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1
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2
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Dif
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[
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[
1
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1
1
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o
b
jects
in
th
e
clu
s
ter
s
[
2
0
]
,
[
2
1
]
.
I
n
th
i
s
m
e
th
o
d
,
th
e
d
ata
f
u
n
ctio
n
is
s
h
o
wn
b
y
r
eg
u
lar
alter
atio
n
b
et
wee
n
ze
r
o
an
d
o
n
e
.
C
o
n
s
eq
u
en
tly
,
in
d
icate
s
th
e
m
eth
o
d
o
n
h
o
w
th
e
o
b
ject
is
ca
teg
o
r
ized
in
to
d
if
f
er
e
n
t
clu
s
ter
s
.
T
h
e
FC
M
alg
o
r
ith
m
m
o
d
el
ca
n
class
if
y
elec
tr
ical
p
o
wer
d
em
a
n
d
s
a
n
d
c
o
m
p
ete
n
t
to
p
r
ed
ict
t
h
e
n
u
m
b
e
r
o
f
n
ew
cu
s
to
m
er
r
eq
u
ests
o
f
ea
ch
r
eg
io
n
wh
ich
is
s
h
o
wn
f
r
o
m
th
e
cu
s
to
m
er
d
ata
in
ea
c
h
v
illag
e
an
d
s
u
b
-
d
is
tr
ict
[
2
2
]
.
T
h
is
will
b
e
u
s
ef
u
l
f
o
r
I
n
d
o
n
esian
Natio
n
al
E
lectr
icity
(
PLN
)
in
m
o
n
ito
r
in
g
t
h
e
s
u
p
p
ly
o
f
elec
tr
ic
p
o
wer
s
o
th
a
t
th
e
d
is
tr
ib
u
tio
n
o
f
elec
tr
ical
en
er
g
y
r
em
ain
s
s
tab
le
an
d
r
eso
u
r
ce
f
u
l.
T
h
r
o
u
g
h
th
is
s
tu
d
y
,
th
e
clu
s
ter
m
eth
o
d
ap
p
lie
d
to
id
en
tify
el
ec
tr
ical
p
o
wer
clu
s
ter
in
g
f
o
r
e
ac
h
r
eg
io
n
in
th
e
wo
r
k
in
g
ar
ea
o
f
PLN
w
h
ich
h
as
v
a
r
iab
le
v
al
u
e
co
n
s
is
tin
g
o
f
jo
b
(
V1
)
,
o
v
er
all
i
n
co
m
e
(
V2
)
,
h
o
u
s
e
ar
ea
(
V3
)
,
n
u
m
b
er
o
f
r
o
o
m
s
(
V4
)
,
n
u
m
b
er
o
f
eq
u
ip
m
en
t
elec
tr
o
n
ic
(
V5
)
an
d
th
e
am
o
u
n
t
o
f
u
s
ag
e
p
o
wer
(
V6
)
,
ea
ch
o
f
wh
ich
ca
n
b
e
g
r
o
u
p
ed
in
to
clu
s
ter
in
g
class
if
icatio
n
to
e
f
f
icien
tly
in
p
r
ed
ictin
g
th
e
p
o
wer
r
eq
u
ir
e
m
en
ts
o
f
eac
h
s
u
b
-
d
is
tr
ict
o
r
r
eg
i
o
n
g
r
o
u
p
in
g
[
2
3
]
.
Fu
r
th
e
r
m
o
r
e
,
th
es
e
v
ar
iab
les
wer
e
in
clu
d
ed
i
n
t
h
e
p
r
o
ce
s
s
o
f
FC
M
class
if
icatio
n
in
d
eter
m
in
i
n
g
t
h
e
am
o
u
n
t
o
f
p
o
wer
in
ea
ch
r
eg
io
n
a
n
d
th
e
s
y
s
tem
t
o
b
e
b
u
ilt
in
p
r
e
d
ictin
g
t
h
e
to
tal
am
o
u
n
t
o
f
p
o
wer
in
s
talled
f
r
o
m
ea
ch
g
r
o
u
p
in
g
r
eg
io
n
.
2.
