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E
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I
J
E
CE
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Vo
l.
15
,
No
.
3
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J
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n
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20
25
,
p
p
.
3054
~
3
0
6
3
I
SS
N:
2088
-
8
7
0
8
,
DOI
: 1
0
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1
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v
15
i
3
.
pp
3
0
5
4
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3
3054
J
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ttp
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ec
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co
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Energ
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umpt
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thods
in
a
cybe
r
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phy
sica
l
sy
stem
K
enzheg
a
li Nurg
a
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Aky
lbek
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In
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d
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c
a
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s,
c
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p
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a
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sy
ste
m
s
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h
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c
o
m
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m
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u
stry
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h
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s
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teg
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c
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p
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ts
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ifi
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t
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ta,
wh
ich
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c
ial
to
a
c
c
u
ra
tely
m
o
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e
l.
Re
se
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rc
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rs
u
se
p
re
d
icti
v
e
a
n
a
ly
sis
t
o
d
e
v
e
lo
p
m
o
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e
ls
t
h
a
t
f
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re
c
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st
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e
n
d
s
a
n
d
sim
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late
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l
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wo
rl
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c
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it
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n
a
b
li
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m
t
o
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a
k
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b
e
tt
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-
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rm
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d
d
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isio
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s.
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h
is
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rti
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le
p
re
se
n
ts
a
c
o
m
p
a
ra
ti
v
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n
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ly
sis
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t
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e
ls
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a
ta
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s,
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sin
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rt
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ter
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o
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)
a
n
d
lo
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g
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rt
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term
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e
m
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TM
)
.
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p
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v
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s
:
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tatio
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p
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ce
s
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p
tio
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ed
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Ma
ch
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lear
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Op
tim
izatio
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alg
o
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ith
m
s
T
h
is i
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p
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a
rticle
u
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CC B
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p
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PS
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p
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n
t
t
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s
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t
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c
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l
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o
c
es
s
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v
i
a
f
e
e
d
b
a
ck
l
o
o
p
s
.
I
n
m
o
d
e
r
n
c
o
n
d
i
t
i
o
n
s
,
t
h
e
d
e
v
el
o
p
m
e
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t
o
f
C
PS
is
i
m
p
o
r
t
a
n
t
f
o
r
i
n
d
u
s
t
r
y
f
i
e
l
d
s
s
u
c
h
a
s
s
m
a
r
t
ci
t
ie
s
,
i
n
d
u
s
t
r
i
a
l
a
u
t
o
m
a
t
i
o
n
,
m
e
d
i
c
i
n
e
,
a
n
d
t
r
a
n
s
p
o
r
ta
t
i
o
n
.
C
PS
e
n
a
b
l
es
m
o
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e
f
f
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ci
e
n
t
a
n
d
i
n
t
e
l
l
i
g
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n
t
m
a
n
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g
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m
e
n
t
o
f
v
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r
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o
u
s
p
r
o
c
e
s
s
es
a
n
d
r
e
s
o
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r
c
e
s
,
w
h
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c
h
i
n
t
u
r
n
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e
q
u
i
r
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s
a
c
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r
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te
m
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s
f
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r
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c
ti
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a
n
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p
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z
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g
e
n
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y
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s
u
m
p
t
i
o
n
.
E
n
er
g
y
c
o
n
s
u
m
p
tio
n
in
C
PS
is
a
cr
itically
im
p
o
r
tan
t
asp
ec
t
as
it
d
ir
ec
tly
af
f
ec
ts
th
e
p
er
f
o
r
m
an
ce
,
r
eliab
ilit
y
,
an
d
o
p
er
atio
n
al
co
s
ts
o
f
s
y
s
tem
s
[
1
]
.
Pre
d
ictin
g
e
n
er
g
y
c
o
n
s
u
m
p
tio
n
allo
ws
f
o
r
ad
v
an
ce
d
p
lan
n
i
n
g
an
d
r
eso
u
r
ce
m
a
n
ag
em
en
t,
o
p
t
im
izin
g
th
eir
u
s
e
an
d
m
in
im
iz
in
g
co
s
ts
.
T
h
is
is
p
ar
ticu
lar
ly
im
p
o
r
tan
t g
iv
e
n
th
e
co
n
tin
u
o
u
s
ly
g
r
o
win
g
v
o
lu
m
es
o
f
d
ata
an
d
th
e
in
cr
ea
s
in
g
co
m
p
lex
ity
o
f
e
n
er
g
y
r
eso
u
r
ce
m
a
n
ag
em
en
t.
Pre
d
ictin
g
elec
tr
icity
u
s
ag
e
e
f
f
ec
tiv
ely
co
n
s
er
v
es
en
er
g
y
,
as
n
u
m
er
o
u
s
s
cien
tific
s
tu
d
ies
[
2
]
d
e
m
o
n
s
tr
ate
.
R
esear
ch
er
s
u
s
e
m
an
y
m
eth
o
d
s
at
th
e
s
am
e
t
im
e
to
g
u
ess
h
o
w
m
u
ch
elec
tr
icity
p
eo
p
le
will u
s
e.
T
h
ese
in
clu
d
e
f
u
zz
y
lo
g
ic
m
at
h
em
atica
l
m
o
d
els
[
3
]
,
d
ee
p
a
n
d
class
ical
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
es
[
4
]
,
[
5
]
,
an
d
m
o
d
els
th
at
tak
e
i
n
to
ac
c
o
u
n
t
h
o
w
elec
tr
icity
u
s
e
c
h
an
g
es
with
th
e
s
ea
s
o
n
s
a
n
d
th
e
i
n
f
lu
en
ce
o
f
v
ar
io
u
s
ch
ar
ac
ter
is
tics
[
6
]
,
[
7
]
.
C
o
n
s
id
er
in
g
th
e
g
r
ea
t
r
an
g
e
o
f
p
o
wer
co
n
s
u
m
p
tio
n
f
o
r
ec
asti
n
g
m
eth
o
d
o
lo
g
ies,
th
er
e
is
n
o
co
m
m
o
n
ap
p
r
o
ac
h
th
at
allo
ws
th
e
p
r
esen
tatio
n
o
f
a
r
eliab
le
elec
tr
icity
co
n
s
u
m
p
tio
n
f
o
r
ec
ast
f
o
r
ea
c
h
s
u
b
ject
ar
ea
.
T
h
e
m
ain
r
ea
s
o
n
s
f
o
r
th
is
in
clu
d
e
ev
o
lv
in
g
f
o
r
ec
asti
n
g
ac
cu
r
ac
y
s
tan
d
ar
d
s
,
th
e
n
ec
ess
ity
to
co
n
s
id
er
n
u
m
e
r
o
u
s
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
E
n
erg
y
co
n
s
u
mp
tio
n
p
r
ed
icti
o
n
meth
o
d
s
in
a
cy
b
er
-
p
h
ysica
l sys
tem
…
(
K
en
z
h
eg
a
li N
u
r
g
a
liyev
)
3055
f
ac
to
r
s
th
at
d
ef
in
e
th
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s
u
b
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ar
ea
'
s
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p
ec
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ics,
an
d
a
d
v
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m
en
ts
in
d
ata
m
in
in
g
tec
h
n
o
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ies,
wh
ich
en
ab
l
e
m
o
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e
ef
f
icien
t
h
an
d
lin
g
o
f
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ata
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m
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to
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n
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m
ath
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m
atica
l
s
tati
s
tics
m
eth
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s
.
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o
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e,
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o
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u
ct
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ap
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y
t
h
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ata
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eg
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ly
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e
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tim
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e
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em
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n
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w
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tech
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iq
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e.
Fo
llo
win
g
th
e
in
tr
o
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u
ctio
n
,
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e
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ar
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ata
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en
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o
d
el
s
elec
tio
n
.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
W
e
s
h
all
ca
teg
o
r
ize
th
e
f
o
r
ec
asts
em
p
lo
y
ed
in
th
is
s
tu
d
y
a
cc
o
r
d
in
g
to
th
e
d
u
r
atio
n
o
f
a
n
ticip
atio
n
b
ef
o
r
e
m
o
v
in
g
o
n
to
s
p
ec
if
ic
m
eth
o
d
s
.
Fo
r
ec
asts
ar
e
c
ateg
o
r
ized
d
i
f
f
er
en
tly
d
ep
en
d
in
g
o
n
h
o
w
lo
n
g
p
r
ed
ictio
n
n
ee
d
ed
[
8
]
.
W
e
s
h
all
f
o
llo
w
th
e
f
o
llo
win
g
g
u
id
elin
es
in
th
e
co
n
tex
t
o
f
th
is
in
v
esti
g
atio
n
.
T
h
e
p
r
o
jectio
n
s
th
at
we
will
tak
e
in
to
co
n
s
id
er
atio
n
ar
e
class
if
ied
as
lo
n
g
-
ter
m
,
wh
ich
s
p
an
s
s
ev
er
al
m
o
n
th
s
to
y
ea
r
s
,
an
d
as
s
h
o
r
t
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ter
m
,
wh
ich
s
p
an
s
d
ay
s
to
s
ev
er
al
w
ee
k
s
as
s
h
o
wn
in
Fig
u
r
e
1
.
