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r
m
ac
h
in
e
(
SVM)
,
XGBo
o
s
t,
en
s
em
b
le
m
eth
o
d
s
)
h
av
e
ap
p
r
o
x
im
ately
eq
u
al
m
etr
ics
(
R
²,
MA
E
,
R
MSE
,
s
y
m
m
etr
ic
m
ea
n
ab
s
o
lu
te
p
er
ce
n
t
ag
e
er
r
o
r
(
S
MA
PE)
)
.
T
o
f
o
r
m
a
n
en
s
em
b
le
m
o
d
el
f
r
o
m
th
e
p
r
e
v
io
u
s
m
o
d
els,
wh
ich
ca
n
co
m
b
in
e
th
e
p
er
f
o
r
m
a
n
ce
s
o
f
th
e
f
o
r
m
in
g
m
o
d
els
to
g
et
an
o
p
tim
al
an
d
p
er
f
o
r
m
a
n
t
m
o
d
el,
th
e
au
th
o
r
s
s
elec
ted
th
e
th
r
ee
b
est m
o
d
els,
wh
ich
ar
e
r
an
d
o
m
f
o
r
est (
R
F)
,
SVM,
an
d
XGBo
o
s
t,
to
f
o
r
m
en
s
em
b
le
m
o
d
els.
B
u
t
u
n
f
o
r
t
u
n
ately
,
t
h
e
en
s
em
b
le
m
o
d
els
d
i
d
n
o
t
im
p
r
o
v
e
u
p
o
n
th
e
b
est
p
er
f
o
r
m
in
g
R
F
m
o
d
el
b
u
t
r
at
h
er
ac
h
iev
ed
s
im
ilar
r
esu
lts
o
n
t
r
a
in
in
g
.
T
h
e
b
est
r
esu
lts
wer
e
o
b
tain
ed
u
s
in
g
th
e
s
tack
in
g
en
s
em
b
le
m
o
d
el.
W
ith
an
R
²
o
f
0
.
7
,
th
e
s
tack
in
g
en
s
em
b
le
m
o
d
el
o
u
tp
e
r
f
o
r
m
s
o
th
er
m
o
d
els,
with
an
R
MSE
o
f
5
.
5
k
W
h
,
MA
E
o
f
3
.
3
8
k
W
h
,
an
d
a
SMAPE
o
f
1
1
.
6
%.
E
n
er
g
y
co
n
s
u
m
p
tio
n
p
r
ed
ictio
n
b
y
t
h
e
v
o
tin
g
en
s
em
b
le
g
iv
es
r
esu
lts
alm
o
s
t
as
im
p
o
r
tan
t
as
th
e
s
tack
in
g
en
s
em
b
le,
an
d
th
e
r
esu
l
tin
g
s
co
r
es
ar
e
ap
p
r
o
x
im
ately
s
im
ilar
,
with
an
R
²
o
f
0
.
6
9
,
R
MSE
o
f
5
.
5
4
k
W
h
,
MA
E
o
f
3
.
4
1
k
W
h
,
an
d
a
S
MA
PE
o
f
1
1
.
8
%.
I
n
an
o
t
h
er
s
tu
d
y
f
o
cu
s
in
g
o
n
Mo
r
o
cc
o
,
th
e
r
esear
ch
er
s
in
[
1
3
]
a
p
p
lied
a
d
ee
p
le
ar
n
in
g
ap
p
r
o
ac
h
to
p
r
ed
ict
th
e
d
u
r
atio
n
o
f
ch
ar
g
in
g
s
ess
io
n
s
.
T
h
ey
u
tili
ze
d
alg
o
r
ith
m
s
s
u
ch
as
r
ec
u
r
r
en
t
n
e
u
r
al
n
etwo
r
k
(
R
NN)
,
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
L
STM
)
,
an
d
g
ate
d
r
ec
u
r
r
e
n
t
u
n
it
(
GR
U)
,
an
d
ass
ess
ed
th
e
ir
ef
f
ec
tiv
en
ess
u
s
in
g
m
etr
ics
lik
e
m
ea
n
s
q
u
a
r
ed
e
r
r
o
r
(
MSE
)
,
R
MSE
,
an
d
MA
E
.
T
h
e
p
ap
e
r
s
[
1
4
]
–
[
1
6
]
r
ev
ea
ls
th
at
lo
wer
s
co
r
es in
th
ese
m
etr
ics in
d
icate
m
o
r
e
ac
cu
r
ate
p
r
ed
ictio
n
s
,
s
u
g
g
esti
n
g
th
at
th
e
p
r
e
d
icted
d
ata
cl
o
s
ely
ap
p
r
o
x
im
ates
th
e
ac
tu
al
v
alu
es.
T
h
e
r
esu
lts
in
d
icate
d
th
at
th
e
R
NN
alg
o
r
ith
m
y
ield
ed
h
ig
h
er
v
alu
es
o
f
MSE
,
R
MSE
,
an
d
MA
E
,
s
u
g
g
esti
n
g
a
s
ig
n
if
ican
t
d
is
cr
ep
an
cy
b
etwe
en
t
h
e
p
r
e
d
icted
an
d
ac
tu
al
ch
a
r
g
in
g
s
e
s
s
io
n
d
u
r
atio
n
s
,
th
er
eb
y
m
ak
i
n
g
it
less
ef
f
ec
tiv
e
co
m
p
ar
ed
t
o
th
e
o
th
er
al
g
o
r
it
h
m
s
.
Fo
r
th
e
L
STM
alg
o
r
ith
m
,
an
MSE
o
f
1
.
5
2
3
%,
wh
ic
h
is
o
n
ly
2
0
%
o
f
th
e
MSE
f
o
r
R
NN,
in
d
icate
s
th
at
its
p
r
ed
ictio
n
s
o
f
s
es
s
io
n
d
u
r
atio
n
ar
e
m
u
c
h
clo
s
er
to
th
e
ac
tu
al
ch
ar
g
in
g
d
u
r
atio
n
s
.