RE
S
E
ARCH
M
E
T
HOD
T
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
t
h
at
a
p
p
lied
in
th
is
s
tu
d
y
is
f
u
zz
y
lo
g
ic,
th
is
m
eth
o
d
is
ap
p
ly
in
g
m
ap
p
in
g
th
e
s
p
ac
es
o
f
an
a
p
p
r
o
p
r
iate
o
u
tp
u
t
s
p
ac
e
wh
er
ea
s
,
i
n
f
u
zz
y
lo
g
ic
th
er
e
a
r
e
th
r
ee
p
r
o
ce
s
s
es
th
at
p
lay
a
r
o
le,
n
am
ely
:
f
u
zz
if
icatio
n
,
in
f
er
e
n
ce
,
an
d
d
ef
u
zz
i
f
icatio
n
.
f
u
r
t
h
er
m
o
r
e
t
h
e
f
u
zz
y
clu
s
ter
m
ea
n
s
(
FC
M)
is
a
d
ata
g
r
o
u
p
in
g
tech
n
iq
u
e
th
at
is
d
eter
m
in
ed
b
y
th
e
d
eg
r
ee
o
f
m
e
m
b
er
s
h
ip
[
2
4
]
.
T
h
e
c
o
n
ce
p
t
o
f
f
u
zz
y
c
-
m
ea
n
s
is
to
d
eter
m
in
e
t
h
e
ce
n
ter
o
f
t
h
e
clu
s
ter
wh
ich
will b
e
a
s
ig
n
o
f
th
e
av
er
a
g
e
lo
ca
tio
n
f
o
r
ea
ch
clu
s
ter
.
C
lu
s
ter
ce
n
ter
will
m
o
v
e
f
o
r
war
d
t
o
th
e
o
p
tim
al
v
alu
e
b
y
im
p
r
o
v
i
n
g
th
e
cl
u
s
ter
ce
n
ter
an
d
th
e
m
em
b
er
s
h
ip
v
alu
e
o
f
ea
c
h
d
ata
r
ep
ea
ted
ly
[
2
5
]
.
1)
I
n
itialize
=
[
]
m
atr
ix
,
(
0
)
2)
At
k
-
s
tep
: c
alcu
late
th
e
ce
n
ter
s
v
ec
to
r
s
(
)
=
[
]
with
(
k
)
=
∑
.
−
1
∑
−
1
3)
Up
d
ate
(
k
)
(
k
+
1
)
=
1
∑
(
‖
−
‖
‖
−
‖
)
2
−
1
−
1
4)
If
|
|
(
k
+
1
)
−
(
k
)
|
|
<
th
en
STOP; o
th
er
wis
e
r
etu
r
n
to
s
tep
2
.
3.
RE
SU
L
T
S
A
ND
AN
AL
Y
SI
S
T
h
e
d
e
n
s
e
u
r
b
an
ar
ea
h
as
h
ea
d
ed
to
th
e
h
ig
h
d
e
m
an
d
f
o
r
elec
t
r
icity
in
th
e
city
o
f
L
h
o
k
s
eu
m
a
we
wh
ich
in
2
0
2
0
is
ex
p
ec
ted
to
in
cr
ea
s
e
to
1
5
%.
B
ased
o
n
th
e
r
esu
lts
o
f
th
e
p
r
ed
ictio
n
d
ata,
I
n
d
o
n
esian
Natio
n
a
l
E
lectr
icity
C
o
m
p
an
y
(
PT
PLN
)
L
h
o
k
s
eu
m
awe
m
u
s
t
b
e
ab
le
to
r
ev
ea
l
th
e
p
at
ter
n
s
o
f
g
r
o
u
p
in
g
cu
s
to
m
er
f
r
o
m
ea
ch
s
u
b
d
is
tr
ict
in
lh
o
k
s
eu
m
awe
ar
ea
.
I
n
th
is
s
tu
d
y
th
e
V
ar
iab
le
d
ata
th
at
d
eter
m
i
n
es
th
e
d
em
a
n
d
o
f
t
h
e
elec
tr
icity
co
n
s
is
t
o
f
s
ix
v
ar
i
ab
le
cr
iter
ia
o
f
cu
s
to
m
er
:
V
1
=o
cc
u
p
atio
n
,
V2
=o
v
e
r
all
in
co
m
e,
V3
=sp
ac
io
u
s
h
o
u
s
e,
V4
=n
u
m
b
er
o
f
r
o
o
m
s
,
V5
=th
e
am
o
u
n
t
o
f
elec
tr
o
n
ic
eq
u
ip
m
en
t,
V
6
=th
e
am
o
u
n
t
o
f
p
o
wer
co
n
s
u
m
p
tio
n
,
th
e
v
alu
es o
f
th
ese
v
ar
iab
les will
b
e
g
r
o
u
p
e
d
in
to
f
o
u
r
g
r
o
u
p
s
n
am
ely
,
C
1
=Su
b
s
id
y
R
-
1
/
4
5
0
VA,
C
2
=Su
b
s
id
y
R
-
1
/9
0
0
VA,
C
3
=N
o
n
-
Su
b
s
id
y
R
-
1
/9
0
0
,
C
4
=
No
n
-
Su
b
s
id
y
R
-
1
/1
3
0
0
.