B
y
s
ep
ar
atin
g
th
e
m
eth
o
d
o
l
o
g
ies
b
ased
o
n
th
e
e
x
p
ec
ted
lead
tim
e,
we
wer
e
ab
le
to
lo
o
k
m
o
r
e
clo
s
ely
at
th
e
ap
p
r
o
ac
h
es
th
at
wo
r
k
well
f
o
r
co
r
r
esp
o
n
d
i
n
g
lead
tim
e
f
o
r
ec
asti
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g
.
W
e
b
eliev
e
th
at
th
is
d
iv
is
io
n
o
f
a
p
p
r
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m
ak
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it
ea
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ier
f
o
r
u
s
to
ex
p
lo
r
e
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e
r
elate
d
liter
atu
r
e
an
d
h
elp
s
u
s
to
tag
th
e
ch
ar
ac
ter
is
tics
o
f
th
e
ap
p
licatio
n
co
n
d
itio
n
s
f
o
r
ev
e
r
y
f
o
r
ec
asti
n
g
p
er
io
d
.
Fig
u
r
e
1
.
Po
wer
c
o
n
s
u
m
p
tio
n
p
r
ed
ictio
n
ca
teg
o
r
ies
2
.
1
.
Sh
o
rt
-
t
er
m
f
o
re
ca
s
t
ing
Op
er
atio
n
al
f
o
r
ec
asts
ar
e
ess
en
tial
f
o
r
m
an
a
g
in
g
elec
tr
icity
co
n
s
u
m
p
tio
n
with
its
p
ea
k
lo
ad
s
wh
ich
ca
n
b
e
d
ec
r
ea
s
ed
b
y
h
av
in
g
a
f
o
r
ec
ast
th
at
is
ac
c
u
r
ate
o
n
e
d
ay
o
r
s
ev
er
al
wee
k
s
in
ad
v
an
ce
.
T
h
is
ty
p
e
o
f
f
o
r
ec
asti
n
g
is
r
elev
an
t
f
o
r
p
la
n
n
in
g
p
o
wer
d
em
an
d
s
u
p
p
o
r
ted
b
y
an
an
aly
s
is
o
f
s
m
all
p
o
wer
g
r
id
s
[
9
]
.
I
k
e
d
a
an
d
Nag
ai
[
1
0
]
p
r
esen
t
a
n
a
p
p
r
o
ac
h
t
h
at
allo
ws
o
p
tim
al
m
an
ag
em
en
t o
f
t
h
e
en
e
r
g
y
co
n
s
u
m
p
tio
n
o
f
a
b
u
ild
in
g
(
h
o
tel)
.
T
h
e
m
eth
o
d
m
ad
e
it p
o
s
s
ib
le
to
r
ed
u
ce
o
p
er
atin
g
c
o
s
ts
f
o
r
elec
tr
icity
co
n
s
u
m
p
tio
n
b
y
m
o
r
e
th
an
1
0
%.
T
h
e
r
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ch
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r
s
ex
p
lain
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at
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g
s
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ch
m
eth
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d
allo
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f
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r
th
e
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o
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lin
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r
ity
o
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p
o
wer
c
o
n
s
u
m
p
tio
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d
ata
f
o
r
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m
e
ty
p
es
o
f
e
q
u
ip
m
e
n
t
to
b
e
co
n
s
id
er
e
d
.
T
h
e
f
in
d
i
n
g
s
o
f
ap
p
l
y
in
g
m
ac
h
in
e
lear
n
in
g
to
f
o
r
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ast
o
f
f
ice
b
u
ild
in
g
co
o
lin
g
e
n
er
g
y
co
n
s
u
m
p
tio
n
wh
ile
tak
in
g
h
u
m
an
b
eh
av
io
r
in
to
ac
co
u
n
t
ar
e
p
r
es
en
ted
b
y
th
e
au
th
o
r
in
[
1
1
]
.
W
h
en
d
ev
elo
p
in
g
th
e
m
o
d
el,
s
ev
er
al
m
ac
h
i
n
e
lear
n
in
g
alg
o
r
ith
m
s
wer
e
test
ed
an
d
co
m
p
ar
ed
.
T
h
e
s
im
u
latio
n
r
esu
lts
d
em
o
n
s
tr
ated
th
e
g
r
ea
t
in
f
l
u
en
ce
o
f
th
e
v
ar
iab
les
co
n
s
id
er
ed
in
t
h
e
s
tu
d
y
o
n
th
e
tar
g
et
r
esu
lt
-
elec
tr
icity
co
n
s
u
m
p
tio
n
.
T
h
e
ac
tiv
ities
o
f
o
f
f
ice
b
u
i
ld
in
g
wo
r
k
er
s
af
f
ec
ted
en
er
g
y
co
n
s
u
m
p
tio
n
b
y
m
o
r
e
th
an
7
tim
es.
R
esear
ch
e
r
s
ar
g
u
e
in
[
1
2
]
th
at
a
p
r
ec
is
e
co
m
p
r
eh
en
s
io
n
o
f
en
er
g
y
lo
a
d
s
lo
p
e
is
e
s
s
en
tial
f
o
r
m
a
n
ag
in
g
p
lan
t
p
o
wer
s
y
s
tem
s
ef
f
ec
tiv
ely
an
d
s
er
v
e
s
as
th
e
f
o
u
n
d
atio
n
f
o
r
an
o
m
aly
id
en
tific
atio
n
.
Scien
tis
ts
al
s
o
n
o
te
th
at
lo
ad
cu
r
v
e
an
al
y
s
is
is
an
im
p
o
r
tan
t
ad
d
itio
n
in
th
e
ab
s
en
ce
o
f
m
eth
o
d
s
f
o
r
ass
ess
in
g
v
ar
io
u
s
tim
e
tr
an
s
itio
n
s
b
etwe
en
en
er
g
y
s
tates.
T
h
e
ar
ticle
p
r
esen
ts
th
e
r
esu
lts
o
f
u
tili
z
in
g
a
d
ee
p
s
p
atio
-
tem
p
o
r
al
r
esid
u
al
n
e
u
r
al
n
etwo
r
k
(
ST
-
R
esNet
)
to
f
o
r
ec
ast
p
o
wer
g
e
n
er
atio
n
(
s
ales)
[
1
3
]
.
Fo
r
b
o
t
h
s
h
o
r
t
-
ter
m
(
a
d
ay
)
an
d
m
e
d
iu
m
-
ter
m
(
a
wee
k
)
f
o
r
ec
asti
n
g
,
th
e
av
e
r
ag
e
ab
s
o
lu
te
p
er
ce
n
ta
g
e
f
o
r
e
ca
s
t
er
r
o
r
m
a
y
b
e
d
ec
r
ea
s
ed
b
y
m
o
r
e
th
an
2
.
5
p
er
ce
n
t
b
y
u
s
in
g
ST
-
R
esNet.
On
th
e
o
n
e
h
a
n
d
,
th
e
d
e
v
elo
p
m
en
t
an
d
ap
p
licatio
n
o
f
d
ata
m
in
in
g
m
eth
o
d
s
,
in
p
ar
ticu
lar
f
o
r
t
h
e
task
o
f
p
r
ed
ictin
g
elec
tr
ical
lo
ad
s
,
h
elp
s
to
r
ed
u
ce
an
d
r
atio
n
alize
th
e
u
s
e
o
f
r
eso
u
r
ce
s
.
Ho
wev
er
,
p
er
f
o
r
m
in
g
m
ac
h
in
e
lear
n
in
g
p
r
o
ce
d
u
r
es
r
eq
u
ir
es
s
ig
n
if
ican
t
co
m
p
u
tin
g
r
eso
u
r
ce
s
wh
ic
h
r
a
is
es th
e
am
o
u
n
t o
f
elec
tr
ical
e
n
er
g
y
u
s
ed
.
2
.
2
.
L
o
ng
-
t
er
m
f
o
re
ca
s
t
ing
On
g
o
in
g
f
o
r
ec
asts
ar
e
u
s
ed
t
o
p
lan
th
e
co
n
s
tr
u
ctio
n
o
r
r
e
n
o
v
atio
n
o
f
im
p
o
r
tan
t
in
f
r
astr
u
ctu
r
e
a
n
d
m
an
u
f
ac
tu
r
in
g
f
ac
ilit
ies,
as
well
as
to
d
e
v
elo
p
p
lan
s
f
o
r
th
e
ex
p
a
n
s
io
n
o
f
s
m
ar
t
en
er
g
y
s
y
s
tem
s
b
o
th
in
d
ep
en
d
en
tly
with
in
a
p
ar
tic
u
lar
in
d
u
s
tr
ial
s
ec
to
r
an
d
at
th
e
s
tate
lev
el.
A
v
ar
iety
o
f
lo
n
g
-
ter
m
s
ce
n
ar
io
s
ar
e
ty
p
ically
em
p
lo
y
ed
t
o
g
et
t
h
e
p
r
o
jectio
n
n
u
m
b
er
s
f
o
r
to
tal
e
n
er
g
y
co
n
s
u
m
p
tio
n
.