Ov
e
r
all,
th
e
r
esu
lts
d
em
o
n
s
tr
ate
th
at
t
h
e
g
ated
r
ec
u
r
r
en
t
u
n
it
(
GR
U)
m
o
d
el
s
u
r
p
ass
es
b
o
th
R
NN
an
d
L
STM
in
p
e
r
f
o
r
m
an
ce
.
I
n
th
is
s
tu
d
y
,
we
in
itially
i
n
tr
o
d
u
ce
d
th
e
a
r
tific
ial
in
tellig
en
ce
alg
o
r
ith
m
s
u
tili
ze
d
f
o
r
p
r
e
d
ictin
g
E
V
en
er
g
y
co
n
s
u
m
p
tio
n
.
W
e
p
r
o
v
id
ed
a
co
n
cise
d
ef
in
itio
n
alo
n
g
with
s
u
p
p
lem
e
n
tar
y
i
n
f
o
r
m
atio
n
a
b
o
u
t
h
o
w
th
ese
alg
o
r
ith
m
s
f
u
n
ctio
n
(
KN
N
,
XGBo
o
s
t,
r
an
d
o
m
f
o
r
est
r
eg
r
ess
o
r
,
an
d
r
id
g
e
r
e
g
r
ess
o
r
)
.
Fo
llo
win
g
th
at,
we
d
is
cu
s
s
ed
th
e
m
etr
ics
em
p
lo
y
ed
to
ev
alu
ate
th
e
p
er
f
o
r
m
an
ce
o
f
th
ese
alg
o
r
ith
m
s
,
wh
ich
ar
e
MA
E
,
MSE
,
m
ea
n
ab
s
o
l
u
te
p
e
r
ce
n
tag
e
er
r
o
r
(
MA
PE)
,
an
d
ex
ec
u
tio
n
tim
e.
L
astl
y
,
we
p
r
esen
ted
th
e
o
u
t
co
m
es
u
s
in
g
v
is
u
al
r
ep
r
esen
tatio
n
s
.
T
o
ac
c
o
m
p
lis
h
th
is
,
r
esear
ch
er
s
ar
e
ad
v
is
e
d
to
b
eg
in
b
y
r
e
v
iewin
g
ex
is
tin
g
p
ap
er
s
tr
ea
tin
g
alg
o
r
ith
m
s
em
p
l
o
y
ed
f
o
r
E
V
ch
ar
g
in
g
p
r
e
d
ictio
n
a
n
d
i
d
en
ti
f
y
in
g
an
y
g
ap
s
in
cu
r
r
en
t
k
n
o
wled
g
e.
T
h
e
aim
o
f
th
is
wo
r
k
is
to
m
ak
e
a
co
m
p
ar
is
o
n
b
etwe
en
AI
p
r
ed
ictio
n
alg
o
r
ith
m
s
with
th
e
co
n
ce
p
t
o
f
p
r
ed
ictin
g
f
u
tu
r
e
en
er
g
y
c
o
n
s
u
m
p
ti
o
n
b
y
g
iv
i
n
g
th
e
alg
o
r
ith
m
a
lis
t o
f
f
u
t
u
r
e
d
ates.
T
h
is
p
ap
er
is
o
r
g
an
ized
in
to
s
ev
er
al
s
ec
tio
n
s
.
Sectio
n
1
s
er
v
es
as
th
e
in
tr
o
d
u
ctio
n
,
s
ettin
g
th
e
s
ta
g
e
f
o
r
th
e
r
esear
ch
p
r
esen
te
d
.
Se
ctio
n
2
d
etails
th
e
m
eth
o
d
o
lo
g
y
,
em
p
h
asizin
g
d
ata
co
n
ten
t,
c
lean
in
g
o
p
er
atio
n
s
,
an
d
th
e
cr
iter
ia
f
o
r
s
elec
tin
g
d
ata
f
o
r
b
o
t
h
tr
ain
in
g
a
n
d
test
in
g
.
T
h
is
s
ec
tio
n
also
in
tr
o
d
u
c
es
th
e
AI
alg
o
r
ith
m
u
tili
ze
d
in
th
e
s
tu
d
y
.
Sectio
n
3
p
r
esen
ts
th
e
r
esu
lts
d
er
iv
e
d
f
r
o
m
th
e
alg
o
r
ith
m
,
s
h
o
wca
s
in
g
n
u
m
er
ical
d
ata
an
d
f
ig
u
r
es
th
at
illu
s
tr
ate
th
es
e
f
in
d
in
g
s
.
T
h
e
p
ap
er
c
o
n
clu
d
es
in
s
ec
tio
n
4
with
a
s
u
m
m
ar
y
o
f
th
e
r
esear
c
h
an
d
o
f
f
er
s
p
er
s
p
ec
tiv
es o
n
f
u
t
u
r
e
wo
r
k
.
2.
M
E
T
H
O
D
T
h
e
d
ataset
u
s
ed
to
tr
ain
th
e
m
o
d
els
f
o
r
p
r
ed
ictin
g
E
V
ch
ar
g
in
g
en
e
r
g
y
r
e
q
u
ir
em
e
n
ts
co
n
tain
s
in
f
o
r
m
atio
n
ab
o
u
t
th
e
ch
a
r
g
in
g
p
atter
n
s
o
f
elec
tr
ic
v
eh
icles.
I
t
is
im
p
o
r
tan
t
to
n
o
te
th
at
th
is
d
ata
is
r
elate
d
to
an
en
er
g
y
d
is
tr
ib
u
tio
n
n
etwo
r
k
,
in
d
icatin
g
th
at
it
is
d
er
i
v
ed
f
r
o
m
a
g
r
o
u
p
o
f
E
V
ch
ar
g
i
n
g
s
tatio
n
s
th
at
r
ec
eiv
e
elec
tr
ical
p
o
wer
f
r
o
m
th
e
s
a
m
e
s
u
p
p
lier
.
T
h
e
d
ataset
is
a
5
-
y
ea
r
e
n
er
g
y
co
n
s
u
m
p
tio
n
r
ec
o
r
d
o
f
an
elec
tr
ic
v
eh
icle
ch
ar
g
er
in
R
ab
at
city
in
Mo
r
o
cc
o
.