T
h
e
in
itial
s
tep
p
r
o
ce
s
s
o
f
th
e
FC
M
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
F
u
z
z
y
clu
s
teri
n
g
mea
n
s
a
lg
o
r
ith
m
a
n
a
lysi
s
fo
r
p
o
w
er d
ema
n
d
p
r
ed
ictio
n
…
(
Mu
h
a
mma
d
S
a
d
li
)
1147
m
eth
o
d
is
to
d
eter
m
in
e
th
e
m
e
d
ian
o
f
th
e
clu
s
ter
(
ce
n
tr
o
i
d
)
a
r
b
itra
r
ily
,
b
ased
o
n
th
e
T
a
b
le
1
m
ed
ian
clu
s
ter
t
h
at
ca
n
b
e
d
eter
m
in
ed
,
a
m
o
n
g
o
th
er
s
: C1
=(
2
)
,
C
2
=(
9
)
,
C
3
=(
4
)
,
C
=(
7
)
.
T
ab
le
1
.
C
u
s
to
m
er
d
ata
N
u
mb
e
r
C
u
s
t
o
mer'
s
n
a
m
e
V1
V2
V3
V4
V5
V6
1
Te
r
p
i
a
d
i
A
.
M
a
j
i
d
5
1
5
5
5
2
2
S
a
r
i
y
u
l
i
s
1
5
2
1
1
4
3
M
u
h
a
r
z
a
5
3
4
3
3
3
4
M
u
h
a
mm
a
d
T
a
i
b
5
1
5
5
5
1
5
B
a
r
maw
i
2
3
3
3
3
2
6
N
a
z
a
m
u
d
d
i
n
2
5
3
3
3
4
7
Zu
l
f
i
k
r
i
2
5
4
1
3
4
8
Ed
i
P
u
t
r
a
2
1
3
3
3
4
9
N
o
r
a
K
u
r
n
i
a
P
u
t
r
i
2
3
3
1
3
3
10
R
a
h
ma
t
S
h
a
l
e
h
3
5
1
1
1
5
T
h
e
f
ir
s
t d
ata
g
a
p
(
A)
with
th
e
f
ir
s
t c
lu
s
ter
ce
n
ter
:
1
=
√
(
1
−
1
1
)
2
+
(
2
−
2
1
)
2
+
(
3
−
3
1
)
2
+
(
4
−
4
1
)
2
+
(
5
−
5
1
)
2
+
(
6
−
6
1
)
2
=
√
(
5
−
2
)
2
+
(
1
−
2
)
2
+
(
5
−
2
)
2
+
(
5
−
2
)
2
+
(
5
−
2
)
2
+
(
2
−
2
)
2
=
6
.
083
Seco
n
d
d
ata
g
ap
(
B
)
with
th
e
f
ir
s
t c
lu
s
ter
ce
n
ter
:
1
=
√
(
1
−
1
1
)
2
+
(
2
−
2
1
)
2
+
(
3
−
3
1
)
2
+
(
4
−
4
1
)
2
+
(
5
−
5
1
)
2
+
(
6
−
6
1
)
2
=
√
(
1
−
2
)
2
+
(
5
−
2
)
2
+
(
2
−
2
)
2
+
(
1
−
2
)
2
+
(
1
−
2
)
2
+
(
4
−
2
)
2
=
4
.
000
T
h
ir
d
d
ata
g
ap
(
C
)
with
th
e
f
ir
s
t c
lu
s
ter
ce
n
ter
:
1
=
√
(
1
−
1
1
)
2
+
(
2
−
2
1
)
2
+
(
3
−
3
1
)
2
+
(
4
−
4
1
)
2
+
(
5
−
5
1
)
2
+
(
6
−
6
1
)
2
=
√
(
5
−
2
)
2
+
(
3
−
2
)
2
+
(
4
−
2
)
2
+
(
3
−
2
)
2
+
(
3
−
2
)
2
+
(
3
−
2
)
2
=
4
.
123
Fo
u
r
th
d
ata
g
ap
(
D)
with
th
e
f
i
r
s
t c
lu
s
ter
ce
n
ter
:
1
=
√
(
1
−
1
1
)
2
+
(
2
−
2
1
)
2
+
(
3
−
3
1
)
2
+
(
4
−
4
1
)
2
+
(
5
−
5
1
)
2
+
(
6
−
6
1
)
2
=
√
(
5
−
2
)
2
+
(
1
−
2
)
2
+
(
5
−
2
)
2
+
(
5
−
2
)
2
+
(
5
−
2
)
2
+
(
1
−
2
)
2
=
6
.
