its
m
o
s
t
cr
itical
in
f
o
r
m
atio
n
al
co
n
ten
t.
T
h
is
m
eth
o
d
o
lo
g
ical
ap
p
r
o
ac
h
en
h
an
ce
d
co
m
p
u
tat
io
n
al
ef
f
icie
n
cy
b
y
r
ed
u
cin
g
th
e
o
v
er
all
f
ea
tu
r
e
s
et
an
d
m
itig
ated
th
e
r
is
k
o
f
o
v
er
f
itti
n
g
,
th
er
eb
y
im
p
r
o
v
in
g
th
e
m
o
d
el'
s
g
en
er
aliza
b
ilit
y
.
T
h
e
p
r
o
ce
s
s
o
f
s
elec
tin
g
f
ea
tu
r
es u
s
in
g
t
h
e
PC
A
ap
p
r
o
ac
h
in
v
o
lv
es th
e
f
o
ll
o
win
g
s
tep
s
:
i)
T
h
e
co
m
p
u
tatio
n
o
f
th
e
c
o
v
a
r
ian
ce
m
atr
ix
in
v
o
lv
es
th
e
s
u
b
tr
ac
tio
n
o
f
th
e
m
ea
n
v
alu
e
o
f
f
ea
tu
r
e
d
at
a
ch
ar
ac
ter
is
tics
.
ii)
T
h
e
eig
e
n
v
ec
to
r
s
a
n
d
eig
en
v
alu
es
o
f
th
e
co
v
ar
ia
n
ce
m
atr
ix
ar
e
co
m
p
u
ted
.
T
h
e
p
r
o
ce
s
s
in
v
o
lv
e
s
s
elec
tin
g
m
-
n
u
m
b
e
r
eig
en
v
a
lu
es
f
r
o
m
th
e
lis
t
o
f
eig
en
v
ec
to
r
s
an
d
s
u
b
s
eq
u
en
tly
ass
ig
n
in
g
th
ese
eig
en
v
ec
to
r
s
as
1
,
.
.
.
.
,
.
iii)
C
alcu
latin
g
th
e
co
n
tr
ib
u
tio
n
o
f
ea
ch
f
ea
tu
r
e
with
th
e
(
1
)
.
=
∑
|
|
=
1
(
1
)
iv
)
C
h
o
o
s
in
g
th
e
lar
g
est
n
u
m
b
e
r
o
f
v
alu
es
ac
co
r
d
i
n
g
to
th
e
n
u
m
b
er
o
f
f
ea
tu
r
es
y
o
u
wan
t
to
m
ain
tain
,
s
o
y
o
u
g
et
th
e
jth
f
ea
tu
r
e
wh
ich
i
s
a
s
ig
n
if
ican
t f
ea
tu
r
e.
Sev
er
al
ca
r
ef
u
lly
s
elec
ted
f
ea
tu
r
es,
d
er
iv
e
d
f
r
o
m
t
h
e
p
r
ep
r
o
ce
s
s
in
g
an
d
d
im
en
s
io
n
ality
r
ed
u
ctio
n
s
tag
es,
wer
e
s
u
b
s
eq
u
en
tly
u
til
ized
as
in
p
u
t
v
a
r
iab
les
f
o
r
th
e
en
er
g
y
co
n
s
u
m
p
tio
n
p
r
ed
icti
o
n
p
h
ase.
T
h
is
s
tep
aim
ed
to
en
s
u
r
e
th
at
th
e
m
o
s
t r
elev
an
t a
n
d
s
ig
n
if
ican
t f
ea
tu
r
es c
o
n
tr
ib
u
ted
to
th
e
p
r
e
d
ictiv
e
m
o
d
elin
g
p
r
o
ce
s
s
,
th
er
eb
y
e
n
h
an
ci
n
g
th
e
ac
cu
r
ac
y
an
d
r
eliab
ilit
y
o
f
th
e
r
esu
lts
.
T
wo
d
is
tin
ct
p
r
e
d
ictiv
e
alg
o
r
i
th
m
s
wer
e
u
tili
ze
d
f
o
r
th
is
p
u
r
p
o
s
e,
in
cl
u
d
in
g
th
e
ANN
alg
o
r
ith
m
,
a
r
ec
o
g
n
iz
ed
m
eth
o
d
n
o
te
d
f
o
r
its
s
tr
o
n
g
ab
ilit
y
to
m
o
d
el
co
m
p
lex
n
o
n
lin
ea
r
r
elatio
n
s
h
ip
s
.