T
h
e
lo
a
d
cu
r
v
e
i
n
Fig
u
r
e
1
illu
s
tr
ates
th
e
co
n
s
u
m
ed
en
er
g
y
o
v
e
r
5
y
ea
r
s
in
k
W
h
.
I
n
Fig
u
r
e
2
,
we
h
av
e
d
ep
icted
th
e
tr
ain
in
g
d
ataset
an
d
th
e
tes
t
d
ataset
u
s
in
g
a
s
in
g
le
cu
r
v
e.
T
h
is
s
elec
tio
n
o
f
tr
ain
in
g
an
d
test
in
g
d
atasets
was
m
ad
e
af
ter
co
n
d
u
ctin
g
a
n
an
al
y
s
is
o
f
m
u
ltip
le
iter
atio
n
s
to
d
eter
m
in
e
th
e
o
p
tim
al
p
er
f
o
r
m
an
ce
.
T
h
is
v
is
u
aliza
tio
n
tec
h
n
iq
u
e
allo
ws
u
s
to
ass
ess
th
e
ef
f
ec
tiv
en
ess
o
f
th
e
s
elec
ted
d
atasets
an
d
m
ak
e
in
f
o
r
m
e
d
d
ec
is
io
n
s
r
eg
ar
d
in
g
th
eir
u
s
ag
e
in
th
e
m
o
d
el
tr
ain
in
g
an
d
test
in
g
p
r
o
ce
s
s
es.
T
h
e
m
eth
o
d
o
l
o
g
y
in
itially
c
o
n
ce
r
n
s
th
e
d
ata
co
llectio
n
to
f
o
r
m
t
h
e
f
o
u
n
d
atio
n
o
f
th
e
an
a
ly
s
is
.
T
h
is
co
llected
in
f
o
r
m
atio
n
th
en
n
ee
d
s
p
r
o
ce
s
s
in
g
,
wh
er
e
it
is
clea
n
ed
an
d
p
r
ep
ar
ed
f
o
r
ex
am
i
n
a
tio
n
.
T
h
e
n
ex
t
s
tep
in
v
o
lv
es
ex
p
lo
r
ato
r
y
d
ata
an
aly
s
is
,
d
u
r
in
g
wh
ich
th
e
p
r
e
p
ar
ed
d
ata
is
th
o
r
o
u
g
h
ly
ex
am
in
ed
to
u
n
c
o
v
e
r
p
atter
n
s
,
tr
en
d
s
,
an
d
v
alu
a
b
le
in
s
ig
h
ts
.
T
h
ese
in
itial
s
tag
es
ar
e
cr
u
cial
as
th
ey
lay
th
e
g
r
o
u
n
d
wo
r
k
f
o
r
th
e
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
.
4
,
Au
g
u
s
t
20
25
:
4
1
9
2
-
4201
4194
s
u
b
s
eq
u
en
t
m
o
d
elin
g
an
d
f
o
r
ec
asti
n
g
ac
tiv
ities
,
en
s
u
r
in
g
a
r
o
b
u
s
t
an
d
ac
cu
r
ate
p
r
ed
ictio
n
o
f
en
e
r
g
y
co
n
s
u
m
p
tio
n
an
d
r
ev
en
u
e
f
o
r
e
ca
s
ts
[
1
7
]
.
Fig
u
r
e
1
.
R
eq
u
ested
e
n
er
g
y
f
o
r
5
y
ea
r
s
f
r
o
m
t
h
e
d
ataset
Fig
u
r
e
2
.
T
r
ain
in
g
a
n
d
test
in
g
d
ataset
T
h
e
d
ata
u
s
ed
f
o
r
th
is
p
u
r
p
o
s
e
was
s
tr
ateg
ically
d
iv
i
d
ed
,
with
8
0
%
allo
ca
ted
f
o
r
tr
ain
in
g
t
h
e
m
o
d
els
an
d
th
e
r
em
ain
i
n
g
2
0
% u
s
ed
f
o
r
test
in
g
th
eir
ac
cu
r
ac
y
.
T
h
is
s
p
ec
if
ic
s
p
lit wa
s
f
in
alize
d
af
t
er
s
ev
er
al
iter
atio
n
s
to
d
eter
m
in
e
th
e
m
o
s
t
ef
f
ec
tiv
e
d
is
tr
ib
u
tio
n
f
o
r
o
p
tim
al
p
r
ed
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e
p
er
f
o
r
m
an
ce
.
T
h
e
ch
o
s
en
r
atio
o
f
8
0
%
tr
ain
in
g
to
2
0
%
test
in
g
co
n
s
is
ten
tly
p
r
o
d
u
ce
d
th
e
b
est
o
u
tco
m
es,
s
tr
ik
in
g
a
b
alan
ce
b
etwe
en
lear
n
in
g
co
m
p
lex
ity
a
n
d
v
alid
atio
n
ef
f
e
ctiv
en
ess
.
2
.
1
.
Ra
nd
o
m
f
o
re
s
t
re
g
re
s
s
o
r
R
an
d
o
m
f
o
r
est
r
e
g
r
ess
o
r
is
a
p
o
p
u
lar
en
s
em
b
le
m
ac
h
in
e
l
ea
r
n
in
g
alg
o
r
ith
m
c
o
m
m
o
n
ly
u
s
ed
f
o
r
task
s
in
clu
d
in
g
class
if
icatio
n
an
d
r
e
g
r
ess
io
n
.
I
t
w
o
r
k
s
b
y
c
r
ea
tin
g
m
u
ltip
le
d
ec
is
io
n
tr
ee
s
d
u
r
in
g
th
e
tr
ai
n
in
g
p
h
ase.
T
h
is
alg
o
r
ith
m
ag
g
r
e
g
ates
a
co
llectio
n
o
f
d
ec
is
io
n
tr
ee
s
to
p
er
f
o
r
m
class
if
icatio
n
an
d
r
eg
r
ess
io
n
o
p
er
atio
n
s
,
m
a
k
in
g
it c
a
p
ab
le
o
f
m
ak
in
g
ac
cu
r
ate
p
r
ed
ictio
n
s
[
1
8
]
.