164
T
h
e
tim
e
s
er
ies
d
ata
o
f
p
o
we
r
lo
ad
co
n
s
u
m
p
tio
n
d
ata
p
r
es
en
t
in
th
e
tab
le
ar
e
r
esu
lted
f
r
o
m
ac
tu
al
m
ea
s
u
r
em
en
t
th
at
was
co
n
d
u
c
ted
f
r
o
m
d
if
f
e
r
en
t
o
f
p
o
we
r
lo
ad
with
in
th
e
ar
ea
o
f
L
h
o
k
s
eu
m
awe
,
th
e
r
esu
lt
o
f
clu
s
ter
v
alid
ity
o
b
tain
s
u
s
ed
t
o
d
eter
m
in
e
o
p
tim
u
m
o
f
tar
g
et
in
g
clu
s
ter
.
T
h
e
ca
lcu
latio
n
r
esu
lts
b
etwe
en
th
e
in
itial
d
ata
an
d
th
e
m
e
d
ian
s
h
o
wed
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e
d
ata
r
esu
lt
th
at
ass
o
ci
ate
to
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e
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u
s
ter
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wh
ich
h
as
th
e
least
g
ap
f
r
o
m
th
e
m
ed
ian
o
f
th
e
clu
s
ter
.
Fu
r
th
er
m
o
r
e,
t
h
e
s
tab
le
d
ata
s
h
o
wn
in
T
ab
le
2
T
h
e
m
e
d
ian
clu
s
ter
o
f
e
ac
h
d
atu
m
f
o
r
th
e
av
er
ag
e
o
f
th
e
v
ar
iab
le
v
al
u
es in
clu
d
ed
in
a
clu
s
ter
in
th
e
r
esu
lts
o
f
th
e
FC
M
class
if
icatio
n
.
T
h
e
d
ata
p
r
esen
t
in
T
a
b
le
3
is
th
e
r
an
g
e
d
ata
o
n
s
ec
o
n
d
clu
s
ter
,
th
e
d
ata
o
f
p
o
wer
co
n
s
u
m
p
tio
n
class
if
ied
in
to
d
if
f
er
en
t
clu
s
te
r
,
wh
ich
co
n
s
is
t
o
f
th
r
ee
clu
s
t
er
s
n
am
ely
C
1
,
C
2
an
d
C
3
.
T
h
e
tim
e
s
er
ies
d
ata
wh
ich
class
if
ied
in
to
th
r
ee
clu
s
ter
s
o
f
cu
s
to
m
er
s
was
ca
lcu
lated
to
o
b
tain
th
e
ap
p
r
o
p
r
iate
p
o
wer
co
n
s
u
m
p
tio
n
d
em
an
d
a
m
o
n
g
th
e
c
u
s
to
m
er
i
n
th
e
ar
ea
o
f
L
h
o
s
k
eu
m
awe
.
T
h
e
av
er
a
g
e
d
ata
cl
u
s
ter
in
T
ab
le
4
tab
u
lated
f
r
o
m
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C1
T
ab
le
4
.
Av
e
r
ag
e
v
al
u
es o
f
m
e
d
ian
clu
s
ter
v
ar
iab
le
N
EW
C
LU
S
T
ER
1
C
l
u
st
e
r
V1
V2
V3
V4
V5
V6
C1
3
3
3
.
6
6
6
6
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6
6
6
7
3
3
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6
6
7
2
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6
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6
7
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3
2
.
6
3
.
2
8
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1
4
2
8
6
3
3
3
C3
2
.
5
5
2
.
5
1
2
4
.
5
3
.
1
.
Clus
t
er
ing
v
a
lid
it
y
da
t
a
o
f
clus
t
er
T
h
e
o
b
jectiv
e
o
f
clu
s
ter
in
g
th
e
d
ata
o
f
cu
s
to
m
er
is
to
d
eter
m
in
e
th
e
o
r
d
in
ar
y
g
r
o
u
p
o
f
t
h
e
d
at
a
clu
s
ter
s
;
th
e
alg
o
r
it
h
m
was
ex
ec
u
ted
s
ev
er
al
tim
es.
B
ased
o
n
th
e
n
u
m
b
er
o
f
clu
s
ter
s
ass
o
ciate
d
with
th
e
tar
g
etin
g
v
ar
iab
les,
th
e
m
ax
im
u
m
n
u
m
b
er
o
f
cl
u
s
ter
s
,
T
h
er
ef
o
r
e
th
e
iter
atio
n
p
r
o
ce
s
s
was
r
ep
ea
ted
f
o
r
f
iv
e
tim
es.