T
h
e
s
tu
d
y
u
tili
ze
d
th
ese
alg
o
r
ith
m
s
to
p
r
o
d
u
ce
ac
cu
r
a
te
an
d
d
ep
en
d
ab
le
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ad
v
Ap
p
l Sci
I
SS
N:
2252
-
8
8
1
4
E
n
h
a
n
ci
n
g
a
r
tifi
cia
l n
eu
r
a
l n
e
tw
o
r
k
p
erfo
r
ma
n
ce
fo
r
en
erg
y
efficien
cy
in
la
b
o
r
a
to
r
ies
… (
Desmir
a
)
313
p
r
ed
ictio
n
s
o
f
en
er
g
y
co
n
s
u
m
p
tio
n
,
h
ig
h
lig
h
tin
g
th
e
ef
f
ec
tiv
en
ess
o
f
f
ea
tu
r
e
s
elec
ti
o
n
an
d
alg
o
r
it
h
m
ic
ad
ap
tab
ilit
y
in
tack
lin
g
co
m
p
lex
f
o
r
ec
asti
n
g
is
s
u
es.
2
.
2
.
E
nerg
y
co
ns
um
ptio
n
predict
io
n
us
i
ng
a
rt
if
icia
l
neura
l
ne
t
wo
rk
a
nd
princip
a
l
co
m
po
nent
a
na
ly
s
is
a
lg
o
rit
hm
s
Pre
s
s
u
r
e,
tem
p
er
atu
r
e
s
en
s
o
r
DHT
2
2
,
cu
r
r
en
t,
v
o
ltag
e,
p
o
wer
,
lig
h
t
in
ten
s
ity
,
tem
p
e
r
atu
r
e
s
en
s
o
r
B
MP1
8
0
,
altitu
d
e,
an
d
h
u
m
id
ity
all
in
f
lu
en
ce
th
e
esti
m
atio
n
o
f
elec
tr
ical
en
er
g
y
r
eq
u
i
r
e
m
en
ts
in
th
e
L
E
PE.
T
h
e
lab
o
r
ato
r
y
co
llected
an
a
v
er
ag
e
o
f
6
7
d
ay
s
o
f
d
ata
u
s
i
n
g
a
s
in
g
le
lay
e
r
o
f
h
i
d
d
en
n
etwo
r
k
in
o
r
d
er
t
o
r
ed
u
ce
th
e
co
m
p
u
tatio
n
al
tim
e
r
eq
u
ir
ed
f
o
r
p
r
e
d
ictio
n
u
s
in
g
ANN
[
6
0
]
.
T
h
e
ANN
tech
n
iq
u
e
is
u
s
ed
as
a
co
m
p
u
tatio
n
al
to
o
l
u
s
in
g
a
f
ee
d
f
o
r
war
d
n
etwo
r
k
ty
p
e
to
esti
m
ate
th
e
en
er
g
y
co
n
s
u
m
p
tio
n
o
f
t
h
e
two
lab
o
r
ato
r
ies
at
th
e
Fa
cu
lty
o
f
E
lectr
ical
E
n
g
in
ee
r
in
g
(
FKE)
,
Un
iv
er
s
iti
T
ek
n
o
lo
g
i
MA
R
A
(
UiT
M)
Ma
lay
s
ia.
T
h
e
r
esu
lts
in
d
icate
th
at
th
e
ANN
is
ef
f
ec
tiv
ely
tr
ain
ed
to
f
o
r
ec
ast
en
er
g
y
u
s
ag
e
[
3
3
]
.
A
s
tu
d
y
co
n
d
u
cted
b
y
[
6
1
]
,
in
d
icate
s
th
at
b
o
th
th
e
im
p
r
o
v
ed
p
a
r
ticle
s
war
m
o
p
tim
izatio
n
(
iPSO
)
-
ANN
an
d
a
h
y
b
r
id
g
en
etic
alg
o
r
ith
m
(
GA
)
-
ANN
s
u
r
p
ass
th
e
co
n
v
e
n
tio
n
al
ANN
in
ter
m
s
o
f
p
r
e
d
ictio
n
ac
c
u
r
ac
y
.
Ad
d
itio
n
ally
,
t
h
e
im
p
r
o
v
e
d
p
ar
ticle
s
war
m
o
p
tim
izatio
n
(
iPSO
)
-
ANN
m
o
d
el
m
o
s
t
s
ig
n
if
ican
tly
r
ed
u
ce
s
co
m
p
u
tatio
n
al
tim
e,
estab
lis
h
in
g
it
as
a
f
ea
s
ib
le
ch
o
ice
f
o
r
r
ea
l
-
tim
e
en
e
r
g
y
f
o
r
ec
asti
n
g
.
B
o
u
jo
u
d
ar
et
a
l.