2
.
2
.
XG
-
B
o
o
s
t
Gr
ad
ien
t
b
o
o
s
tin
g
is
an
ef
f
ec
tiv
e
s
u
p
er
v
is
ed
lear
n
in
g
t
ec
h
n
iq
u
e
th
at
ai
m
s
to
m
ak
e
p
r
ec
is
e
p
r
ed
ictio
n
s
o
f
a
tar
g
et
v
ar
iab
l
e.
I
t
ac
h
iev
es
th
is
b
y
ag
g
r
eg
at
in
g
th
e
p
r
e
d
ictio
n
s
o
f
s
ev
er
al
wea
k
m
o
d
els.
T
h
is
alg
o
r
ith
m
is
p
a
r
ticu
lar
ly
a
d
v
a
n
tag
eo
u
s
wh
e
n
d
ea
lin
g
wit
h
d
atasets
o
f
m
ed
iu
m
s
ize.
XGBo
o
s
t
s
h
o
ws
g
o
o
d
r
esu
lts
in
ter
m
s
o
f
s
p
ee
d
,
ac
cu
r
ac
y
,
a
n
d
e
f
f
icien
cy
[
1
9
]
.
T
h
e
r
e
ar
e
s
ev
e
r
al
v
a
r
iatio
n
s
o
f
th
e
g
r
ad
ie
n
t
b
o
o
s
tin
g
alg
o
r
ith
m
,
in
cl
u
d
in
g
XGBo
o
s
t,
L
ig
h
tGB
M,
an
d
C
atB
o
o
s
t.
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
C
o
mp
a
r
a
tive
o
f p
r
ed
ictio
n
a
l
g
o
r
ith
ms fo
r
en
erg
y
co
n
s
u
mp
t
io
n
b
y
elec
tr
ic
ve
h
icle
…
(
A
yo
u
b
A
b
id
a
)
4195
2
.
3
.
K
NN
T
h
e
KNN
alg
o
r
ith
m
is
a
ty
p
e
o
f
in
s
tan
ce
-
b
ased
lear
n
i
n
g
m
eth
o
d
u
s
ed
in
m
ac
h
i
n
e
l
ea
r
n
in
g
f
o
r
class
if
icatio
n
an
d
r
e
g
r
ess
io
n
t
ask
s
[
2
0
]
.
I
t
o
p
e
r
ates
o
n
th
e
p
r
em
is
e
th
at
s
im
ilar
d
ata
p
o
in
t
s
ar
e
clo
s
e
t
o
ea
ch
o
th
er
.
T
h
e
alg
o
r
ith
m
ca
lc
u
lates
th
e
d
is
tan
ce
b
etwe
en
a
q
u
er
y
ex
am
p
le
an
d
ev
er
y
ex
a
m
p
le
in
th
e
d
ataset,
s
o
r
ts
th
em
in
ascen
d
in
g
o
r
d
er
,
an
d
s
elec
ts
th
e
f
ir
s
t
K
ex
am
p
les
[
2
1
]
.
I
n
class
if
icatio
n
task
s
,
it
ass
ig
n
s
th
e
m
o
s
t
co
m
m
o
n
lab
el
am
o
n
g
th
e
K
ex
am
p
les
to
t
h
e
q
u
er
y
ex
a
m
p
le.
I
n
class
if
icatio
n
task
s
,
it
ass
ig
n
s
th
e
m
o
s
t
co
m
m
o
n
lab
el
am
o
n
g
t
h
e
K
e
x
am
p
les
to
th
e
q
u
er
y
ex
a
m
p
le
.
I
n
r
eg
r
ess
io
n
task
s
,
it
ass
ig
n
s
th
e
m
ea
n
v
alu
e
o
f
th
e
lab
els o
f
th
e
K
ex
am
p
les t
o
th
e
q
u
er
y
ex
am
p
le.
T
h
e
ch
o
i
ce
o
f
K,
th
e
n
u
m
b
er
o
f
n
eig
h
b
o
r
s
to
co
n
s
id
er
,
is
a
cr
itical
f
ac
to
r
in
th
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
alg
o
r
ith
m
.
I
n
th
is
wo
r
k
,
th
e
K
p
ar
am
eter
was
s
elec
ted
af
ter
m
u
ltip
le
iter
atio
n
s
,
an
d
we
s
elec
ted
th
e
K
wh
ich
g
av
e
th
e
b
est m
etr
ic
r
esu
lts
.
2
.
4
.
Ri
dg
e
re
g
r
ess
o
r
R
id
g
e
r
eg
r
ess
io
n
is
a
r
em
ed
ial
m
ea
s
u
r
e
tak
en
to
allev
i
ate
m
u
ltico
llin
ea
r
ity
am
o
n
g
r
eg
r
ess
io
n
p
r
ed
icto
r
v
ar
iab
les
i
n
a
m
o
d
el.
Of
ten
u
s
ed
in
m
ac
h
in
e
le
ar
n
in
g
,
it
is
a
tec
h
n
iq
u
e
f
o
r
an
aly
zin
g
m
u
ltip
le
r
eg
r
ess
io
n
d
ata
th
at
s
u
f
f
er
f
r
o
m
m
u
ltico
llin
ea
r
ity
.
B
y
ad
d
i
n
g
a
d
eg
r
ee
o
f
b
ias
to
th
e
r
e
g
r
ess
io
n
esti
m
ates,
R
id
g
e
r
eg
r
ess
io
n
r
ed
u
ce
s
th
e
s
tan
d
ar
d
e
r
r
o
r
s
[
2
2
]
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
As
m
en
tio
n
ed
b
e
f
o
r
e,
th
is
p
ap
er
aim
s
to
s
et
a
p
er
f
o
r
m
a
n
ce
co
m
p
ar
is
o
n
b
etwe
en
th
e
al
g
o
r
ith
m
s
an
d
th
en
r
ev
ea
l
th
eir
im
p
o
r
tan
ce
a
n
d
p
o
wer
f
u
l
ch
ar
ac
ter
is
tics
.