Fo
r
ec
asti
n
g
m
ak
es
it p
o
s
s
ib
le
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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&
C
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,
Vo
l.
15
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No
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3
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25
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to
r
eg
u
late
en
er
g
y
s
to
r
ag
e
d
e
v
ices
'
o
p
er
atin
g
m
o
d
es
o
p
tim
al
ly
,
wh
ich
p
r
o
m
o
tes
m
o
r
e
s
en
s
ib
le
u
s
ag
e
o
f
th
em
.
T
h
e
o
u
tco
m
es
o
f
a
p
p
ly
in
g
a
d
ec
is
io
n
tr
ee
m
o
d
el
to
an
ticip
ate
p
o
wer
u
s
ag
e
ar
e
s
h
o
wn
i
n
th
e
r
esear
ch
[
1
4
]
.
T
h
e
p
r
o
ce
d
u
r
e
o
f
f
ig
u
r
i
n
g
o
u
t
an
elec
tr
ical
s
to
r
ag
e
d
e
v
ice'
s
i
d
ea
l c
ap
ac
ity
m
ak
es
u
s
e
o
f
th
e
r
esu
lts
g
ath
er
ed
.
Ma
ch
in
e
lear
n
in
g
an
d
co
n
v
en
t
io
n
al
ap
p
r
o
ac
h
es
to
p
r
e
d
ictin
g
p
o
wer
u
s
e
wer
e
co
m
p
ar
e
d
in
th
e
p
ap
er
[
1
5
]
.
T
h
e
f
in
d
in
g
s
d
em
o
n
s
tr
a
te
th
at,
in
ter
m
s
o
f
f
o
r
ec
ast
ac
cu
r
ac
y
,
m
ac
h
in
e
lear
n
i
n
g
t
ec
h
n
iq
u
es
ex
ec
u
te
n
o
ticea
b
ly
b
etter
t
h
an
co
n
v
en
tio
n
al
tech
n
iq
u
es,
in
d
icatin
g
t
h
e
s
ig
n
if
ican
ce
o
f
d
ev
elo
p
in
g
p
r
ed
ictio
n
m
o
d
els
u
s
in
g
co
n
v
en
tio
n
al
m
ac
h
in
e
l
ea
r
n
in
g
m
eth
o
d
s
an
d
n
eu
r
al
n
etwo
r
k
s
.
T
h
e
p
r
i
m
ar
y
d
r
aw
b
ac
k
o
f
em
p
lo
y
in
g
th
ese
tech
n
iq
u
es,
as
m
en
tio
n
ed
in
[
1
6
]
,
is
th
eir
co
m
p
u
tat
io
n
al
co
m
p
lex
ity
,
wh
ich
m
a
k
es
im
p
r
o
v
in
g
d
ata
m
in
in
g
alg
o
r
ith
m
s
'
ef
f
icien
cy
a
p
ar
ticu
lar
ly
p
er
tin
e
n
t
jo
b
.
A
m
o
d
el
was
cr
ea
ted
in
[
1
7
]
th
at
co
m
b
in
es
an
en
tire
ly
co
n
n
ec
ted
n
e
u
r
al
n
et
wo
r
k
u
s
in
g
s
in
g
le
s
p
ec
tr
u
m
ev
alu
atio
n
to
s
ep
ar
ate
th
e
ti
m
e
s
er
ies
o
f
p
o
wer
co
n
s
u
m
p
tio
n
.
Su
ch
an
ap
p
licatio
n
is
a
p
r
o
m
is
in
g
d
ir
ec
tio
n
,
s
in
ce
it
u
s
es
a
co
m
b
in
atio
n
o
f
s
ev
er
al
m
eth
o
d
s
o
f
in
tellectu
al
an
aly
s
is
.
Scien
tis
t
s
o
b
s
er
v
e,
n
o
n
eth
eless
,
th
e
r
estrictio
n
s
in
th
e
u
s
e
o
f
s
o
m
e
te
ch
n
iq
u
es
ev
en
with
th
e
m
o
s
t
co
n
tem
p
o
r
ar
y
to
o
ls
to
esti
m
ate
p
o
wer
u
s
ag
e
[
1
8
]
.
T
h
is
ju
s
t
r
ea
f
f
ir
m
s
th
e
n
ec
ess
ity
o
f
r
esear
ch
to
u
p
d
ate
an
d
b
r
o
ad
e
n
th
e
to
o
l
k
it
o
f
m
eth
o
d
s
u
s
ed
to
s
o
lv
e
th
e
p
o
wer
co
n
s
u
m
p
tio
n
f
o
r
ec
asti
n
g
p
r
o
b
lem
.
T
h
er
ef
o
r
e,
co
m
p
r
eh
e
n
s
iv
e
an
a
ly
s
es
s
u
ch
as
in
[
1
9
]
,
wh
ich
r
ef
lect
th
e
cu
r
r
en
t
lev
el
o
f
r
ese
ar
ch
in
p
o
wer
lo
ad
p
r
ed
ictio
n
,
ar
e
ad
v
is
ed
to
b
e
c
o
n
d
u
cte
d
.
Hy
b
r
id
f
o
r
ec
asti
n
g
i
s
n
ec
ess
ar
y
to
c
r
ea
te
r
ec
o
m
m
e
n
d
atio
n
s
r
e
g
ar
d
in
g
th
e
p
o
s
s
ib
le
u
s
e
o
f
ess
en
tial
b
u
ild
in
g
f
ac
ilit
ies
d
u
e
to
th
e
s
cien
tific
co
m
m
u
n
ity
'
s
in
ter
est
in
e
n
er
g
y
-
s
av
in
g
is
s
u
es,
th
e
ad
v
an
ce
m
en
t
o
f
s
m
ar
t
d
ata
p
r
o
ce
s
s
in
g
m
et
h
o
d
s
an
d
th
e
n
ec
ess
ity
o
f
e
n
er
g
y
-
s
av
in
g
s
o
lu
tio
n
s
in
r
ea
l
-
wo
r
ld
ap
p
licatio
n
s
.
2
.
3
.
H
y
brid f
o
re
ca
s
t
ing
No
wad
ay
s
,
ac
h
iev
in
g
m
a
x
im
u
m
en
er
g
y
ef
f
icien
cy
is
an
ticip
ated
to
b
e
th
e
m
ain
em
p
h
asis
o
f
in
ter
n
et
o
f
th
in
g
s
(
I
o
T
)
in
n
o
v
atio
n
in
th
e
f
u
tu
r
e.
T
h
is
is
s
u
e
m
ay
b
e
m
et
b
y
in
co
r
p
o
r
atin
g
ar
tific
ial
in
tellig
en
ce
(
AI
)
-
p
o
wer
ed
tech
n
o
lo
g
ies s
u
ch
as m
ac
h
in
e
lear
n
in
g
(
ML
)
an
d
d
e
ep
lear
n
in
g
[
2
0
]
.
AI
r
esear
ch
f
ield
s
ar
e
ad
v
an
ce
d
as
a
r
esu
lt
o
f
ML
s
y
s
tem
s
'
co
n
s
tan
t
s
elf
-
im
p
r
o
v
em
en
t.
ML
em
p
lo
y
s
alg
o
r
ith
m
s
th
at
e
n
ab
le
th
em
to
r
ea
ct
to
en
v
ir
o
n
m
en
tal
in
p
u
ts
an
d
r
ec
o
g
n
ize
n
o
n
lin
ea
r
r
elatio
n
s
h
ip
s
in
co
m
p
lex
o
r
u
n
ce
r
tain
s
y
s
te
m
s
.
Pre
d
ictin
g
h
o
w
m
u
ch
e
n
er
g
y
a
C
PS
will
u
s
e
in
v
ar
io
u
s
tim
e
in
ter
v
als
is
a
u
s
ef
u
l
ap
p
r
o
ac
h
to
o
p
tim
ize
e
n
er
g
y
u
s
e,
f
o
r
ec
ast
f
u
tu
r
e
e
n
er
g
y
r
eq
u
i
r
em
en
ts
,
a
n
d
s
p
o
t
p
o
s
s
ib
le
in
ef
f
icien
cie
s
in
en
er
g
y
u
s
e,
am
o
n
g
o
t
h
er
ag
en
ts
in
a
s
m
ar
t
g
r
id
.
T
h
e
q
u
ality
a
n
d
r
elev
an
c
y
o
f
th
e
d
ata
th
at
ML
al
g
o
r
ith
m
s
u
s
e
h
as
a
s
ig
n
if
ican
t
im
p
a
ct
o
n
h
o
w
well
th
ey
o
p
er
ate.