[
6
2
]
is
cu
r
r
en
tly
en
g
a
g
ed
in
th
e
in
teg
r
atio
n
o
f
ANN
to
esti
m
ate
th
e
s
tate
o
f
ch
ar
g
e
(
SOC
)
o
f
b
atter
ies
an
d
to
m
a
n
ag
e
b
id
ir
ec
tio
n
al
co
n
v
er
ter
s
.
T
h
e
p
er
f
o
r
m
a
n
ce
an
d
r
o
b
u
s
tn
ess
o
f
th
e
s
u
g
g
ested
co
n
tr
o
l
s
tr
ateg
y
ar
e
elu
cid
ated
b
y
t
h
e
s
im
u
latio
n
r
esu
lts
o
b
tain
ed
in
th
e
MA
T
L
AB
/Si
m
u
lin
k
en
v
ir
o
n
m
en
t.
I
n
o
r
d
er
to
e
n
h
an
ce
th
e
ef
f
icac
y
o
f
th
e
ANN
in
f
o
r
ec
asti
n
g
t
h
e
d
em
a
n
d
f
o
r
elec
tr
ical
e
n
er
g
y
with
in
a
lab
o
r
ato
r
y
s
ettin
g
,
it
is
im
p
er
ativ
e
to
c
o
n
s
id
e
r
th
e
im
p
ac
t
o
f
eig
e
n
v
ec
to
r
s
an
d
eig
en
v
alu
es
o
f
th
e
p
r
in
cip
al
co
m
p
o
n
en
t
o
f
ea
c
h
in
p
u
t
v
ar
iab
le
o
n
th
e
r
ed
u
ct
io
n
a
n
d
tr
a
n
s
f
o
r
m
atio
n
p
r
o
ce
s
s
es.
2
.
3
.
A
rt
if
icia
l neura
l net
wo
rk
a
n
d
princi
pa
l c
o
m
po
nent
a
na
ly
s
is
pre
dict
io
n per
f
o
rm
a
nce
t
est
T
o
ass
ess
an
d
ev
alu
ate
th
e
p
r
ec
is
io
n
o
f
d
ata
co
llected
in
r
ea
l
-
tim
e
in
th
e
L
E
PE,
o
n
e
ca
n
c
o
m
p
ar
e
th
e
ac
tu
al
d
ata
o
r
o
r
ig
in
al
d
ata
u
s
in
g
th
e
ANN
ap
p
r
o
ac
h
.
T
h
e
m
ea
n
s
q
u
ar
e
er
r
o
r
(
MSE
)
f
o
r
m
u
la
was
em
p
lo
y
e
d
to
co
m
p
ar
e
ac
tu
al
m
ea
s
u
r
em
e
n
t
d
ata
with
p
r
ed
icted
d
at
a
g
en
e
r
ated
b
y
th
e
ANN
m
o
d
el.
T
h
e
MSE
is
em
p
lo
y
ed
to
ass
es
s
th
e
p
r
ec
is
io
n
o
f
f
o
r
e
ca
s
tin
g
o
u
tco
m
es
in
r
elatio
n
t
o
th
e
in
itial
d
ataset
o
f
lab
o
r
at
o
r
y
m
ea
s
u
r
em
e
n
ts
.
T
h
e
r
a
n
g
e
o
f
f
o
r
ec
asti
n
g
r
esu
lts
an
d
MSE
v
alu
es
is
f
r
o
m
0
t
o
in
f
i
n
ity
,
with
0
r
ep
r
esen
tin
g
th
e
o
p
tim
al
v
alu
e
[
6
3
]
.
T
h
e
MSE
ca
n
b
e
co
m
p
u
t
ed
u
s
in
g
th
e
(
2
)
.
=
∑
(
−
̅
)
2
=
1
(
2
)
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
p
r
esen
t
s
tu
d
y
em
p
lo
y
e
d
th
e
PC
A
m
o
d
el
f
o
r
f
ea
tu
r
e
s
elec
tio
n
,
an
d
th
e
ANN
m
o
d
el
f
o
r
th
e
p
r
ed
ictio
n
s
tag
e.
T
h
e
d
ata
u
tili
ze
d
f
o
r
en
er
g
y
c
o
n
s
u
m
p
ti
o
n
p
r
ed
ictio
n
en
c
o
m
p
ass
ed
v
ar
io
u
s
p
ar
am
eter
s
,
n
am
ely
tem
p
er
atu
r
e
s
en
s
o
r
DHT
2
2
,
tem
p
e
r
atu
r
e
s
en
s
o
r
B
MP1
8
0
,
p
r
ess
u
r
e,
h
u
m
id
ity
,
v
o
ltag
e
,
c
u
r
r
en
t,
p
o
wer
,
altitu
d
e,
an
d
lig
h
t
in
ten
s
ity
.