T
h
e
ad
d
ed
v
al
u
e
o
f
th
is
wo
r
k
l
ies
in
th
e
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
;
in
t
h
is
wo
r
k
,
we
tr
ied
to
s
h
ed
lig
h
t
o
n
f
u
tu
r
e
alg
o
r
ith
m
s
to
ad
d
th
em
to
t
h
e
lis
t
o
f
th
o
s
e
u
s
ed
f
o
r
E
V
ch
ar
g
in
g
b
e
h
av
i
o
r
p
r
ed
ictio
n
s
an
d
p
r
o
v
id
e
o
u
r
co
n
tr
ib
u
tio
n
to
th
e
ex
is
tin
g
liter
atu
r
e.
T
ab
le
1
r
ev
ea
ls
th
e
r
esu
lts
o
b
tain
ed
f
o
r
th
e
f
o
u
r
alg
o
r
ith
m
s
u
s
ed
in
th
is
wo
r
k
.
T
ab
le
1
.
M
etr
ics
co
m
p
ar
ativ
e
r
esu
lts
o
f
AI
alg
o
r
ith
m
s
M
A
E
(
k
W
h
)
M
S
E
(
k
W
h
)
M
A
P
E
(
%)
Ex
e
c
u
t
i
o
n
t
i
m
e
(
mi
n
)
K
N
N
4
.
8
5
3
8
.
2
9
0
.
2
3
0
.
1
4
XG
-
B
o
o
s
t
4
.
2
0
3
3
.
6
6
0
.
2
3
5
.
8
5
R
a
n
d
o
m
-
F
o
r
e
st
4
.
3
3
3
5
.
8
5
0
.
2
4
2
0
.
8
2
R
i
d
g
e
-
R
e
g
r
e
ss
o
r
5
.
0
4
4
1
.
2
2
0
.
2
6
0
.
1
4
I
n
th
is
an
al
y
s
is
,
we
ex
am
in
e
d
th
e
p
e
r
f
o
r
m
an
ce
o
f
d
if
f
er
e
n
t
m
o
d
els
b
ased
o
n
v
a
r
io
u
s
m
e
tr
ics.
T
h
e
tab
le
p
r
o
v
id
ed
in
clu
d
es
th
e
MA
E
,
MSE
,
MA
PE
,
an
d
ex
e
cu
tio
n
tim
e
f
o
r
ea
ch
m
o
d
el.
Am
o
n
g
t
h
e
m
o
d
els,
XGBo
o
s
t
d
em
o
n
s
tr
ated
th
e
b
est
ac
cu
r
ac
y
,
as
it
ac
h
iev
ed
th
e
lo
we
s
t
MA
E
an
d
MSE
v
alu
es
o
f
4
.
2
0
k
W
h
an
d
33
.
6
6
k
W
h
,
r
esp
ec
tiv
ely
.
T
h
i
s
in
d
icate
s
th
at
X
GB
o
o
s
t
i
s
m
o
r
e
ef
f
ec
tiv
e
in
p
r
e
d
ictin
g
en
er
g
y
co
n
s
u
m
p
tio
n
co
m
p
ar
ed
to
th
e
o
th
er
m
o
d
el
s
.
I
n
te
r
m
s
o
f
ex
ec
u
tio
n
tim
e,
r
an
d
o
m
f
o
r
est
h
ad
th
e
lo
n
g
est
d
u
r
atio
n
,
tak
in
g
20
.
8
2
m
in
u
tes
to
c
o
m
p
lete.
I
n
co
n
tr
ast,
KNN
an
d
r
id
g
e
r
e
g
r
ess
o
r
h
ad
r
elativ
ely
s
h
o
r
ter
ex
ec
u
tio
n
tim
es
o
f
0
.
1
4
an
d
0
.
8
8
m
in
u
tes,
r
esp
ec
tiv
ely
.
I
t
is
im
p
o
r
tan
t
t
o
n
o
te
th
at
th
e
ch
o
ice
o
f
th
e
b
est
m
o
d
el
d
ep
e
n
d
s
o
n
th
e
s
p
ec
if
ic
r
eq
u
ir
em
en
ts
an
d
p
r
i
o
r
ities
o
f
th
e
task
at
h
an
d
.
I
f
ac
cu
r
ac
y
is
th
e
p
r
im
ar
y
c
o
n
ce
r
n
,
XGBo
o
s
t
wo
u
ld
b
e
th
e
p
r
ef
e
r
r
ed
ch
o
ice.
Ho
we
v
er
,
if
ex
ec
u
tio
n
tim
e
is
a
cr
itical
f
ac
to
r
,
KNN
o
r
r
id
g
e
r
eg
r
e
s
s
o
r
m
ig
h
t b
e
m
o
r
e
s
u
itab
le
o
p
tio
n
s
.
T
h
is
an
aly
s
is
p
r
o
v
id
es
v
alu
ab
le
i
n
s
ig
h
ts
in
to
th
e
p
er
f
o
r
m
a
n
ce
o
f
d
if
f
er
en
t
m
o
d
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e
th
e
L
STM
m
o
d
el
'
s
ef
f
ec
tiv
en
ess
in
f
o
r
ec
asti
n
g
task
s
with
in
th
is
p
ar
ticu
lar
co
n
tex
t.
Ad
d
itio
n
ally
in
[
2
5
]
wh
en
a
u
th
o
r
s
an
al
y
ze
d
m
ac
r
o
-
d
ata
with
s
im
p
le
p
atter
n
s
,
th
e
au
to
r
eg
r
es
s
iv
e
in
teg
r
ated
m
o
v
in
g
av
er
ag
e
(
AR
I
MA
)
m
o
d
el
with
r
eg
r
ess
o
r
s
o
u
tp
er
f
o
r
m
ed
o
th
er
m
eth
o
d
s
,
f
o
ll
o
wed
b
y
tr
i
g
o
n
o
m
etr
ic,
b
o
x
-
co
x
tr
an
s
f
o
r
m
,
AR
MA
er
r
o
r
s
,
tr
en
d
an
d
s
ea
s
o
n
al
co
m
p
o
n
en
ts
(
T
B
AT
S),
ar
tific
ial
n
eu
r
al
n
etwo
r
k
(
ANN)
.