T
h
e
cu
s
to
m
e
r
d
ata
th
at
co
n
s
is
t
o
f
,
t
h
e
cu
s
to
m
e
r
'
s
n
am
e,
th
e
n
am
e
o
f
th
e
v
illag
e,
s
u
b
d
is
tr
ict
o
f
d
a
ta
ce
n
ter
an
d
clu
s
ter
ce
n
tr
o
id
,
th
e
f
o
llo
win
g
v
iews
ar
e
as
f
o
llo
ws:
an
d
clu
s
ter
c
o
n
s
is
tin
g
o
f
C
1
=Su
b
s
id
y
R
-
1
/4
5
0
VA,
C
2
=Su
b
s
id
y
R
-
1
/9
0
0
VA,
C
3
=N
o
n
-
Su
b
s
id
y
R
-
1
/9
0
0
,
C
4
=N
o
n
Su
b
s
id
y
R
-
1
/1
3
0
0
,
C
5
=N
o
n
Su
b
s
id
y
R
-
1
/2
2
0
0
VA.
T
h
e
clu
s
ter
in
g
v
alid
ity
d
ata
o
f
p
o
wer
lo
ad
c
o
n
s
u
m
p
tio
n
d
ata
p
r
esen
ted
in
T
ab
le
5
was
v
alid
atin
g
f
r
o
m
d
ata
s
er
ies
o
f
p
o
wer
lo
ad
wi
th
in
th
e
ar
ea
o
f
L
h
o
k
s
eu
m
a
we,
th
e
r
esu
lt
o
f
clu
s
ter
v
ali
d
ity
o
b
tain
s
u
s
ed
to
d
eter
m
in
e
o
p
tim
u
m
o
f
tar
g
eti
n
g
clu
s
ter
.
T
h
e
p
o
wer
lo
ad
d
em
an
d
o
f
cu
s
to
m
er
v
ar
ies
b
r
o
ad
ly
.
T
h
e
clu
s
ter
v
alid
ity
an
d
clu
s
ter
in
g
p
h
ase
em
p
lo
y
ed
to
o
b
tain
a
p
r
o
p
er
n
u
m
b
er
o
f
cu
s
to
m
er
clu
s
ter
,
w
h
ich
is
class
if
ied
in
tar
g
etin
g
th
e
v
alid
ated
p
r
o
p
o
s
e
o
f
p
r
ed
ictio
n
o
f
f
u
tu
r
e
co
n
s
u
m
p
tio
n
lo
a
d
o
f
e
v
er
y
g
r
o
u
p
o
f
cu
s
t
o
m
er
s
.
3
.
2
.
De
m
a
nd
elec
t
ric
po
wer
re
qu
irem
ent
s
f
o
r
ea
ch
re
g
io
na
l c
lus
t
er
T
h
e
d
ata
p
r
esen
t
in
Fig
u
r
e
1
s
h
o
wn
g
r
ap
h
o
f
clu
s
ter
o
f
cu
s
to
m
er
d
em
a
n
d
o
f
elec
tr
ic
p
o
w
er
f
o
r
ea
c
h
cu
s
to
m
er
g
r
o
u
p
.
T
h
e
d
ata
o
f
th
e
elec
tr
ical
p
o
wer
r
eq
u
i
r
em
en
t
s
f
o
r
ea
ch
s
u
b
-
d
is
tr
ict
an
d
th
e
c
en
ter
o
f
th
e
cl
u
s
ter
,
f
o
r
h
o
u
s
eh
o
ld
c
u
s
to
m
er
t
h
e
m
ea
s
u
r
ed
d
ata
we
r
e
9
,
5
8
8
,
4
4
6
a
n
d
th
e
p
r
ed
ictio
n
1
0
,
037
,
2
4
8
,
b
u
s
in
ess
cu
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I
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19
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1150
4.
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RE
F
E
R
E
NC
E
S
[1
]
F
.
Li
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n
d
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.
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,
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se
d
o
n
KN
N,”
In
t.
J
.
Am
b
ie
n
t
En
e
rg
y
,
Oc
t.
2
0
1
9
,
d
o
i:
1
0
.
1
0
8
0
/
0
1
4
3
0
7
5
0
.
2
0
1
9
.
1
6
8
2
0
4
1
.
[2
]
L.
Aliah
m
a
d
i
p
o
u
r,
V.
T
o
rra
,
E
.
E
sla
m
i,
“
On
h
e
sitan
t
f
u
z
z
y
c
lu
ste
ri
n
g
a
n
d
c
lu
ste
ri
n
g
o
f
h
e
sitan
t
f
u
z
z
y
d
a
ta,”
S
tu
d
ies
in
Co
m
p
u
t
a
ti
o
n
a
l
I
n
telli
g
e
n
c
e
,
Ja
n
.