C
o
n
cu
r
r
en
tly
,
th
e
p
r
ed
i
ctio
n
m
o
d
el
p
r
o
d
u
ce
s
en
er
g
y
co
n
s
u
m
p
tio
n
as
its
o
u
tp
u
t.
T
h
e
n
etwo
r
k
was
tr
a
in
ed
u
s
in
g
MA
T
L
AB
2
0
1
9
a.
T
h
e
alg
o
r
ith
m
s
u
n
d
er
wen
t
t
esti
n
g
in
o
r
d
er
t
o
ascer
tain
th
e
m
o
s
t
o
p
tim
al
a
lg
o
r
ith
m
f
o
r
a
p
p
licatio
n
.
T
h
e
L
ev
en
b
e
r
g
-
Ma
r
q
u
ar
d
t
m
et
h
o
d
[
6
4
]
–
[
6
6
]
was
ev
alu
ated
f
o
r
its
ab
il
ity
to
cr
e
ate
o
u
tp
u
t
in
ac
co
r
d
an
ce
with
th
e
in
ten
d
e
d
iter
atio
n
tar
g
et.
T
h
e
alg
o
r
ith
m
was
f
o
u
n
d
to
b
e
th
e
f
astes
t
in
ter
m
s
o
f
tr
ain
in
g
r
esu
lts
an
d
test
in
g
co
r
r
elatio
n
co
e
f
f
icien
t
R
=1
,
in
d
icatin
g
th
at
th
e
av
er
ag
e
er
r
o
r
in
th
e
tr
ain
in
g
d
ata
f
r
o
m
ea
ch
test
is
clo
s
e
to
ze
r
o
.
T
h
e
b
in
ar
y
s
ig
m
o
id
an
d
id
en
tity
ac
tiv
atio
n
f
u
n
ctio
n
s
wer
e
s
elec
ted
b
ased
o
n
t
h
e
h
i
g
h
est
p
er
f
o
r
m
a
n
ce
i
n
all
ex
p
er
im
en
ts
.
W
h
en
th
e
it
er
atio
n
o
b
jectiv
e
is
m
et,
th
e
tr
ai
n
in
g
o
n
d
ata
is
ter
m
in
ated
a
f
ter
1
,
0
0
0
iter
atio
n
s
,
with
an
ep
o
c
h
o
f
2
0
0
,
0
0
0
.
T
h
e
tr
ain
in
g
d
at
a
u
tili
ze
d
v
ar
iab
le
in
p
u
t
d
e
r
iv
e
d
f
r
o
m
s
en
s
o
r
d
ata,
wh
ich
u
n
d
er
wen
t
f
ea
tu
r
e
s
elec
tio
n
u
s
in
g
th
e
PC
A
m
o
d
el.
T
ab
le
1
d
em
o
n
s
tr
ates
th
at
th
e
eig
en
v
ec
to
r
a
n
d
eig
e
n
v
alu
e
ali
g
n
ed
with
(
1
)
f
o
llo
win
g
th
e
g
r
o
u
p
in
g
o
f
th
e
d
ata
an
d
th
e
ac
q
u
is
itio
n
o
f
PC
A
r
esu
lts
p
r
esen
ted
in
T
ab
le
1
.
T
h
e
d
ata
in
T
ab
le
1
i
n
d
icate
s
th
at
th
e
d
im
en
s
io
n
r
ed
u
ctio
n
p
r
o
ce
s
s
,
p
ar
ticu
lar
ly
t
h
e
s
elec
tio
n
o
f
p
r
in
cip
al
co
m
p
o
n
e
n
ts
(
PC
1
to
PC
9
)
,
y
ield
s
eig
en
v
ec
to
r
s
an
d
eig
en
v
alu
es th
at
o
u
tp
e
r
f
o
r
m
th
o
s
e
ch
o
s
en
f
o
r
th
e
ANN
m
o
d
el.
T
ab
le
1
s
h
o
ws
t
h
at
th
e
ei
g
en
v
al
u
es
f
o
r
PC
1
t
o
PC
4
s
u
r
p
ass
th
e
th
r
esh
o
l
d
v
alu
e
o
f
1
,
in
d
icatin
g
th
at
th
ese
co
m
p
o
n
en
ts
s
ig
n
if
ican
tly
co
n
tr
ib
u
te
to
th
e
v
ar
ia
n
ce
in
th
e
d
ataset.
T
h
is
f
in
d
i
n
g
u
n
d
e
r
s
co
r
es
th
e
ef
f
icac
y
o
f
th
e
PC
A
m
eth
o
d
in
is
o
latin
g
th
e
m
o
s
t
in
f
o
r
m
ati
v
e
co
m
p
o
n
e
n
ts
wh
ile
m
i
n
im
izin
g
n
o
is
e
an
d
r
ed
u
n
d
an
cy
.