E
lectr
icity
u
s
ag
e
wa
s
d
iv
id
ed
i
n
to
s
ev
er
al
ca
teg
o
r
ies
in
[
2
1
]
:
r
aw
m
ea
s
u
r
em
e
n
ts
an
d
r
ec
o
r
d
s
o
f
p
r
iv
ate
lo
a
d
s
(
air
c
o
n
d
itio
n
er
s
,
r
ef
r
ig
e
r
ato
r
s
)
at
ce
r
tain
p
er
io
d
s
as
it
en
ab
les
p
r
ec
is
e
en
er
g
y
m
an
ag
em
e
n
t,
lo
a
d
d
is
ag
g
r
eg
atio
n
,
an
d
ap
p
lian
ce
id
en
tific
atio
n
.
Ma
ch
in
e
lear
n
in
g
al
g
o
r
ith
m
s
h
av
e
o
v
e
r
co
m
e
th
e
p
r
im
ar
y
s
h
o
r
tco
m
in
g
s
o
f
h
y
b
r
id
p
r
ed
ictio
n
s
y
s
tem
s
.
Fo
r
in
s
tan
ce
,
s
cien
tis
t
s
in
[
2
2
]
d
ev
elo
p
ed
a
f
o
r
ec
asti
n
g
m
o
d
el
f
o
r
b
u
ild
in
g
en
e
r
g
y
u
s
ag
e
b
y
in
teg
r
atin
g
ex
p
an
d
e
d
s
h
o
r
t
-
ter
m
m
em
o
r
y
n
etwo
r
k
s
with
a
s
in
e
-
co
s
in
e
o
p
tim
izatio
n
al
g
o
r
ith
m
to
p
r
o
v
id
e
p
r
ed
ictio
n
s
i
n
r
ea
l
tim
e.
Su
b
s
eq
u
en
tly
,
Su
r
a
n
ata
et
a
l.
[
2
3
]
co
n
ce
n
tr
ated
o
n
f
o
r
ec
asti
n
g
d
in
in
g
r
o
o
m
en
e
r
g
y
u
s
ag
e.
Au
th
o
r
s
u
s
ed
L
STM
m
o
d
el;
its
f
ea
tu
r
es
wer
e
ex
tr
ac
ted
u
s
in
g
p
r
i
n
cip
al
co
m
p
o
n
en
t
a
n
aly
s
is
(
PC
A)
.
Fu
r
th
er
m
o
r
e,
Sh
ap
i
et
a
l.
[
2
4
]
u
s
ed
clo
u
d
-
b
ased
m
ac
h
in
e
lea
r
n
in
g
f
r
am
ewo
r
k
(
Mic
r
o
s
o
f
t
Azu
r
e)
to
o
f
f
er
f
o
llo
win
g
m
eth
o
d
o
l
o
g
y
.
Usi
n
g
th
r
ee
s
tr
ateg
ies
(
k
-
n
ea
r
est
n
eig
h
b
o
r
s
,
ar
tific
ial
n
eu
r
al
n
etwo
r
k
s
,
an
d
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
es),
th
e
s
tu
d
y
em
p
lo
y
s
two
ten
an
ts
f
r
o
m
an
in
d
u
s
tr
ial
b
u
ild
in
g
in
Ma
lay
s
ia.
T
h
e
ex
p
er
im
en
tal
f
in
d
in
g
s
s
h
o
w
th
at
ea
ch
r
e
n
ter
'
s
en
er
g
y
co
n
s
u
m
p
tio
n
h
as
a
ce
r
tain
d
is
tr
ib
u
tio
n
p
atter
n
,
an
d
th
e
p
r
o
p
o
s
ed
m
o
d
el
is
ca
p
ab
le
o
f
p
r
ec
is
ely
ca
lcu
latin
g
ea
ch
r
e
n
ter
'
s
en
er
g
y
co
n
s
u
m
p
tio
n
.
Hy
b
r
id
n
etwo
r
k
is
a
ter
m
u
s
e
d
to
d
escr
ib
e
s
o
m
e
in
ter
c
o
n
n
ec
ted
s
y
s
tem
s
with
d
if
f
er
en
t
f
o
r
ec
asti
n
g
is
s
u
es.
Mo
h
am
m
ed
et
a
l.
[
2
5
]
f
o
cu
s
o
n
e
n
h
an
ci
n
g
t
h
er
m
al
co
m
f
o
r
t
an
d
en
er
g
y
l
o
ad
s
in
h
ea
tin
g
,
v
en
tilatio
n
,
an
d
air
co
n
d
itio
n
i
n
g
(
HVAC
)
s
y
s
tem
s
b
y
th
e
ap
p
licatio
n
o
f
an
in
tellig
en
t
co
n
tr
o
l
alg
o
r
ith
m
.
T
h
e
au
th
o
r
s
s
u
g
g
est
o
p
tim
izin
g
h
ea
t
tr
an
s
f
er
co
ef
f
icien
ts
an
d
air
tem
p
e
r
atu
r
e
v
alu
es
b
y
c
o
m
b
in
in
g
s
u
p
er
v
is
o
r
y
co
n
tr
o
l
an
d
d
ata
ac
q
u
is
itio
n
(
SC
ADA
)
s
y
s
tem
s
with
a
s
m
ar
t
b
u
ild
in
g
m
an
ag
e
m
en
t
to
o
l.
T
h
en
,
g
en
etic
alg
o
r
ith
m
s
ar
e
u
tili
ze
d
to
m
in
im
ize
en
er
g
y
u
s
ag
e
an
d
p
r
eser
v
e
u
s
er
co
m
f
o
r
t.
R
eg
ar
d
in
g
p
o
wer
u
s
ag
e,
He
an
d
T
s
an
g
[
2
6
]
d
esig
n
ed
a
h
y
b
r
id
s
o
lu
tio
n
th
at
co
m
b
i
n
es
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
L
STM
)
with
I
m
p
r
o
v
e
d
c
o
m
p
lete
en
s
em
b
le
em
p
ir
ical
m
o
d
e
d
ec
o
m
p
o
s
itio
n
with
ad
ap
tiv
e
n
o
is
e
(
iC
E
E
MD
AN
)
.
R
e
s
ea
r
ch
er
s
u
s
ed
it
to
id
en
tify
tr
en
d
s
in
th
e
f
ir
s
t
p
o
wer
u
s
ag
e
d
ata,
an
d
t
h
en
th
ey
f
o
r
ec
asted
ea
ch
m
o
d
e
s
ep
ar
ately
u
s
in
g
a
B
ay
esian
-
o
p
tim
ized
L
STM
.
3.
M
E
T
H
O
D
T
h
i
s
s
e
ct
i
o
n
d
e
s
c
r
i
b
es
t
h
e
r
e
s
ea
r
c
h
m
e
t
h
o
d
s
u
s
e
d
t
o
p
r
e
d
i
c
t
e
n
e
r
g
y
d
e
m
a
n
d
i
n
C
P
S
.
F
o
r
e
ca
s
t
i
n
g
e
n
e
r
g
y
c
o
n
s
u
m
p
t
i
o
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is
a
c
o
m
p
l
e
x
t
a
s
k
t
h
a
t
r
e
q
u
i
r
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t
h
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v
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h
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i
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f
p
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e
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.
A
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o
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a
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ar
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m
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t
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h
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h
a
n
g
e
o
f
r
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n
d
o
m
o
n
e
s
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c
h
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c
k
t
h
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r
i
e
s
f
o
r
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h
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p
r
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s
e
n
c
e
o
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a
m
ai
n
t
r
e
n
d
a
n
d
,
i
f
t
h
e
r
e
i
s
o
n
e
,
t
o
i
s
o
l
a
t
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it
,
i
d
e
n
t
i
f
y
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h
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r
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n
d
a
n
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i
t
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d
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t
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n
.
R
e
g
a
r
d
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s
h
o
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t
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m
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as
s
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o
w
n
i
n
T
a
b
l
e
1
.
T
ab
le
1
.
Sh
o
r
t
-
ter
m
f
o
r
ec
asti
n
g
m
eth
o
d
s
an
aly
s
is
M
e
t
h
o
d
D
e
scri
p
t
i
o
n
a
n
d
Li
m
i
t
a
t
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o
n
s
M
a
t
h
Le
a
s
t
s
q
u
a
r
e
s
met
h
o
d
(
LS)
B
y
mi
n
i
mi
z
i
n
g
t
h
e
su
m
o
f
sq
u
a
r
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d
v
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i
t
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s
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se
d
t
o
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st
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ma
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h
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p
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met
e
r
s
o
f
mat
h
e
m
a
t
i
c
a
l
mo
d
e
l
s
[
2
7
]
.
I
t
d
e
scr
i
b
e
s
t
i
m
e
seri
e
s
c
h
a
n
g
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n
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S
l
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n
d
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h
i
f
t
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T
h
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s
a
f
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m
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u
a
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m
i
n
i
m
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z
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t
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o
n
i
n
r
e
a
d
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n
g
s
d
e
v
i
a
t
i
o
n
s
.
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ren
d
m
o
d
e
l
i
s
r
i
g
i
d
l
y
f
i
x
e
d
.
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i
n
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l
f
o
re
c
a
s
t
c
o
n
si
d
e
rs
a
n
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n
f
l
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n
c
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v
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l
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e
s.