2
0
1
7
,
d
o
i:
1
0
.
1
0
0
7
/
9
7
8
-
3
-
3
1
9
-
4
7
5
5
7
-
8
_
1
0
.
[3
]
E.
A.
F
e
i
n
b
e
rg
a
n
d
D.
G
e
n
e
th
li
o
u
,
“
Lo
a
d
f
o
re
c
a
stin
g
,
”
Ap
p
li
e
d
M
a
th
e
ma
ti
c
s
f
o
r
Res
tru
c
t
u
re
d
El
e
c
tric
Po
we
r
S
y
ste
ms
,
p
p
.
2
6
9
-
2
8
5
,
2
0
0
6
.
[4
]
V.
Bh
a
ti
a
,
a
n
d
R.
Ra
n
i,
“
A
p
a
ra
ll
e
l
fu
z
z
y
c
l
u
ste
rin
g
a
lg
o
rit
h
m
fo
r
larg
e
g
ra
p
h
s
u
sin
g
P
re
g
e
l,
”
Exp
e
rt
S
y
ste
ms
wit
h
Ap
p
li
c
a
ti
o
n
s,
v
o
l
.
7
8
,
p
p
.
1
3
5
-
1
4
4
,
Ju
l
.
2
0
1
7
,
d
o
i:
1
0
.
1
0
1
6
/
j.
e
sw
a
.
2
0
1
7
.
0
2
.
0
0
5
.
[5
]
N.
Jo
h
a
n
n
e
se
n
,
M
.
Ko
lh
e
,
a
n
d
M
.
G
o
o
d
win
,
“
Re
lativ
e
e
v
a
lu
a
ti
o
n
o
f
re
g
re
ss
io
n
t
o
o
ls
fo
r
u
rb
a
n
a
re
a
e
lec
tri
c
a
l
e
n
e
rg
y
d
e
m
a
n
d
fo
re
c
a
stin
g
,
”
J
o
u
rn
a
l
o
f
Clea
n
e
r
Pro
d
u
c
ti
o
n
,
v
o
l.
2
1
8
,
p
p
.
5
5
5
-
5
6
4
,
M
a
y
2
0
1
9
,
d
o
i:
1
0
.
1
0
1
6
/j
.
jcle
p
ro
.
2
0
1
9
.
0
1
.
1
0
8
.
[6
]
E.
Alm
e
sh
a
iei,
a
n
d
H.
S
o
l
tan
,
“
A
m
e
th
o
d
o
lo
g
y
fo
r
e
lec
tri
c
p
o
we
r
l
o
a
d
fo
re
c
a
stin
g
,
”
Al
e
x
a
n
d
ria
E
n
g
i
n
e
e
rin
g
J
o
u
r
n
a
l
,
v
o
l.
5
0
,
n
o
.
2
,
p
p
.
1
3
7
-
1
4
4
,
d
o
i:
1
0
.
1
0
1
6
/j
.
a
e
j.
2
0
1
1
.
0
1
.
0
1
5
.
[7
]
N.
Jo
h
a
n
n
e
se
n
,
M
.
Ko
lh
e
,
a
n
d
M
.
G
o
o
d
win
,
“
S
m
a
rt
lo
a
d
p
re
d
ictio
n
a
n
a
ly
sis
fo
r
m
icro
-
g
ri
d
wit
h
Ho
li
d
a
y
Ca
b
in
s
i
n
No
rwe
g
ian
r
u
ra
l
a
re
a
1
,
”
J
o
u
rn
a
l
o
f
Clea
n
e
r
Pro
d
u
c
t
io
n
,
v
o
l.
2
6
6
,
p
p
.
1
2
1
4
2
3
,
Ap
r.
2
0
2
0
,
d
o
i:
1
0
.
1
0
1
6
/j
.
jcle
p
ro
.
2
0
2
0
.
1
2
1
4
2
3
.
[8
]
K.
S
p
e
rli
n
g
,
a
n
d
B
.
M
o
ll
e
r,
“
En
d
-
u
se
e
n
e
r
g
y
sa
v
in
g
s
a
n
d
d
istri
c
t
h
e
a
ti
n
g
e
x
p
a
n
si
o
n
in
a
l
o
c
a
l
re
n
e
wa
b
le
e
n
e
rg
y
sy
ste
m
–
A
sh
o
rt
-
term
p
e
rsp
e
c
ti
v
e
,
”
Ap
p
li
e
d
En
e
rg
y
,
v
o
l.