I
n
th
e
n
ex
t
p
h
ase
,
th
e
v
alu
es
co
r
r
esp
o
n
d
in
g
to
PC
1
an
d
PC
4
,
r
ec
o
g
n
ize
d
as
t
h
e
m
o
s
t
s
ig
n
if
ica
n
t
p
r
in
cip
al
co
m
p
o
n
e
n
ts
,
will
b
e
u
tili
ze
d
as
in
p
u
ts
f
o
r
th
e
ANN
m
o
d
el
[
6
7
]
–
[
6
9
]
.
T
h
is
s
elec
tio
n
s
ee
k
s
to
u
tili
ze
th
e
ess
en
tial f
ea
tu
r
es f
o
r
f
o
r
ec
asti
n
g
en
er
g
y
co
n
s
u
m
p
tio
n
in
th
e
L
E
PE
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2252
-
8
8
1
4
I
n
t J Ad
v
Ap
p
l Sci
,
Vo
l.
14
,
No
.
2
,
J
u
n
e
2
0
2
5
:
3
1
0
-
321
314
T
ab
le
1
.
T
h
e
p
er
f
o
r
m
a
n
ce
o
f
PC
A
tr
an
s
f
o
r
m
atio
n
r
esu
lts
No
P
r
i
n
c
i
p
l
e
c
o
mp
o
n
e
n
t
a
n
a
l
y
s
i
s (P
C
A
)
R
e
d
u
c
t
i
o
n
PC
A
=
=
∑
|
|
=
1
1
P
C
A
1
3
.
2
2
P
C
A
2
1
.
7
3
P
C
A
3
1
.
5
4
P
C
A
4
1
.
1
5
P
C
A
5
0
.
7
6
P
C
A
6
0
.
5
7
P
C
A
7
0
.
2
8
P
C
A
8
0
.
0
5
9
P
C
A
9
0
.
0
3
(
(
)
)
=
∑
(
1
−
)
⃑
⃑
⃑
⃑
=
1
=
1
(
3
)
(
℃
)
=
∑
(
1
−
)
⃑
⃑
⃑
⃑
=
1
=
1
(
4
)
Var
ian
ts
o
f
ea
c
h
d
ata
wer
e
o
b
tain
ed
u
s
in
g
(
2
)
a
n
d
(
3
)
to
d
et
er
m
in
e
eig
e
n
v
ec
to
r
s
an
d
eig
e
n
v
alu
es.
I
n
ep
o
ch
1
,
0
0
0
,
th
e
MSE
tr
ain
in
g
[
7
0
]
ac
h
iev
e
d
a
v
alu
e
o
f
0
.
0
4
5
9
3
1
.
T
h
e
test
y
ield
ed
a
co
r
r
elatio
n
c
o
ef
f
icien
t
R
=0
.
3
5
1
3
6
,
with
9
in
p
u
t
v
a
r
iab
les
an
d
o
n
e
o
u
tp
u
t.
T
h
e
ANN
s
tr
u
ctu
r
e
em
p
lo
y
ed
th
e
g
a
u
s
s
m
f
in
p
u
t
m
em
b
er
s
h
ip
f
u
n
ctio
n
.
Fig
u
r
e
2
d
is
p
lay
s
a
d
ataset
co
n
s
i
s
tin
g
o
f
5
0
tr
ain
in
g
d
a
ta
p
o
in
ts
,
wh
ich
r
esu
lted
in
a
co
r
r
elatio
n
c
o
ef
f
icien
t
o
f
R
=1
af
ter
2
,
0
0
0
ep
o
ch
s
.
Fig
u
r
e
s
2
(
a)
a
n
d
2
(
b
)
s
h
o
w
PC
A
an
d
ANN
d
ata
tr
ain
in
g
with
R
=1
an
d
d
ataset
in
d
ex
,
wh
ile
Fig
u
r
e
2
(
c)
d
is
p
lay
s
th
e
b
est
tr
ain
in
g
p
er
f
o
r
m
a
n
ce
.
Gr
ad
ien
t,
v
alid
atio
n
ch
ec
k
,
an
d
lea
r
n
in
g
r
ate
ca
n
b
e
s
ee
n
in
Fig
u
r
e
2
(
d
)
an
d
t
h
e
t
r
ain
in
g
s
tate
p
lo
t in
Fig
u
r
e
2
(
e
)
.
Fig
u
r
e
2
d
e
m
o
n
s
tr
ates
th
at
th
e
ANN
m
o
d
el
ac
h
iev
es
a
h
ig
h
l
ev
el
o
f
ac
c
u
r
ac
y
i
n
b
o
t
h
th
e
d
i
s
tr
ib
u
tio
n
o
f
r
ea
l
d
ata
a
n
d
p
r
ed
icted
d
at
a.