T
h
is
s
u
g
g
ests
th
at
f
o
r
s
tr
aig
h
tf
o
r
war
d
m
ac
r
o
-
lev
el
d
ata,
tr
ad
itio
n
al
s
tatis
tical
ap
p
r
o
ac
h
es
an
d
s
im
p
ler
m
ac
h
i
n
e
lear
n
i
n
g
m
o
d
els
m
ay
b
e
m
o
r
e
ef
f
ec
tiv
e
th
an
co
m
p
lex
d
ee
p
lear
n
in
g
tech
n
i
q
u
es.
T
h
is
s
tu
d
y
co
m
p
ar
ed
m
ac
h
in
e
lear
n
in
g
m
o
d
els
f
o
r
p
r
e
d
ictin
g
en
er
g
y
co
n
s
u
m
p
tio
n
in
E
V
ch
ar
g
er
s
,
ev
alu
atin
g
th
eir
p
er
f
o
r
m
an
ce
u
s
in
g
v
ar
io
u
s
m
etr
ics.
I
ts
s
ig
n
if
ican
ce
lies
in
p
r
o
v
id
in
g
p
r
ac
tical
in
s
ig
h
ts
f
o
r
en
er
g
y
m
an
ag
em
en
t,
s
u
p
p
o
r
ti
n
g
g
r
id
r
eso
u
r
ce
p
lan
n
in
g
,
an
d
ad
v
an
cin
g
p
r
ed
ictiv
e
m
o
d
elin
g
in
th
e
s
u
s
tain
ab
le
tr
an
s
p
o
r
tatio
n
s
ec
to
r
.
B
y
o
f
f
er
in
g
m
o
d
el
co
m
p
ar
is
o
n
s
,
2
0
2
4
f
o
r
ec
asts
,
an
d
b
en
ch
m
a
r
k
in
g
ag
ain
s
t
o
th
er
r
esear
ch
,
th
e
s
tu
d
y
aid
s
d
ec
is
io
n
-
m
ak
e
r
s
in
s
elec
tin
g
ap
p
r
o
p
r
iate
m
o
d
e
ls
f
o
r
en
er
g
y
co
n
s
u
m
p
tio
n
f
o
r
ec
asti
n
g
,
co
n
tr
ib
u
tin
g
to
m
o
r
e
ef
f
icien
t
an
d
s
u
s
tain
ab
le
p
r
ac
tices in
th
e
ex
p
an
d
in
g
elec
tr
ic
v
eh
icle
in
d
u
s
tr
y
.
Fo
r
p
o
wer
d
is
tr
ib
u
t
o
r
s
,
it'
s
im
p
er
ativ
e
to
h
a
v
e
an
in
-
d
ep
th
u
n
d
er
s
tan
d
in
g
o
f
th
e
lo
ad
p
r
o
f
i
le
o
f
ea
c
h
elec
tr
ic
v
eh
icle
(
E
V)
ch
ar
g
e
r
to
o
p
tim
ize
en
er
g
y
m
an
a
g
em
en
t
d
u
r
in
g
h
ig
h
-
d
em
a
n
d
p
er
io
d
s
[
2
6
]
.
T
h
is
n
ec
ess
itates
th
e
f
o
r
m
u
latio
n
o
f
a
ch
ar
ac
ter
is
tic
co
n
s
u
m
p
ti
o
n
cu
r
v
e
f
o
r
ea
ch
E
V
ch
a
r
g
e
r
in
r
esid
en
tial
an
d
non
-
r
esid
e
n
tial
s
ec
to
r
s
[
2
7
]
,
o
r
a
clu
s
ter
o
f
an
alo
g
o
u
s
c
h
ar
g
er
s
,
p
r
ed
icate
d
o
n
co
n
g
r
u
en
t
co
n
s
u
m
e
r
lo
a
d
p
r
o
f
iles
[
2
8
]
,
[
2
9
]
.
4.
CO
NCLU
SI
O
N
I
n
s
u
m
m
ar
y
,
th
is
r
esear
ch
f
o
cu
s
es
o
n
d
em
a
n
d
-
s
id
e
m
an
ag
em
en
t
b
y
p
r
e
d
ictin
g
f
u
tu
r
e
en
er
g
y
co
n
s
u
m
p
tio
n
f
o
r
E
V
ch
ar
g
er
s
,
ad
d
r
ess
in
g
a
g
ap
in
th
e
liter
atu
r
e.
B
y
em
p
lo
y
in
g
m
ac
h
in
e
l
ea
r
n
in
g
alg
o
r
ith
m
s
an
d
s
tatis
tical
m
o
d
elin
g
,
th
e
s
tu
d
y
f
o
r
ec
asts
E
V
ch
ar
g
in
g
d
em
a
n
d
f
o
r
2
0
2
4
,
p
r
o
v
id
in
g
i
n
s
ig
h
ts
th
at
ca
n
b
e
g
en
er
alize
d
to
o
th
er
t
y
p
es
o
f
ch
ar
g
er
s
.
T
h
e
co
m
p
ar
is
o
n
o
f
alg
o
r
ith
m
s
,
in
cl
u
d
in
g
KNN,
XGBo
o
s
t,
r
id
g
e
r
eg
r
ess
o
r
,
a
n
d
r
an
d
o
m
f
o
r
est,
r
ev
ea
ls
th
at
KNN
o
f
f
er
s
s
u
p
er
io
r
ac
cu
r
ac
y
in
p
r
ed
ictin
g
E
V
ch
ar
g
in
g
b
e
h
av
io
r
.
T
h
es
e
f
in
d
in
g
s
ar
e
s
ig
n
if
ica
n
t
f
o
r
d
is
tr
ib
u
tio
n
o
p
e
r
ato
r
s
,
as
th
ey
h
elp
m
a
n
ag
e
th
e
b
alan
ce
b
etwe
en
en
er
g
y
g
en
er
atio
n
an
d
co
n
s
u
m
p
tio
n
,
p
r
ev
en
tin
g
g
r
id
is
s
u
es
lik
e
o
v
er
lo
ad
s
.