H
i
g
h
s
e
n
s
i
t
i
v
i
t
y
t
o
d
e
t
e
c
t
i
n
g
u
n
u
s
u
a
l
d
a
t
a
p
o
i
n
t
s
.
S
lo
p
e
=
(
×
)
−
(
×
)
2
−
2
(
1
)
sh
i
f
t
=
−
s
l
o
p
e
×
(
2
)
(E
a
–
a
c
t
u
a
l
c
o
n
s
u
m
p
t
i
o
n
v
a
l
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n
–
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u
m
b
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t
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m
e
se
ri
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t
–
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m
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o
f
o
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s
e
rv
a
t
i
o
n
p
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r
i
o
d
)
.
(
)
=
×
+
ℎ
(
3
)
M
o
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g
a
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s
met
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o
d
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t
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l
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a
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t
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a
l
v
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l
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s
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Th
i
s
c
r
e
a
t
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s
a
n
i
m
p
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e
ssi
o
n
t
h
a
t
t
h
e
a
v
e
r
a
g
e
i
s
“
sl
i
d
i
n
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”
a
l
o
n
g
t
h
e
s
e
r
i
e
s
,
d
i
sc
a
r
d
i
n
g
o
n
e
l
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v
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l
a
n
d
a
d
d
i
n
g
t
h
e
n
e
x
t
e
a
c
h
t
i
me
[
2
8
]
.
F
l
u
c
t
u
a
t
i
o
n
s
a
r
e
r
e
p
l
a
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d
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h
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a
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c
m
e
a
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l
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d
t
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m
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ser
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e
s.
C
o
n
s
u
m
p
t
i
o
n
v
a
l
u
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s
(
t
i
m
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ser
i
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s)
a
r
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h
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t
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r
o
g
e
n
e
o
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s
.
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d
e
n
t
i
f
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c
a
t
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o
n
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f
l
a
r
g
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n
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m
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t
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s r
e
s
o
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r
c
e
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n
t
e
n
si
v
e
.
PC
=
−
1
×
1
×
(
−
−
1
)
(
4
)
(E
av
–
a
v
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ra
g
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s
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m
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n
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st
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n
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m
b
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t
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s)
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p
o
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l
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o
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h
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n
g
(
ES)
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t
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s
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se
d
t
o
f
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a
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f
u
t
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r
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l
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a
s
e
d
o
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p
a
s
t
o
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serv
a
t
i
o
n
s
,
u
sef
u
l
f
o
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d
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t
a
w
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t
h
o
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t
si
g
n
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f
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c
a
n
t
se
a
s
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n
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l
f
l
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c
t
u
a
t
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o
n
s.
T
h
e
l
a
r
g
e
r
t
i
m
e
ser
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s
i
n
t
e
r
v
a
l
w
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d
t
h
,
t
h
e
s
mo
o
t
h
e
r
a
t
r
e
n
d
w
i
l
l
b
e
[
2
9
]
.
T
ren
d
m
o
d
e
l
w
a
s
f
o
rm
e
d
e
n
d
o
f
t
h
e
c
h
o
se
n
p
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o
d
a
n
d
d
o
e
s
n
o
t
e
x
t
r
a
p
o
l
a
t
e
c
u
rre
n
t
d
e
p
e
n
d
e
n
c
i
e
s
i
n
t
o
l
o
n
g
f
u
t
u
r
e
.
Pr
o
v
i
d
e
s
a
m
u
c
h
t
i
g
h
t
e
r
v
a
l
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e
f
o
r
c
l
o
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o
u
t
p
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t
s
t
h
a
n
f
o
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l
o
n
g
-
t
e
rm
e
x
t
reme
.
+
1
=
+
(
−
)
(
5
)
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av
e
r
–
a
v
e
r
a
g
e
c
o
n
su
m
p
t
i
o
n
,
E
n
–
l
a
s
t
v
a
l
u
e
b
e
f
o
re
f
o
r
e
c
a
st
i
n
g
,
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-
sm
o
o
t
h
i
n
g
p
a
r
a
m
e
t
e
r)
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f
α →
1
,
t
h
e
n
t
h
e
i
n
f
l
u
e
n
c
e
o
f
p
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st
v
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l
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d
u
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e
d
,
o
n
l
y
t
h
e
l
a
st
v
a
l
u
e
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s
c
o
n
s
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d
e
r
e
d
f
o
r
t
h
e
f
o
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e
c
a
s
t
.
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o
n
s
i
d
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r
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n
g
l
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m
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t
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ti
o
n
s
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n
T
a
b
l
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1
,
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t
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s
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l
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at
t
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t
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m
e
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t
m
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d
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r
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d
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s
o
t
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l
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w
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n
g
m
a
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h
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l
e
a
r
n
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n
g
(
M
L
)
m
e
th
o
d
s
a
r
e
p
r
o
p
o
s
e
d
:
g
r
a
d
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n
t
b
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t
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n
g
r
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g
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(
XG
B
o
o
s
t
i
n
g
)
,
r
a
n
d
o
m
f
o
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e
s
t
(
R
F)
,
a
n
d
l
o
n
g
s
h
o
r
t
-
t
e
r
m
m
e
m
o
r
y
(
L
S
T
M
)
.
XGBo
o
s
t
is
a
p
o
wer
f
u
l
an
d
ef
f
ec
tiv
e
m
ac
h
in
e
lea
r
n
in
g
m
eth
o
d
f
o
r
r
eg
r
ess
io
n
an
d
clas
s
if
icatio
n
task
s
[
3
0
]
.
T
h
e
b
asis
f
o
r
it
is
i
n
th
e
id
ea
o
f
g
r
a
d
ien
t
b
o
o
s
tin
g
,
wh
ich
ag
g
r
eg
ates
s
ev
er
al
w
ea
k
m
o
d
els
(
u
s
u
ally
d
ec
is
io
n
tr
ee
s
)
in
to
o
n
e
p
o
wer
f
u
l
p
r
ed
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n
m
o
d
el.
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n
th
e
co
n
tex
t
o
f
e
n
er
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y
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n
s
u
m
p
tio
n
f
o
r
ec
asti
n
g
,
XGBo
o
s
t
allo
ws
to
ex
p
lo
r
e
i
n
tr
icate
n
o
n
lin
ea
r
d
ep
en
d
en
cies
an
d
in
ter
ac
tio
n
s
am
o
n
g
n
u
m
e
r
o
u
s
elem
en
ts
th
at
ef
f
ec
t
en
er
g
y
c
o
n
s
u
m
p
tio
n
,
th
er
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y
e
n
h
an
ci
n
g
th
e
ac
cu
r
ac
y
o
f
p
r
ed
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n
s
.
M
u
ltip
le
d
ec
is
io
n
tr
ee
s
ar
e
u
s
ed
in
RF
,
a
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
e,
to
im
p
r
o
v
e
p
r
ed
ictio
n
ac
cu
r
ac
y
an
d
d
e
p
en
d
a
b
ilit
y
[
3
1
]
.
T
h
is
tech
n
iq
u
e
allo
ws
f
o
r
th
e
ass
ess
m
en
t
o
f
t
h
e
s
ig
n
if
ican
ce
o
f
ea
c
h
f
ea
tu
r
e,
h
elp
in
g
to
in
te
r
p
r
et
th
e
m
o
d
el
an
d
i
d
en
tify
k
ey
f
ac
to
r
s
in
f
lu
e
n
cin
g
en
er
g
y
c
o
n
s
u
m
p
tio
n
.
R
F
is
well
-
s
u
ited
f
o
r
h
a
n
d
lin
g
lar
g
e
am
o
u
n
t
s
o
f
d
ata
an
d
ca
n
ef
f
ec
tiv
ely
p
r
o
ce
s
s
h
i
g
h
-
d
im
e
n
s
io
n
al
d
atasets
.
T
h
e
ty
p
e
o
f
r
ec
u
r
r
en
t
n
e
u
r
al
n
etwo
r
k
(
R
NN)
to
p
r
o
ce
s
s
tim
e
s
er
ies
an
d
to
ac
co
u
n
t
f
o
r
lo
n
g
-
ter
m
d
ep
en
d
en
cies
is
ca
lled
an
L
STM
[
3
2
]
.
T
h
is
m
eth
o
d
ca
n
ad
ap
t
to
ch
an
g
es
in
d
ata
an
d
d
y
n
am
ically
u
p
d
at
e
its
p
r
ed
ictio
n
s
b
ased
o
n
n
ew
d
ata.
B
ef
o
r
e
th
e
d
ata
is
u
tili
ze
d
to
tr
ain
an
d
test
th
e
m
o
d
els,
it
will
b
e
p
r
ep
ar
ed
an
d
e
x
am
in
e
d
.
C
ase
s
tu
d
y
wa
s
co
n
d
u
cted
u
s
in
g
J
u
p
iter
p
r
o
g
r
am
m
in
g
to
o
l
an
d
co
m
m
o
n
lib
r
ar
ies to
an
aly
ze
s
elec
ted
ML
alg
o
r
ith
m
s
.