T
h
e
co
r
r
elatio
n
co
e
f
f
icien
t
R
is
1
,
an
d
t
h
e
MSE
is
0
.
0
4
5
9
3
1
,
in
d
icatin
g
th
at
th
e
m
o
d
el
h
as
m
et
its
aim
af
ter
1
,
0
0
0
iter
atio
n
s
.
A
lear
n
in
g
r
ate
o
f
2
.
3
7
3
2
e
-
0
5
was
o
b
s
er
v
ed
.
T
h
e
d
u
r
atio
n
r
eq
u
ir
ed
to
r
ea
c
h
th
e
d
esig
n
ated
o
b
jectiv
e
was
6
.
2
9
m
i
n
u
tes.
I
n
ea
ch
ep
o
c
h
,
th
e
g
r
ad
ie
n
t
v
alu
e
was
r
ec
o
r
d
e
d
as 2
.
0
2
6
5
,
th
e
v
alid
atio
n
ch
ec
k
was set to
0
,
an
d
t
h
e
lear
n
in
g
r
ate
was m
ain
tain
ed
at
2
.
3
7
3
2
e
-
05.
Similar
to
th
e
d
ata
tr
ai
n
in
g
p
h
ase,
th
e
test
in
g
p
h
ase
em
p
lo
y
ed
ANN
[
5
9
]
,
[
6
1
]
,
[
6
8
]
,
[
7
1
]
s
et
with
b
o
th
b
in
ar
y
an
d
id
en
tity
s
ig
m
o
id
ac
tiv
atio
n
f
u
n
ctio
n
s
to
g
u
ar
a
n
tee
th
e
b
est
p
er
f
o
r
m
an
ce
in
m
o
d
elin
g
n
o
n
lin
ea
r
in
ter
ac
tio
n
s
with
in
th
e
d
ata.
T
h
is
p
h
ase
s
o
u
g
h
t
to
co
n
f
ir
m
th
e
m
o
d
el'
s
g
en
er
aliza
b
ilit
y
an
d
its
ca
p
ac
ity
to
r
eliab
ly
f
o
r
ec
ast
en
er
g
y
co
n
s
u
m
p
tio
n
u
s
in
g
p
r
ev
io
u
s
ly
u
n
ex
am
in
e
d
test
d
ata.
T
h
e
test
in
g
p
r
o
ce
d
u
r
e
p
r
o
d
u
ce
d
a
n
MSE
s
co
r
e
o
f
0
.
0
4
5
9
3
1
,
s
ig
n
if
y
i
n
g
litt
le
er
r
o
r
a
n
d
h
ig
h
p
r
e
d
icted
ac
cu
r
ac
y
.
Fig
u
r
e
3
p
r
esen
ts
a
co
m
p
r
eh
e
n
s
iv
e
co
m
p
ar
is
o
n
b
etwe
en
th
e
an
ticip
ated
en
er
g
y
v
alu
es
f
r
o
m
th
e
ANN
m
o
d
el
an
d
th
e
ac
tu
al
en
er
g
y
u
s
ag
e
d
ata.
T
h
is
im
ag
e
u
n
d
er
s
co
r
es
t
h
e
m
o
d
el'
s
ca
p
ac
ity
to
ac
cu
r
ately
r
ep
licate
r
ea
l
-
wo
r
ld
en
er
g
y
co
m
p
u
tatio
n
s
,
d
em
o
n
s
tr
atin
g
its
d
u
r
ab
ilit
y
an
d
th
e
ef
f
icac
y
o
f
th
e
ANN
d
esig
n
in
id
en
tify
in
g
th
e
f
u
n
d
am
e
n
tal
p
atter
n
s
with
in
th
e
d
ata
.
T
h
e
co
r
r
elatio
n
b
et
wee
n
tr
ain
in
g
an
d
test
in
g
r
e
s
u
lts
h
ig
h
lig
h
ts
th
e
d
ep
en
d
a
b
ilit
y
an
d
r
elev
an
ce
o
f
th
e
ANN
m
o
d
el
in
r
ea
l
-
wo
r
l
d
en
er
g
y
f
o
r
ec
asti
n
g
co
n
tex
ts
.
T
h
e
co
m
p
ar
is
o
n
o
f
ac
tu
al
en
er
g
y
d
ata
with
test
in
g
d
ata
u
s
in
g
ANN
an
d
PC
A
m
o
d
els in
th
e
L
E
PE,
as
de
p
icted
in
T
ab
le
2
an
d
Fig
u
r
e
3
,
d
em
o
n
s
tr
ates
a
co
m
p
a
r
ab
le
lev
el
o
f
r
esem
b
lan
ce
.
