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I
J
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Vo
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,
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.
6
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Decem
b
er
2
0
2
0
,
p
p
.
5
7
7
9
~
5
7
8
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I
SS
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8708
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DOI
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I
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8
8
-
8708
I
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t J
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p
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n
g
,
Vo
l.
10
,
No
.
6
,
Decem
b
er
2020
:
5
7
7
9
-
5
7
8
4
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a
m
eter
s
u
s
i
n
g
s
o
m
e
i
ter
atio
n
s
o
n
th
e
d
ataset.
I
n
s
o
m
e
s
tu
d
ie
s
,
d
ata
m
i
n
i
n
g
al
g
o
r
ith
m
s
h
av
e
also
b
ee
n
u
s
ed
f
o
r
p
r
ed
ictio
n
.
Si
m
ilar
lear
n
i
n
g
a
lg
o
r
ith
m
s
ar
e
u
s
ed
in
d
ata
m
in
i
n
g
,
j
u
s
t
as
t
h
e
y
ar
e
u
s
ed
i
n
ar
tif
icia
l
n
e
u
r
al
n
et
w
o
r
k
s
[
1
1
,
16,
1
7
]
.
Statis
ti
ca
l
lear
n
i
n
g
m
eth
o
d
s
ar
e
p
r
e
f
er
r
ed
f
o
r
t
w
o
m
ai
n
r
ea
s
o
n
s
,
wh
ich
ar
e
p
r
ed
icti
on
m
o
d
el
s
th
a
t a
r
e
ea
s
y
to
cr
ea
te
an
d
h
a
v
e
h
i
g
h
p
r
ed
ictio
n
ac
cu
r
ac
y
f
o
r
th
e
s
h
o
r
t
-
ter
m
.
Mo
d
el
s
b
ased
o
n
p
h
y
s
ical
s
y
s
te
m
s
ca
n
al
s
o
b
e
u
s
ed
in
win
d
f
o
r
ec
asti
n
g
.
T
h
ese
m
et
h
o
d
s
co
n
s
i
s
t
o
f
a
s
er
ie
s
o
f
d
if
f
er
e
n
tial
eq
u
at
io
n
s
a
n
d
ar
e
u
s
ed
f
o
r
n
u
m
er
ical
w
ea
t
h
e
r
f
o
r
ec
asts
[
1
1
]
.
T
h
ese
p
h
y
s
ic
al
m
et
h
o
d
s
ca
n
b
e
u
s
ed
i
n
lo
n
g
-
ter
m
s
y
s
te
m
s
t
h
a
t
r
eq
u
ir
e
m
o
r
e
d
etailed
i
n
f
o
r
m
atio
n
.
A
ls
o
,
w
h
e
n
t
h
ese
p
h
y
s
ical
s
y
s
te
m
s
ar
e
co
m
p
ar
ed
with
t
h
e
e
s
ti
m
atio
n
s
m
ad
e
u
s
i
n
g
s
tatis
t
ical
lear
n
i
n
g
m
et
h
o
d
s
,
s
u
cc
e
s
s
r
ates
ar
e
lo
w
er
.
Ho
w
e
v
er
,
t
h
e
r
e
ar
e
ap
p
r
o
ac
h
es
th
at
u
s
e
b
o
t
h
th
e
p
h
y
s
ical
m
o
d
el
an
d
s
tati
s
ti
ca
l
m
o
d
els
to
g
eth
er
.
T
h
ese
ar
e
ca
lled
h
y
b
r
id
m
o
d
el
ap
p
r
o
ac
h
,
w
h
er
e
n
u
m
er
ical
w
ea
t
h
er
f
o
r
ec
ast d
ata
is
s
o
l
v
e
d
u
s
i
n
g
s
tati
s
tical
m
o
d
els.
I
n
g
en
er
al,
m
u
lti
-
i
n
p
u
t
lik
e
w
in
d
s
p
ee
d
,
d
ir
ec
tio
n
,
an
d
ot
h
er
m
eteo
r
o
lo
g
ical
d
ata
ar
e
u
s
ed
f
o
r
in
p
u
ts
o
f
th
e
p
r
ed
icti
o
n
alg
o
r
ith
m
[
1
8
-
2
1
]
.
T
h
e
A
r
tific
ial
I
n
tel
lig
e
n
ce
(
AI
)
m
et
h
o
d
o
f
f
er
s
v
er
y
s
u
cc
e
s
s
f
u
l
r
es
u
lts
t
h
a
n
k
s
to
its
ab
ili
t
y
to
m
ap
n
o
n
li
n
ea
r
f
ea
t
u
r
es.
T
h
e
m
ai
n
ad
v
an
tag
e
o
f
A
I
m
et
h
o
d
s
is
to
esti
m
ate
p
o
s
s
ib
l
e
w
i
n
d
s
p
ee
d
s
er
ies
w
it
h
o
u
t
a
p
r
ed
ef
in
ed
m
at
h
e
m
atica
l
t
o
o
l.
A
s
t
h
e
r
ep
r
esen
tativ
e
o
f
ar
tif
ic
ial
i
n
tell
ig
e
n
ce
m
et
h
o
d
s
,
ar
tif
icia
l
n
eu
r
al
n
et
w
o
r
k
s
[
2
2
,
2
3
]
,
f
ee
d
-
f
o
r
w
ar
d
n
eu
r
al
n
et
w
o
r
k
[
2
4
]
an
d
ad
ap
tiv
e
lin
ea
r
ele
m
e
n
t
n
e
u
r
al
n
et
w
o
r
k
f
o
r
w
i
n
d
s
p
ee
d
p
r
ed
ictio
n
ar
e
cu
r
r
en
tl
y
u
s
ed
b
y
r
esear
ch
er
s
[
2
4
-
2
6
]
.