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
.
3
,
J
u
n
e
20
25
:
3
0
5
4
-
3
0
6
3
3058
4.
CASE
S
T
UD
Y
4
.
1
.
Da
t
a
prepro
ce
s
s
ing
T
h
e
d
ataset
co
n
s
is
ts
o
f
in
f
o
r
m
atio
n
co
llected
o
v
er
a
p
e
r
io
d
o
f
3
y
ea
r
s
f
r
o
m
I
o
T
s
e
n
s
o
r
s
th
at
wer
e
in
s
talled
in
th
e
b
u
ild
i
n
g
o
f
C
o
r
n
ell
Un
iv
er
s
ity
[
3
3
]
.
E
v
er
y
s
en
s
o
r
n
o
d
e
is
s
et
u
p
to
r
ec
o
r
d
a
n
d
o
p
er
ate
ev
er
y
2
4
h
o
u
r
s
o
n
a
v
er
ag
e
a
s
well
a
s
en
er
g
y
d
ata
was
r
ec
o
r
d
ed
to
o
.
T
h
e
ap
p
lian
ce
s
en
er
g
y
co
n
s
u
m
p
tio
n
(
MW)
is
to
b
e
m
ea
s
u
r
e
d
,
th
e
n
it
was
ch
o
s
en
as
th
e
tar
g
et
v
ar
iab
le.
Data
p
r
ep
r
o
ce
s
s
in
g
is
n
ec
ess
ar
y
to
id
en
tify
tr
en
d
s
an
d
f
ea
tu
r
es
in
elec
tr
ical
p
o
wer
co
n
s
u
m
p
tio
n
s
tatis
ti
cs.
Du
e
to
th
e
in
co
n
s
is
ten
t
v
alu
e
s
ca
les
b
etwe
en
f
ea
tu
r
es
an
d
m
is
s
in
g
d
ata,
p
r
ep
r
o
ce
s
s
in
g
is
tim
e
-
co
n
s
u
m
in
g
is
s
u
e
f
o
r
s
tan
d
ar
d
izatio
n
an
d
im
p
u
tatio
n
.
I
t
is
ev
id
en
t
th
at
th
e
d
ata
m
u
s
t
b
e
s
p
lit
in
to
two
s
ets:
tr
ain
in
g
a
n
d
test
s
am
p
les.
Ob
ject
s
elec
tio
n
f
o
r
ea
ch
s
am
p
le
was
p
er
f
o
r
m
ed
u
s
in
g
th
e
Gri
d
S
ea
r
ch
C
V
m
eth
o
d
.
Sev
er
al
k
ey
d
ata
p
r
e
p
ar
atio
n
s
tr
ateg
ie
s
wer
e
em
p
lo
y
ed
to
en
h
an
ce
f
o
r
ec
asti
n
g
ef
f
icien
c
y
.
On
e
s
u
ch
s
tr
ateg
y
is
f
ea
tu
r
in
g
en
g
in
ee
r
in
g
,
aim
in
g
to
e
x
p
lo
r
e
n
o
t
o
n
ly
th
e
r
elatio
n
s
h
ip
s
b
etwe
en
f
ea
t
u
r
e
s
b
u
t
also
h
o
w
th
ese
f
ea
tu
r
es
r
elate
to
th
e
tar
g
et
v
ar
iab
le.
Featu
r
e
en
g
in
ee
r
in
g
in
v
o
lv
ed
ex
tr
ac
tin
g
n
ew
f
ea
tu
r
es
f
r
o
m
ex
is
tin
g
d
ata.
C
o
r
r
elatio
n
a
n
aly
s
is
was
co
n
d
u
cted
to
estab
lis
h
r
elatio
n
s
h
ip
s
b
etwe
en
v
ar
i
o
u
s
v
ar
iab
les.
A
d
d
i
t
i
o
n
a
l
l
y
,
v
al
u
e
s
r
el
a
t
e
d
t
o
t
i
m
e
p
a
r
a
m
e
te
r
s
(
h
o
u
r
,
d
a
y
,
m
o
n
t
h
,
y
e
a
r
)
w
e
r
e
c
o
n
s
o
li
d
a
t
e
d
i
n
t
o
a
s
i
n
g
le
f
o
r
m
a
t
,
a
s
u
n
i
q
u
e
t
i
m
e
s
e
r
i
e
s
o
v
e
r
s
e
v
e
r
a
l
y
e
a
r
s
a
r
e
r
e
q
u
i
r
ed
f
o
r
f
o
r
e
c
a
s
ti
n
g
.
T
o
e
n
s
u
r
e
th
e
d
a
t
a
is
r
ea
d
y
f
o
r
m
o
d
e
l
i
n
g
,
w
e
c
h
e
c
k
e
d
f
o
r
m
i
s
s
i
n
g
o
r
e
m
p
t
y
v
a
l
u
e
s
,
d
u
p
l
i
c
a
t
e
s
,
a
n
d
a
d
d
r
e
s
s
e
d
o
u
t
l
i
e
r
s
u
s
i
n
g
o
t
h
e
r
s
t
a
ti
s
ti
c
a
l
m
e
t
h
o
d
s
.
T
h
i
s
p
r
o
c
e
s
s
a
i
d
s
in
p
r
o
v
i
d
i
n
g
m
o
r
e
r
e
l
e
v
a
n
t
a
n
d
v
a
l
u
a
b
l
e
i
n
f
o
r
m
a
t
i
o
n
.
I
d
e
n
t
i
f
y
i
n
g
c
o
r
r
e
l
a
t
i
o
n
b
e
t
w
e
e
n
v
a
r
i
a
b
l
es
i
s
b
e
n
e
f
i
c
ia
l
f
o
r
v
a
r
i
o
u
s
p
u
r
p
o
s
e
s
,
i
n
c
l
u
d
i
n
g
d
e
t
e
c
t
i
n
g
a
n
d
s
u
b
s
e
q
u
e
n
t
l
y
r
e
m
o
v
i
n
g
i
r
r
e
l
e
v
a
n
t
v
a
r
i
a
b
l
e
s
,
as
w
el
l
as
u
n
c
o
v
e
r
i
n
g
u
n
i
q
u
e
c
o
r
r
e
l
a
t
i
o
n
s
t
h
a
t
m
i
g
h
t
n
o
t
b
e
e
v
i
d
e
n
t
d
u
r
i
n
g
d
i
r
e
c
t
f
o
r
e
c
a
s
t
i
n
g
.
I
n
s
o
m
e
c
a
s
es
,
c
e
r
t
a
i
n
at
t
r
i
b
u
t
es
c
a
n
b
e
m
o
d
i
f
i
e
d
a
n
d
n
o
r
m
a
li
z
e
d
wi
t
h
i
n
a
s
p
e
ci
f
i
c
r
a
n
g
e
t
o
r
e
d
u
c
e
d
a
t
a
i
m
b
a
l
a
n
c
e
.
F
i
g
u
r
e
2
i
l
l
u
s
t
r
at
e
s
t
h
e
e
n
e
r
g
y
c
o
n
s
u
m
p
t
i
o
n
d
y
n
a
m
i
c
s
o
f
a
b
u
il
d
i
n
g
o
v
e
r
a
p
e
r
i
o
d
o
f
o
n
e
y
e
a
r
.
Fig
u
r
e
2
.
E
n
er
g
y
co
n
s
u
m
p
tio
n
f
o
r
1
-
y
ea
r
p
er
io
d
4
.
2
.
P
re
dict
iv
e
m
o
del dev
elo
pm
ent
As
was
m
en
tio
n
ed
b
ef
o
r
e,
th
e
p
r
o
p
o
s
ed
m
o
d
el
co
n
s
is
ts
o
f
t
h
r
ee
ML
alg
o
r
ith
m
s
to
an
al
y
z
e
p
atter
n
s
o
f
h
y
b
r
id
f
o
r
ec
asti
n
g
.
C
o
n
s
id
er
in
g
tim
e
s
er
ies
d
ata
with
d
if
f
er
en
t
len
g
t
h
s
,
L
STM
wo
r
k
s
ap
p
r
o
p
r
iately
.
I
t
is
h
elp
f
u
l
f
o
r
p
r
ed
ictin
g
en
er
g
y
u
s
e
b
ec
au
s
e
it
ca
n
h
an
d
le
v
ar
iab
le
-
len
g
th
s
eq
u
en
ce
s
,
r
ec
all
p
r
io
r
d
ata,
an
d
ca
p
tu
r
e
lo
n
g
-
ter
m
ass
o
ciatio
n
s
.
T
h
r
ee
lay
er
s
m
ak
e
u
p
th
e
L
STM
m
o
d
el
s
tr
u
ctu
r
e:
in
p
u
t
,
u
n
it,
an
d
o
u
tp
u
t.
L
o
n
g
s
eq
u
en
ce
s
o
f
d
ata
m
ay
b
e
ef
f
ec
tiv
el
y
co
llected
an
d
s
en
t
u
s
in
g
L
STM
.