T
h
e
ANN+
PC
A
m
o
d
el
ac
cu
r
ately
p
r
ed
icts
th
e
r
ea
l
e
n
er
g
y
u
s
e
in
th
e
L
E
PE
f
o
r
tr
ai
n
in
g
d
ata
r
a
n
g
in
g
f
r
o
m
1
to
5
0
.
A
d
is
p
ar
ity
was
s
ee
n
in
th
e
4
1
s
t
d
ataset,
wh
er
e
th
e
m
ea
s
u
r
ed
e
n
er
g
y
was
1
9
2
.
0
1
7
9
5
9
W
h
,
h
o
wev
e
r
,
th
e
e
n
er
g
y
m
ea
s
u
r
em
en
t
o
b
tain
ed
b
y
th
e
u
tili
za
tio
n
o
f
th
e
ANN+
P
C
A
m
o
d
el
was
1
9
1
.
9
9
9
1
W
h
.
T
h
e
test
u
tili
ze
d
an
ac
tu
al
en
er
g
y
d
if
f
er
en
ce
o
f
3
4
8
.
7
2
8
7
W
h
f
o
r
th
e
5
0
th
d
ata,
wh
er
ea
s
th
e
ANN+
PC
A
m
o
d
el
u
tili
ze
d
2
3
5
.
3
9
8
4
W
h
.
Fig
u
r
e
4
s
h
o
w
s
test
in
g
p
r
ed
ictio
n
en
e
r
g
y
(
ac
tu
al)
v
s
.
ANN+
PC
A,
s
p
e
cif
ically
Fig
u
r
e
4
(
a
)
s
h
o
w
s
th
e
test
in
g
an
d
o
u
tp
u
t
d
ata
,
an
d
Fig
u
r
e
4
(
b
)
s
h
o
w
s
th
e
test
in
g
d
ataset
in
d
ex
.
A
to
tal
o
f
1
7
an
d
5
0
tr
ain
in
g
d
ata
s
ets
wer
e
ca
r
ef
u
lly
ch
o
s
en
f
o
r
th
e
test
in
g
p
h
ase
to
g
u
ar
an
tee
a
b
r
o
ad
r
ep
r
esen
tatio
n
o
f
th
e
d
a
taset.
T
h
e
s
elec
tio
n
o
f
test
d
a
ta
co
n
f
o
r
m
ed
to
th
e
cr
iter
ia
o
u
tlin
ed
in
T
ab
le
3
,
g
u
ar
an
teein
g
co
n
s
is
ten
cy
an
d
alig
n
m
en
t
with
th
e
ex
p
er
im
e
n
tal
d
esig
n
.
T
h
e
test
r
esu
lts
,
s
h
o
wn
in
Fig
u
r
e
5
,
d
em
o
n
s
tr
ate
s
ig
n
if
ican
t
d
if
f
e
r
en
c
es
b
etwe
en
th
e
an
ticip
ated
test
d
ata
p
o
in
ts
an
d
th
e
ac
tu
al
tar
g
et
test
d
ata
p
o
in
ts
wh
en
u
s
in
g
th
e
ANN
m
o
d
el.
T
h
e
s
ca
tter
p
lo
t
clea
r
ly
illu
s
tr
ates
th
is
d
is
cr
ep
an
cy
,
s
h
o
win
g
th
at
th
e
m
o
d
el'
s
p
r
ed
ictio
n
s
d
iv
er
g
e
m
ar
k
ed
ly
f
r
o
m
th
e
ta
r
g
et
v
alu
es
.
T
h
e
test
p
r
o
d
u
ce
d
a
co
r
r
elatio
n
co
ef
f
icien
t
(
R
)
o
f
0
.
4
6
1
6
9
,
i
n
d
icatin
g
a
r
ea
s
o
n
ab
le
b
u
t
in
ad
e
q
u
ate
c
o
n
n
e
ctio
n
b
etwe
en
th
e
an
ticip
ated
an
d
ac
tu
al
r
esu
lts
.
T
h
is
o
u
tco
m
e
h
ig
h
lig
h
ts
th
e
d
if
f
icu
lties
en
co
u
n
ter
ed
b
y
th
e
ANN
m
o
d
el
in
p
r
ec
is
ely
r
ep
r
esen
tin
g
th
e
co
n
n
ec
tio
n
s
with
in
t
h
e
test
d
ataset,
wh
ich
m
ay
b
e
attr
ib
u
tab
le
to
co
n
s
tr
ain
ts
in
m
o
d
el
co
m
p
lex
ity
,
d
ata
u
n
p
r
e
d
ictab
ilit
y
,
o
r
th
e
n
ee
d
f
o
r
f
u
r
th
e
r
o
p
tim
izatio
n
o
f
in
p
u
t
f
ea
tu
r
es
an
d
h
y
p
e
r
p
ar
a
m
eter
s
.