9
2
,
p
p
.
8
3
1
-
8
4
2
,
Ap
r.
2
0
1
2
,
d
o
i:
1
0
.
1
0
1
6
/j
.
a
p
e
n
e
rg
y
.
2
0
1
1
.
0
8
.
0
4
0
.
[9
]
Q.
Wan
g
,
S
.
Li
u
,
a
n
d
H.
Ya
n
,
“
T
h
e
a
p
p
li
c
a
ti
o
n
o
f
tri
g
o
n
o
m
e
tri
c
g
r
e
y
p
re
d
ictio
n
m
o
d
e
l
t
o
a
v
e
ra
g
e
p
e
r
c
a
p
it
a
n
a
t
u
ra
l
g
a
s
c
o
n
s
u
m
p
ti
o
n
o
f
h
o
u
se
h
o
ld
s
in
Ch
i
n
a
,
”
Gr
e
y
S
y
st.
T
h
e
o
ry
Ap
p
l.
,
v
o
l.
9
,
n
o
.
1
,
p
p
.
1
9
–
3
0
,
F
e
b
.
2
0
1
9
,
d
o
i:
1
0
.
1
1
0
8
/g
s
-
08
-
2
0
1
8
-
0
0
3
3
.
[1
0
]
B.
S
a
m
a
n
ta,
K.
R.
Al
-
Ba
lu
sh
i
,
a
n
d
S
.
A.
Al
-
Ara
imi
,
“
Artifi
c
ial
n
e
u
ra
l
n
e
two
r
k
s
a
n
d
su
p
p
o
rt
v
e
c
to
r
m
a
c
h
in
e
s
with
g
e
n
e
ti
c
a
lg
o
rit
h
m
f
o
r
b
e
a
rin
g
fa
u
lt
d
e
tec
ti
o
n
,
”
En
g
i
n
e
e
rin
g
Ap
p
li
c
a
ti
o
n
s
o
f
Arti
fi
c
i
a
l
I
n
telli
g
e
n
c
e
,
v
o
l.
1
6
,
n
o
.
7
-
8
,
p
p
.
6
5
7
-
6
6
5
,
Oc
t.
2
0
0
3
,
d
o
i:
1
0
.
1
0
1
6
/j
.
e
n
g
a
p
p
a
i
.
2
0
0
3
.
0
9
.
0
0
6
.
[1
1
]
P
.
M
u
k
h
o
p
a
d
h
y
a
y
,
G
.
M
it
ra
,
S
.
Ba
n
e
rjee
,
a
n
d
G
.
M
u
k
h
e
rjee
,
“
El
e
c
tri
c
it
y
l
o
a
d
f
o
re
c
a
stin
g
u
si
n
g
f
u
z
z
y
lo
g
ic:
S
h
o
rt
term
lo
a
d
fo
re
c
a
stin
g
fa
c
to
rin
g
we
a
th
e
r
p
a
ra
m
e
ter,”
2
0
1
7
7
th
I
n
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Po
we
r
S
y
ste
ms
(ICPS
),
De
c
.
2
0
1
7
,
d
o
i
:
1
0
.
1
1
0
9
/ICP
ES
.
2
0
1
7
.
8
3
8
7
4
0
1
.
[1
2
]
A
.
Lao
u
a
fi,
M
.
M
o
r
d
jao
u
i,
a
n
d
T
.
E.
Bo
u
k
e
li
a
,
“
An
a
d
a
p
ti
v
e
n
e
u
ro
-
fu
z
z
y
i
n
fe
re
n
c
e
sy
ste
m
-
b
a
se
d
a
p
p
ro
a
c
h
f
o
r
d
a
il
y
lo
a
d
c
u
r
v
e
p
re
d
ictio
n
,
”
J
.
E
n
e
rg
y
S
y
st.
,
v
o
l.
2
,
n
o
.
3
,
p
p
.
1
1
5
-
1
2
6
,
2
0
1
8
,
d
o
i:
1
0
.
3
0
5
2
1
/
jes
.
4
3
4
2
2
4
.
[1
3
]
T.
Va
n
tu
c
h
,
a
n
d
M
ich
a
l,
“
An
e
n
s
e
m
b
le o
f
m
u
l
ti
-
o
b
jec
ti
v
e
o
p
ti
m
iz
e
d
fu
z
z
y
re
g
re
ss
io
n
m
o
d
e
ls f
o
r
sh
o
rt
-
term
e
lec
tri
c
lo
a
d
fo
re
c
a
stin
g
,
”
2
0
1
7
IEE
E
S
y
mp
o
si
u
m
S
e
rie
s
o
n
Co
mp
u
ta
ti
o
n
a
l
I
n
telli
g
e
n
c
e
(S
S
C
I)
,
De
c
.