A
m
ac
h
in
e
lear
n
in
g
tec
h
n
iq
u
e
ca
lled
r
an
d
o
m
f
o
r
est
r
eg
r
ess
o
r
co
m
b
in
es
m
a
n
y
d
ec
is
io
n
tr
e
es
to
p
r
o
v
i
d
e
a
p
r
ed
ictio
n
m
o
d
el
f
o
r
r
eg
r
ess
io
n
p
r
o
b
lem
s
.
A
p
o
r
tio
n
o
f
th
e
tr
a
in
in
g
d
ata
an
d
f
ea
tu
r
es
is
ch
o
s
en
at
r
an
d
o
m
t
o
b
u
ild
ea
c
h
tr
e
e.
T
h
e
f
in
al
o
u
tp
u
t
is
p
r
o
d
u
ce
d
d
u
r
in
g
p
r
ed
ictio
n
b
y
th
e
r
eg
r
ess
o
r
co
m
b
in
i
n
g
p
r
ed
ictio
n
s
f
r
o
m
ea
c
h
tr
ee
as
s
h
o
wn
in
Fig
u
r
e
3
.
T
h
is
m
eth
o
d
is
wid
ely
o
p
er
ate
d
f
o
r
p
atter
n
r
ec
o
g
n
itio
n
a
n
d
p
r
ed
ictio
n
as
it
ca
n
a
n
aly
ze
c
o
m
p
licated
b
e
h
av
io
r
.
Failu
r
es
at
ev
er
y
lev
el
ar
e
g
i
v
en
p
r
io
r
ity
in
g
r
ad
ien
t
b
o
o
s
tin
g
ap
p
r
o
ac
h
.
T
h
is
alg
o
r
ith
m
lo
wer
s
p
r
ed
ictio
n
er
r
o
r
a
n
d
im
p
r
o
v
es
p
r
e
d
ictio
n
p
er
f
o
r
m
a
n
ce
b
y
m
atch
i
n
g
wea
k
tr
ain
in
g
s
ets
to
th
e
lo
s
s
f
u
n
ctio
n
.
Gr
ad
ien
t
b
o
o
s
tin
g
was
u
s
ed
d
u
e
to
its
s
tr
o
n
g
p
r
e
d
ictiv
e
p
e
r
f
o
r
m
an
ce
,
ca
p
ac
ity
to
r
ec
o
g
n
ize
in
tr
icat
e
d
ata
r
elatio
n
s
h
ip
s
an
d
n
o
n
lin
ea
r
m
o
d
els,
an
d
ad
a
p
tab
ilit
y
f
ea
tu
r
es.
Ti
me
p
e
r
i
o
d
To
t
a
l
c
o
n
s
u
m
p
t
i
o
n
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
E
n
erg
y
co
n
s
u
mp
tio
n
p
r
ed
icti
o
n
meth
o
d
s
in
a
cy
b
er
-
p
h
ysica
l sys
tem
…
(
K
en
z
h
eg
a
li N
u
r
g
a
liyev
)
3059
Fig
u
r
e
3
.
R
an
d
o
m
f
o
r
est r
eg
r
e
s
s
o
r
alg
o
r
ith
m
4
.
3
.
E
v
a
lua
t
io
n
T
o
av
o
id
o
v
e
r
f
itti
n
g
,
a
s
tr
aig
h
tf
o
r
war
d
d
ata
p
ar
titi
o
n
i
n
g
tech
n
iq
u
e
was
u
s
ed
th
r
o
u
g
h
o
u
t
t
wo
s
ets
o
f
d
ata.
As
was
m
en
tio
n
ed
ab
o
v
e,
th
e
d
ataset
was
d
iv
id
ed
in
to
tr
ain
in
g
(
2
0
%
o
f
g
i
v
en
d
ata
)
an
d
test
in
g
(
8
0
%
o
f
d
ata)
s
ets.
ML
alg
o
r
ith
m
s
a
n
d
p
r
e
d
ictio
n
m
o
d
els
f
o
r
co
n
s
u
m
p
tio
n
r
ec
o
r
d
in
g
s
wer
e
tr
ai
n
ed
o
n
t
r
ain
in
g
s
et.
T
h
e
ef
f
ec
tiv
en
ess
o
f
s
elec
ted
m
o
d
els wa
s
ass
es
s
ed
u
s
in
g
a
test
in
g
s
et
as sh
o
wn
in
Fig
u
r
e
4
.
Fig
u
r
e
4
.
Pre
d
ictio
n
m
o
d
el
with
u
s
ed
d
ata
m
o
d
ellin
g
T
h
e
ac
cu
r
ac
y
an
d
e
f
f
icien
cy
o
f
p
r
ed
ictio
n
m
o
d
els
p
r
o
d
u
ce
d
b
y
ML
alg
o
r
ith
m
s
wer
e
ev
alu
ated
u
s
in
g
m
etr
ics
s
u
ch
as
m
ea
n
ab
s
o
lu
t
e
p
er
ce
n
tag
e
er
r
o
r
(
MA
PE
)
.
W
h
en
co
n
tr
asti
n
g
th
e
r
an
g
e
o
f
ac
tu
al
v
alu
es
o
r
v
ar
io
u
s
tim
e
s
er
ies
with
th
e
p
r
o
jecte
d
ac
cu
r
ac
y
o
f
a
m
o
d
el,
MA
PE
ca
n
b
e
c
o
n
s
id
er
ed
as
h
elp
f
u
l
to
o
l
d
u
e
to
f
ac
t
th
at
MA
PE
is
ab
le
to
s
ca
le
th
e
er
r
o
r
m
ea
s
u
r
e
to
th
e
ac
tu
al
v
alu
e.
T
h
e
ac
c
u
r
ac
y
in
c
r
ea
s
es
as
th
e
MA
PE
d
ec
r
ea
s
es
[
3
4
]
.
T
h
is
is
a
wid
ely
u
s
ed
tech
n
i
q
u
e
f
o
r
ca
lcu
l
atin
g
p
r
e
d
ictio
n
er
r
o
r
s
,
wh
ich
is
ea
s
ier
to
g
r
asp
b
ec
au
s
e
o
f
its
s
ca
led
u
n
its
.
Fo
r
ev
er
y
tim
e
p
er
i
o
d
,
it
co
m
p
u
t
es
th
e
av
e
r
ag
e
a
b
s
o
lu
te
p
er
ce
n
t
er
r
o
r
,
m
in
u
s
r
ea
l
v
alu
es,
d
iv
id
ed
b
y
ac
t
u
al
v
al
u
es.
T
h
e
q
u
ality
m
etr
ics
wer
e
o
b
tain
ed
b
y
MA
PE,
s
h
o
win
g
th
e
lev
el
o
f
er
r
o
r
s
m
ad
e
b
y
th
e
alg
o
r
ith
m
in
p
er
c
en
tag
e:
M
A
PE =
1
×
(
−
×
100%
)
(
6
)
T
h
er
e
ar
e
2
s
ce
n
ar
io
s
o
f
a
n
aly
tics
:
(
1
)
en
er
g
y
in
d
ex
d
u
r
in
g
g
iv
en
p
e
r
io
d
(
ac
tu
al
v
alu
es),
(
2
)
en
er
g
y
co
n
s
u
m
p
tio
n
af
ter
o
p
tim
izatio
n
(
p
r
e
d
icted
v
alu
es)
5.
DIS
CU
SS
I
O
N
T
ab
le
2
in
d
icate
s
th
at
lo
wer
MA
PE
v
alu
es
s
u
g
g
est
a
b
etter
m
o
d
el
f
it
s
in
ce
th
ey
i
d
en
tify
th
e
d
if
f
er
en
ce
b
etwe
en
an
ticip
ate
d
an
d
ac
tu
al
d
ata.
As
it
f
o
llo
ws
f
r
o
m
T
ab
le
2
,
in
t
h
e
ca
s
e
o
f
m
o
d
el
o
n
ac
tu
al
p
o
wer
co
n
s
u
m
p
tio
n
d
ata,
th
e
least
v
alu
e
was
s
h
o
wn
b
y
th
e
m
o
d
el
b
ased
o
n
th
e
ex
tr
em
e
g
r
ad
ien
t
b
o
o
s
tin
g
r
eg
r
ess
io
n
alg
o
r
ith
m
(
XGBo
o
s
t)
-
th
e
er
r
o
r
was
3
6
.
3
%
wh
i
ch
is
less
th
an
o
th
e
r
m
o
d
els.
T
h
e
GB
R
ap
p
r
o
ac
h
p
er
f
o
r
m
ed
e
x
ce
p
tio
n
ally
well
in
ev
er
y
b
u
il
d
in
g
.
No
n
eth
eles
s
,
th
er
e
wer
e
d
if
f
er
e
n
ce
s
in
p
e
r
f
o
r
m
a
n
ce
b
etwe
en
b
u
ild
in
g
s
wh
en
c
o
m
p
a
r
in
g
L
STM
with
r
a
n
d
o
m
f
o
r
est
tech
n
i
q
u
e.