T
h
e
r
esear
ch
co
n
tr
ib
u
tes
to
th
e
f
ield
b
y
o
f
f
er
in
g
a
r
eliab
le
m
et
h
o
d
f
o
r
p
r
ed
ictin
g
E
V
c
h
ar
g
in
g
d
em
a
n
d
,
aid
in
g
in
ef
f
icien
t e
n
er
g
y
m
an
ag
em
en
t.
As
a
p
r
o
g
r
ess
io
n
a
n
d
ex
ten
s
io
n
o
f
th
is
r
esear
ch
,
we
en
v
is
ag
e
wo
r
k
i
n
g
o
n
lo
ad
p
r
o
f
ilin
g
.
T
h
is
p
ap
er
will
lev
er
ag
e
t
h
e
s
am
e
d
ataset
an
d
will
in
teg
r
ate
clu
s
ter
in
g
alg
o
r
ith
m
s
to
f
u
r
th
er
r
ef
i
n
e
o
u
r
u
n
d
er
s
ta
n
d
in
g
o
f
E
V
ch
ar
g
e
r
l
o
ad
p
r
o
f
iles
.
C
lu
s
ter
in
g
alg
o
r
ith
m
s
will
allo
w
u
s
to
id
en
tif
y
a
n
d
g
r
o
u
p
s
im
ilar
lo
ad
p
r
o
f
iles
,
en
h
an
cin
g
th
e
g
r
an
u
lar
ity
o
f
o
u
r
in
s
ig
h
ts
an
d
allo
win
g
f
o
r
m
o
r
e
tailo
r
ed
en
er
g
y
m
an
ag
e
m
en
t
s
tr
ateg
ies.
T
h
is
ex
ten
d
s
o
u
r
co
n
tr
ib
u
tio
n
to
th
e
f
ield
b
y
p
r
o
v
id
in
g
a
m
o
r
e
n
u
an
ce
d
u
n
d
er
s
tan
d
in
g
o
f
co
n
s
u
m
p
tio
n
b
e
h
av
io
r
s
an
d
o
f
f
er
in
g
r
o
b
u
s
t to
o
ls
f
o
r
e
n
er
g
y
m
an
ag
em
e
n
t in
th
e
co
n
t
ex
t o
f
E
V
c
h
ar
g
in
g
.
F
UNDING
I
NF
O
R
M
A
T
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O
N
Au
th
o
r
s
s
tate
n
o
f
u
n
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in
g
in
v
o
lv
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.
AUTHO
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C
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C
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th
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h
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DATA AV
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AB
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T
h
e
d
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,
[
Ay
o
u
b
Ab
id
a
]
.
T
h
e
d
ata,
wh
ich
co
n
tain
in
f
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n
th
at
co
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co
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p
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r
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ar
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ts
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u
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v
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e
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ce
r
tain
r
estrictio
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s
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RE
F
E
R
E
NC
E
S
[
1
]
S
.
G
r
i
f
f
i
t
h
s
a
n
d
R
.
W
e
i
j
e
r
m
a
r
s,
“
I
n
t
r
o
d
u
c
t
i
o
n
t
o
e
n
e
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g
y
s
t
r
a
t
e
g
y
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e
v
i
e
w
s
t
h
e
me
i
ss
u
e
‘
S
t
r
a
t
e
g
y
o
p
t
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o
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s
a
n
d
mo
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e
l
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e
M
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l
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Ea
st
a
n
d
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o
r
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h
A
f
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(
M
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g
y
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r
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s
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n
,
’
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rg
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Re
v
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s
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v
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l
.
2
,
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o
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1
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1
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n
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3
,
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:
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0
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1
6
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.
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.
2
0
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3
.
0
4
.
0
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[
2
]
S
.
K
e
e
t
a
l
.
,
“
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(
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2
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)
:
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c
u
s
e
d
o
n
t
h
e
d
e
v
e
lo
p
m
e
n
t
o
f
v
e
h
icle
-
to
-
g
rid
p
r
o
to
c
o
ls
with
i
n
sm
a
rt
g
rid
s,
u
ti
li
z
i
n
g
a
rti
ficia
l
in
telli
g
e
n
c
e
.
Th
is
w
o
rk
is
p
iv
o
tal
i
n
t
h
e
re
a
lm
o
f
sm
a
rt
g
rid
tec
h
n
o
l
o
g
y
a
n
d
e
lec
tri
c
v
e
h
i
c
le
in
teg
ra
ti
o
n
,
a
imi
n
g
to
o
p
ti
m
ize
th
e
two
-
wa
y
e
n
e
rg
y
e
x
c
h
a
n
g
e
b
e
twe
e
n
e
lec
tri
c
v
e
h
icle
s
a
n
d
t
h
e
p
o
we
r
g
ri
d
.
His
re
se
a
rc
h
e
n
d
e
a
v
o
rs
c
o
n
tri
b
u
te
sig
n
ifi
c
a
n
tl
y
to
e
n
h
a
n
c
in
g
sm
a
rt
g
ri
d
p
e
rf
o
rm
a
n
c
e
,
p
ro
m
o
ti
n
g
th
e
i
n
teg
ra
ti
o
n
o
f
e
lec
tri
c
v
e
h
icle
s,
a
n
d
o
p
ti
m
izin
g
e
n
e
rg
y
u
se
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
a
y
o
u
b
.
a
b
i
d
a
1
-
e
tu
@e
t
u
.
u
n
i
v
h
2
c
.
m
a
a
n
d
a
y
o
u
b
a
b
id
a
0
8
@
g
m
a
il
.
c
o
m
.
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u
r
a
d
Ze
g
r
a
r
i
is
a
g
ra
d
u
a
te
in
e
lec
tri
c
a
l
e
n
g
i
n
e
e
rin
g
fr
o
m
t
h
e
Hig
h
e
r
No
rm
a
l
S
c
h
o
o
l
o
f
Tec
h
n
ica
l
E
d
u
c
a
ti
o
n
(ENS
ET
)
i
n
Ra
b
a
t.