2
0
1
7
,
d
o
i:
1
0
.
1
1
0
9
/S
S
CI.
2
0
1
7
.
8
2
8
5
3
4
8
.
[1
4
]
E.
Ak
a
rsla
n
,
a
n
d
F
.
O.
Ho
c
a
o
g
l
u
,
“
A
n
o
v
e
l
sh
o
rt
-
term
l
o
a
d
f
o
r
e
c
a
stin
g
a
p
p
r
o
a
c
h
u
sin
g
A
d
a
p
ti
v
e
Ne
u
ro
-
F
u
z
z
y
In
fe
re
n
c
e
S
y
ste
m
,
”
2
0
1
8
6
th
In
ter
n
a
ti
o
n
a
l
Ista
n
b
u
l
S
ma
rt
Gr
id
s
a
n
d
Cit
ies
C
o
n
g
re
ss
a
n
d
F
a
ir
(IC
S
G)
,
A
p
r.
2
0
1
8
,
d
o
i:
1
0
.
1
1
0
9
/S
G
CF
.
2
0
1
8
.
8
4
0
8
9
6
4
.
[1
5
]
M
.
Ya
n
g
,
a
n
d
Y.
Na
talian
i,
“
A
fe
a
tu
re
-
re
d
u
c
ti
o
n
fu
z
z
y
c
l
u
ste
rin
g
a
lg
o
rit
h
m
b
a
se
d
o
n
fe
a
tu
re
-
we
ig
h
ted
e
n
tro
p
y
,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
Fu
zz
y
S
y
st
e
ms
,
v
o
l.
2
6
,
n
o
.
2
,
p
p
.
8
1
7
-
8
3
5
,
Ap
r.
2
0
1
8
,
d
o
i
:
1
0
.
1
1
0
9
/T
F
UZZ.
2
0
1
7
.
2
6
9
2
2
0
3
.
[1
6
]
J.
He
rm
ias
,
K.
Tek
n
o
m
o
,
a
n
d
J.
Claro
N.
M
o
n
je,
“
S
h
o
rt
-
term
sto
c
h
a
stic
lo
a
d
f
o
re
c
a
stin
g
u
sin
g
a
u
to
re
g
re
ss
iv
e
in
teg
ra
ted
m
o
v
i
n
g
a
v
e
ra
g
e
m
o
d
e
l
s
a
n
d
Hi
d
d
e
n
M
a
r
k
o
v
M
o
d
e
l
,
”
2
0
1
7
In
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
I
n
fo
rm
a
t
io
n
a
n
d
Co
mm
u
n
ica
ti
o
n
T
e
c
h
n
o
l
o
g
ies
(ICICT
),
De
c
.
2
0
1
7
,
d
o
i:
1
0
.
1
1
0
9
/IC
ICT.
2
0
1
7
.
8
3
2
0
1
7
7
.
[1
7
]
A.
G
o
sa
in
,
a
n
d
S
o
n
i
k
a
Da
h
i
y
a
,
“
P
e
rfo
rm
a
n
c
e
a
n
a
ly
sis
o
f
v
a
ri
o
u
s
f
u
z
z
y
c
lu
ste
ri
n
g
a
l
g
o
r
it
h
m
s:
a
re
v
i
e
w,”
Pro
c
e
d
ia
Co
mp
u
ter
S
c
ien
c
e
,
v
o
l.
7
9
,
p
p
.
1
0
0
-
1
1
1
,
2
0
1
6
,
d
o
i:
1
0
.
1
0
1
6
/j
.
p
ro
c
s.
2
0
1
6
.
0
3
.
0
1
4
.
[1
8
]
L.
Aliah
m
a
d
i
p
o
u
r,
V
.
T
o
rra
,
a
n
d
E.
Eslam
i,
“
On
h
e
sitan
t
f
u
z
z
y
c
lu
st
e
rin
g
a
n
d
c
lu
ste
ri
n
g
o
f
h
e
sita
n
t
f
u
z
z
y
d
a
ta,”
S
tu
d
ies
in
C
o
mp
u
ta
ti
o
n
a
l
In
tell
ig
e
n
c
e
,
v
o
l
.
6
7
1
,
p
p
.
1
5
7
-
1
6
8
,
Ja
n
.
2
0
1
7
,
d
o
i:
1
0
.
1
0
0
7
/
9
7
8
-
3
-
3
1
9
-
4
7
5
5
7
-
8
_
1
0
.
[1
9
]
M
.
Ye
silb
u
d
a
k
,
S
.
S
a
g
ir
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