Ad
d
itio
n
al
s
tu
d
y
an
d
en
h
an
ce
m
en
t
o
f
th
e
ANN
m
o
d
el
m
ay
b
e
n
ec
ess
ar
y
to
au
g
m
en
t its
p
r
ed
icted
ac
c
u
r
ac
y
a
n
d
r
esil
ien
ce
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ad
v
Ap
p
l Sci
I
SS
N:
2252
-
8
8
1
4
E
n
h
a
n
ci
n
g
a
r
tifi
cia
l n
eu
r
a
l n
e
tw
o
r
k
p
erfo
r
ma
n
ce
fo
r
en
erg
y
efficien
cy
in
la
b
o
r
a
to
r
ies
… (
Desmir
a
)
315
(
a)
(
b
)
(
c)
(
d
)
(
e)
Fig
u
r
e
2
.
PC
A
an
d
ANN
d
ata
tr
ain
in
g
o
f
(
a)
tr
ai
n
in
g
R
=1
,
(
b
)
o
u
tp
u
t a
n
d
ta
r
g
et,
(
c
)
b
est tr
a
in
in
g
p
er
f
o
r
m
an
ce
,
(
d
)
g
r
ad
ien
t,
v
ali
d
atio
n
ch
ec
k
,
an
d
lear
n
in
g
r
at
e,
an
d
(
e
)
tr
ain
in
g
s
tate
p
lo
t
T
r
a
i
ni
ng
:
T
r
a
i
ni
ng
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2252
-
8
8
1
4
I
n
t J Ad
v
Ap
p
l Sci
,
Vo
l.
14
,
No
.
2
,
J
u
n
e
2
0
2
5
:
3
1
0
-
321
316
T
ab
le
2
.
T
r
ai
n
in
g
d
ata
No
D
a
y
/
D
a
t
e
P
r
e
ssu
r
e
(
a
t
m)
En
e
r
g
y
(
a
c
t
u
a
l
)
(
W
h
)
P
r
e
d
i
c
t
i
o
n
A
N
N
+
P
C
A
(
W
h
)
1
M
o
n
,
6
-
1
-
20
0
.
9
9
2
8
1
3
5
.
1
1
1
8
3
7
1
3
5
.
1
1
0
9
2
Tu
e
,
7
-
1
-
20
0
.
9
9
2
1
1
6
3
2
7
9
9
.
6
6
0
4
0
8
2
9
9
.
6
6
0
9
3
W
e
d
,
8
-
1
-
20
0
.
9
9
1
7
1
4
2
8
6
1
1
9
.
2
7
5
1
0
2
1
1
9
.
2
7
4
7
4
Th
u
,
9
-
1
-
20
0
.
9
9
2
3
2
0
4
0
8
3
1
2
.
5
6
1
6
3
3
3
1
3
.
6
1
6
5
5
F
r
i
,
1
0
-
1
-
20
0
.
9
9
1
5
8
7
7
5
5
1
3
2
.
1
5
3
4
6
9
1
3
2
.
1
5
1
6
6
M
o
n
,
1
3
-
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20
0
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9
9
1
6
9
5
9
1
8
2
3
7
.
8
0
8
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8
2
3
7
.
8
1
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6
7
Tu
e
,
1
4
-
1
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20
0
.
9
9
2
1
6
7
3
4
7
1
3
8
.
8
8
4
7
1
3
6
.
7
7
0
2
8
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e
d
,
1
5
-
1
-
20
0
.
9
9
1
5
5
1
0
2
2
6
1
.
1
0
2
0
4
1
2
6
1
.
0
9
9
7
9
Th
u
,
1
6
-
1
-
20
0
.
9
9
0
4
6
3
2
6
5
1
9
5
.
7
7
7
9
5
9
1
9
5
.
7
7
1
1
10
F
r
i
,
1
7
-
1
-
20
0
.
9
9
1
8
9
1
8
3
7
2
3
3
.
2
7
1
8
3
7
2
3
3
.
2
7
1
5
….
….
….
….
….
….
….
….
….
….
50
W
e
d
,
1
8
-
3
-
20
0
.
9
9
2
3
3
2
6
5
3
3
4
9
.
3
3
5
5
1
2
3
5
.
3
9
8
4
Fig
u
r
e
3
.
T
r
ain
in
g
p
r
ed
ictio
n
en
er
g
y
(
ac
tu
al)
v
s
.
ANN
+
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2252
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2252
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8
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RE
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NC
E
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[
1
]
J.
R
u
t
o
v
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t
z
a
n
d
A
.
A
t
h
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t
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:
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[
2
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J.
I
w
a
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M
w
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sh
a
,
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[
4
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J.
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.
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