Fu
r
th
er
m
o
r
e,
L
STM
p
r
o
d
u
ce
d
less
m
is
tak
es
th
an
R
an
d
o
m
Fo
r
est,
wh
ich
s
h
o
wed
a
s
ig
n
if
ican
t d
if
f
er
e
n
ce
.
T
h
is
d
is
co
v
er
y
im
p
lies
th
at
L
STM
o
u
tp
er
f
o
r
m
ed
R
F
in
ter
m
s
o
f
m
is
tak
es
an
d
g
en
er
ated
f
ewe
r
er
r
o
r
s
o
v
er
all.
Af
ter
th
is
an
aly
s
is
,
it
was
ev
id
en
t
th
at
XGBo
o
s
tin
g
ap
p
r
o
ac
h
wo
r
k
e
d
th
e
b
est in
all
b
u
ild
in
g
s
.
I
t
is
ev
id
en
t
f
r
o
m
th
e
d
ata
p
r
o
v
id
ed
in
T
a
b
le
3
th
at
r
an
d
o
m
f
o
r
est
ap
p
r
o
ac
h
,
wh
ich
ex
h
ib
its
h
ig
h
ac
cu
r
ac
y
,
is
th
e
o
n
e
th
at
co
m
e
s
th
e
clo
s
est
to
th
e
ac
tu
al
test
i
n
g
r
esu
lts
.
R
eg
ar
d
in
g
th
e
p
r
ec
is
io
n
o
f
ca
lcu
latin
g
av
er
ag
e
co
n
s
u
m
p
tio
n
,
XGBo
o
s
tin
g
alg
o
r
ith
m
c
o
m
es
in
b
o
tto
m
,
f
o
llo
wed
b
y
L
STM
t
ec
h
n
iq
u
e
in
s
ec
o
n
d
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
.
3
,
J
u
n
e
20
25
:
3
0
5
4
-
3
0
6
3
3060
p
lace
.
No
n
et
h
eless
,
th
er
e
is
a
cr
u
cial
p
er
f
o
r
m
a
n
ce
d
is
p
ar
ity
b
etwe
en
L
STM
a
n
d
XGBo
o
s
t
in
g
,
in
d
icatin
g
t
h
at
b
o
th
m
et
h
o
d
s
ar
e
n
o
t
a
p
p
r
o
p
r
iate
f
o
r
th
e
s
am
e
t
y
p
es
o
f
d
ata.
Alth
o
u
g
h
L
STM
is
h
elp
f
u
l
f
o
r
h
a
n
d
lin
g
n
o
n
lin
ea
r
,
tim
e
-
d
ep
e
n
d
en
t
d
at
a
b
ec
au
s
e
o
f
its
r
ec
u
r
r
in
g
n
atu
r
e,
XGBo
o
s
tin
g
p
er
f
o
r
m
s
well
in
all
s
ce
n
ar
io
s
.
T
ab
le
2
.
Pre
d
ictio
n
p
er
f
o
r
m
a
n
ce
Te
st
i
n
g
s
e
t
M
e
t
h
o
d
M
A
P
E
(
%)
B
u
i
l
d
i
n
g
1
RF
7
6
.
9
LSTM
5
6
.
0
X
G
B
o
o
st
i
n
g
4
0
.
2
B
u
i
l
d
i
n
g
2
RF
3
8
.
4
LSTM
4
8
.
1
X
G
B
o
o
st
i
n
g
5
2
.
5
B
u
i
l
d
i
n
g
3
RF
5
6
.
5
LSTM
6
4
.
8
X
G
B
o
o
st
i
n
g
3
6
.
3
T
ab
le
3
.
Pre
d
ictio
n
p
er
f
o
r
m
a
n
ce
u
s
in
g
th
r
ee
p
r
esen
ted
ML
m
eth
o
d
s
S
c
e
n
a
r
i
o
1
A
c
t
u
a
l
me
t
r
i
c
s
S
c
e
n
a
r
i
o
2
RF
S
c
e
n
a
r
i
o
2
LSTM
S
c
e
n
a
r
i
o
2
X
G
B
o
o
o
s
t
5
6
0
2
5
6
2
6
5
6
3
7
5
5
2
7
6
8
4
2
6
8
9
3
6
8
8
4
6
4
1
7
6
1
7
3
6
1
7
8
6
3
6
6
6
3
5
4
5
7
8
9
5
7
3
0
5
5
9
6
6
3
5
5
C
o
m
p
ar
in
g
to
o
th
er
ar
ticles,
s
cien
tis
t
s
an
d
r
esear
ch
er
s
a
r
e
in
cr
ea
s
in
g
ly
u
s
ed
h
y
b
r
id
m
o
d
els
th
at
co
m
b
in
e
m
o
r
e
th
an
two
m
eth
o
d
o
lo
g
ies.
T
h
e
s
tu
d
ies
p
r
esen
te
d
in
T
ab
le
4
d
escr
ib
e
v
ar
i
o
u
s
ap
p
r
o
ac
h
es
u
s
ed
to
p
r
ed
ict
p
o
wer
c
o
n
s
u
m
p
tio
n
i
n
co
m
p
ar
is
o
n
t
o
th
e
p
r
o
p
o
s
e
d
m
o
d
el.
C
h
o
u
an
d
T
r
a
n
[
3
5
]
u
s
ed
DE
-
L
STM
alg
o
r
ith
m
t
o
p
r
ed
ict
elec
tr
icit
y
p
r
ice
th
r
o
u
g
h
ac
cu
r
ac
y
esti
m
atio
n
in
m
ar
k
et
o
f
Ger
m
an
y
,
Fra
n
ce
an
d
Au
s
tr
ia.
I
n
co
m
p
ar
is
o
n
with
th
e
p
r
o
p
o
s
ed
m
o
d
el,
it
ac
h
iev
es
b
etter
f
o
r
ec
asti
n
g
p
er
f
o
r
m
a
n
ce
in
m
o
s
t
ca
s
es.
Ma
s
s
id
d
a
an
d
Ma
r
r
o
cu
[
3
6
]
a
u
th
o
r
s
ad
d
r
ess
ed
th
e
h
o
u
s
eh
o
l
d
en
er
g
y
l
o
ad
f
o
r
ec
asti
n
g
u
s
in
g
h
y
b
r
i
d
m
o
d
els
o
f
R
F
(
lo
n
g
-
ter
m
)
an
d
lin
ea
r
r
e
g
r
ess
io
n
(
L
R
)
f
o
r
s
h
o
r
t
-
ter
m
p
r
e
d
ictio
n
s
.
T
h
e
d
ataset
co
n
tain
ed
m
ea
s
u
r
em
en
ts
tak
en
f
r
o
m
a
h
o
u
s
e
n
ea
r
Par
is
f
r
o
m
2
0
0
6
t
o
2
0
1
0
.
T
ab
le
4
.
C
o
m
p
a
r
ativ
e
an
aly
s
is
M
e
t
h
o
d
F
o
r
e
c
a
st
p
e
r
i
o
d
M
A
P
E
(
%)
P
r
o
p
o
se
d
m
o
d
e
l
M
i
n
1
m
o
n
t
h
3
6
.
3
–
7
6
.
9
H
y
b
r
i
d
(
D
i
f
f
e
r
e
n
t
i
a
l
e
v
o
l
u
t
i
o
n
(
D
E)
+
LST
M
)
[
3
5
]
M
i
n
1
m
o
n
t
h
2
1
.
8
-
3
5
.
2
H
y
b
r
i
d
(
R
F
+
L
i
n
e
a
r
r
e
g
r
e
ss
i
o
n
)
[
3
6
]
M
i
n
1
m
o
n
t
h
1
1
.
4
–
5
1
.
0
B
y
tak
in
g
in
to
co
n
s
id
er
atio
n
p
r
o
p
o
s
ed
in
ter
p
r
etatio
n
o
f
MA
PE
[
3
7
]
as
was
g
iv
en
in
T
ab
le
5
,
it
i
s
p
o
s
s
ib
le
to
s
tate
th
at
p
r
o
p
o
s
ed
m
o
d
el
h
as
r
ea
s
o
n
ab
le
ac
c
u
r
ac
y
in
c
o
m
p
ar
is
o
n
to
m
en
ti
o
n
ed
a
b
o
v
e
r
elate
d
ar
ticles.
Ho
wev
er
,
ex
o
g
en
o
u
s
f
ac
to
r
s
-
s
u
ch
as
m
eteo
r
o
lo
g
ical,
s
o
cial,
an
d
ec
o
n
o
m
ic
f
ac
to
r
s
-
th
at
h
av
e
a
n
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.
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d
i
f
f
ic
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lties
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p
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d
ictin
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m
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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3061
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STM
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ased
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co
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n
e
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ased
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av
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with
in
th
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ticle.
RE
F
E
R
E
NC
E
S
[
1
]
K
.
N
u
r
g
a
l
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y
e
v
,
A
.
To
k
h
me
t
o
v
,
a
n
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Ta
n
c
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k
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c
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As
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:
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.
3
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0
.
[
2
]
S
.
H
.
A
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