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o
b
tain
e
d
a
Dip
lo
m
a
o
f
A
d
v
a
n
c
e
d
S
tu
d
ies
(DES
A),
th
e
n
d
e
fe
n
d
e
d
h
is
Na
ti
o
n
a
l
Do
c
to
ra
te
th
e
sis
i
n
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g
a
t
Ha
ss
a
n
II
Un
iv
e
rsity
M
o
h
a
m
m
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d
ia
in
2
0
1
2
.
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is
a
fo
rm
e
r
p
ro
fe
ss
o
r
a
t
th
e
F
a
c
u
lt
y
o
f
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c
ien
c
e
s
a
n
d
Tec
h
n
iq
u
e
s
o
f
M
o
h
a
m
m
e
d
ia
(F
S
TM
),
wh
e
re
h
e
tau
g
h
t
El
e
c
tri
c
it
y
,
El
e
c
tro
tec
h
n
ics
,
P
o
we
r
El
e
c
tro
n
ic
s,
El
e
c
tri
c
M
a
c
h
in
e
s,
a
n
d
I
n
d
u
stri
a
l
Co
n
tr
o
l.
S
in
c
e
2
0
1
3
,
h
e
h
a
s
b
e
e
n
th
e
h
e
a
d
o
f
th
e
e
lec
tri
c
a
l
e
n
g
i
n
e
e
rin
g
d
e
p
a
rtme
n
t
a
t
t
h
e
N
a
ti
o
n
a
l
S
c
h
o
o
l
o
f
Arts
a
n
d
Cra
fts
(ENS
AM)
in
Ca
sa
b
lan
c
a
.
He
wa
s
a
wa
rd
e
d
t
h
e
t
it
le
o
f
“
S
tar
P
r
o
fe
ss
o
r
”
b
y
Lav
a
l
Un
iv
e
rsity
-
Ca
n
a
d
a
in
2
0
0
5
.
C
u
rre
n
tl
y
,
h
e
is
a
m
e
m
b
e
r
o
f
t
h
e
Lab
o
ra
to
r
y
o
f
El
e
c
tr
o
n
ics
,
El
e
c
tro
tec
h
n
i
c
s,
Au
t
o
m
a
ti
o
n
a
n
d
In
fo
rm
a
ti
o
n
P
ro
c
e
ss
in
g
(LE
EA
-
T
I),
o
f
REUNET
a
n
d
a
u
t
h
o
r
o
f
se
v
e
ra
l
re
se
a
rc
h
wo
rk
s
o
n
t
h
e
m
o
d
e
li
n
g
a
n
d
c
o
n
tro
l
o
f
re
n
e
wa
b
le
e
n
e
rg
y
s
y
ste
m
s
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
z
e
g
ra
ri.
e
n
sa
m
@g
m
a
il
.
c
o
m
.
Re
d
o
u
a
n
e
M
a
jd
o
u
l
is
a
p
ro
fe
ss
o
r
a
t
th
e
Na
ti
o
n
a
l
S
c
h
o
o
l
o
f
Arts
a
n
d
Cra
ft
s
(ENS
AM)
in
Ca
sa
b
lan
c
a
.
As
a
n
a
ss
o
c
iate
p
ro
fe
ss
o
r
in
El
e
c
tri
c
a
l
En
g
i
n
e
e
rin
g
,
h
e
b
ri
n
g
s
a
we
a
lt
h
o
f
k
n
o
wle
d
g
e
a
n
d
e
x
p
e
rti
se
to
h
is
ro
le.
He
o
b
tain
e
d
h
is
d
o
c
to
ra
te
fro
m
th
e
F
a
c
u
lt
y
o
f
S
c
ien
c
e
s
a
n
d
Tec
h
n
iq
u
e
s
in
2
0
1
7
a
n
d
h
a
s
sin
c
e
m
a
d
e
sig
n
ifi
c
a
n
t
strid
e
s
in
h
is
field
.
In
a
d
d
it
i
o
n
to
h
is
p
ro
fe
ss
o
rial
d
u
ti
e
s
,
M
a
j
d
o
u
le
is
a
lso
th
e
c
o
o
r
d
in
a
t
o
r
o
f
th
e
El
e
c
tro
m
e
c
h
a
n
ica
l
En
g
i
n
e
e
rin
g
P
ro
g
ra
m
a
t
ENS
AM
Ca
sa
b
lan
c
a
,
fu
rth
e
r
sh
o
w
c
a
sin
g
h
is
lea
d
e
rsh
ip
a
n
d
c
o
m
m
it
m
e
n
t
to
e
d
u
c
a
ti
o
n
.
His
re
se
a
rc
h
c
o
n
tri
b
u
t
io
n
s
a
re
e
x
ten
siv
e
a
n
d
c
o
v
e
r
a
wi
d
e
a
rra
y
o
f
to
p
ics
in
e
lec
tri
c
a
l
e
n
g
i
n
e
e
rin
g
,
in
c
l
u
d
i
n
g
fu
n
d
a
m
e
n
tal
fre
q
u
e
n
c
y
,
m
o
d
u
lar
m
u
l
ti
lev
e
l
c
o
n
v
e
rter,
m
u
lt
i
lev
e
l
in
v
e
rters
,
p
o
we
r
e
lec
tro
n
ics
,
a
n
d
p
o
we
r
g
rid
.
His
d
u
a
l
ro
le
a
s
a
n
e
d
u
c
a
to
r
a
n
d
a
c
ti
v
e
re
se
a
rc
h
e
r
e
n
a
b
les
h
im
t
o
c
o
n
ti
n
u
a
ll
y
a
d
v
a
n
c
e
th
e
u
n
d
e
rsta
n
d
in
g
o
f
e
lec
tri
c
a
l
e
n
g
in
e
e
rin
g.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
r.
m
a
jd
o
u
l@
g
m
a
il
.
c
o
m
.
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