Dee
p
lear
n
in
g
al
g
o
r
ith
m
s
ar
e
p
r
ef
er
r
ed
o
v
er
tr
ad
itio
n
al
n
e
u
r
al
n
e
t
w
o
r
k
s
to
r
ev
ea
l
m
o
r
e
d
etailed
f
ea
t
u
r
es
o
f
w
in
d
s
p
ee
d
s
er
ies.
Dee
p
lear
n
in
g
al
g
o
r
ith
m
s
p
r
o
v
id
e
v
er
y
s
u
cc
ess
f
u
l
r
es
u
lts
in
m
a
n
y
co
m
p
u
ter
-
b
ased
s
en
s
i
n
g
,
v
i
s
io
n
,
s
p
ee
ch
an
d
s
i
g
n
al
p
r
o
ce
s
s
i
n
g
[
2
7
]
.
A
d
ee
p
lear
n
in
g
m
et
h
o
d
h
a
s
b
ee
n
ap
p
lied
to
r
e
v
ea
l
th
e
co
m
p
le
x
f
ea
tu
r
es
o
f
w
i
n
d
s
p
ee
d
d
ata
[
2
8
-
3
0
]
.
E
x
p
er
im
en
tal
r
es
u
lt
s
s
h
o
w
th
at
d
ee
p
l
ea
r
n
in
g
al
g
o
r
ith
m
s
h
av
e
th
e
b
est
p
er
f
o
r
m
a
n
ce
a
m
o
n
g
co
m
p
ar
i
s
o
n
m
o
d
el
s
.
W
h
i
l
e
m
a
n
y
a
n
al
y
s
e
s
in
w
in
d
s
p
ee
d
p
r
ed
ictio
n
s
tu
d
ie
s
p
r
o
d
u
ce
d
th
e
p
r
ed
icted
w
i
n
d
s
p
ee
d
v
alu
es d
ir
ec
tl
y
f
r
o
m
t
h
e
r
a
w
w
i
n
d
s
p
ee
d
d
ata,
th
e
n
o
n
-
s
tatio
n
ar
y
e
f
f
ec
ts
o
f
th
e
w
i
n
d
s
p
ee
d
s
er
ies
w
er
e
also
ig
n
o
r
ed
.
B
y
co
m
p
ar
is
o
n
,
d
ee
p
lear
n
in
g
i
s
a
s
u
b
s
et
o
f
m
ac
h
in
e
lea
r
n
i
n
g
.
Ho
w
e
v
er
,
alth
o
u
g
h
d
ee
p
lear
n
in
g
,
an
d
m
ac
h
i
n
e
lear
n
i
n
g
f
u
n
ct
io
n
s
i
m
ilar
l
y
,
th
e
ir
ca
p
ab
ilit
ies
ar
e
d
if
f
er
en
t
.
d
ee
p
-
lear
n
in
g
a
u
to
m
at
icall
y
f
i
n
d
s
th
e
f
ea
t
u
r
es
to
b
e
u
s
ed
f
o
r
class
i
f
icatio
n
,
w
h
ile
m
ac
h
in
e
lear
n
in
g
p
r
o
v
id
e
s
th
ese
f
ea
t
u
r
es
m
a
n
u
all
y
.
I
n
ad
d
itio
n
,
co
m
p
ar
ed
to
m
ac
h
in
e
lear
n
i
n
g
,
d
ee
p
lear
n
i
n
g
r
eq
u
ir
es
s
tr
o
n
g
er
p
r
o
ce
s
s
o
r
s
an
d
lar
g
er
tr
ain
i
n
g
d
ata
f
o
r
its
r
es
u
lts
.
C
o
m
p
ar
ed
to
A
NN,
d
ee
p
lear
n
i
n
g
o
f
f
er
s
m
o
r
e
la
y
er
s
w
o
r
k
i
n
g
[
3
1
-
3
7
]
.
I
n
t
h
is
s
t
u
d
y
,
a
s
h
o
r
t
-
ter
m
w
i
n
d
s
p
ee
d
f
o
r
ec
asti
n
g
s
y
s
te
m
i
s
p
r
o
p
o
s
ed
u
s
i
n
g
d
ee
p
lear
n
i
n
g
f
o
r
w
i
n
d
tu
r
b
in
es
ap
p
licatio
n
s
.
I
n
s
ec
t
io
n
2
,
th
e
s
y
s
te
m
ar
c
h
itect
u
r
e
an
d
s
y
n
th
e
tic
d
ata
g
en
er
at
io
n
ar
e
ex
p
lain
ed
.
T
h
en
th
e
s
h
o
r
t
-
ter
m
w
in
d
s
p
e
ed
f
o
r
ec
asti
n
g
s
y
s
te
m
is
d
escr
ib
ed
in
t
h
e
s
a
m
e
s
ec
tio
n
.
Sh
o
r
t
-
ter
m
w
i
n
d
s
p
ee
d
f
o
r
ec
asti
n
g
r
es
u
lt
s
ar
e
p
r
esen
t
ed
in
Sectio
n
3
.
Fin
a
ll
y
,
co
n
c
l
u
s
io
n
s
ar
e
p
r
esen
ted
in
s
ec
tio
n
4
.
2.
T
H
E
P
RO
P
O
SE
D
SYS
T
E
M
ARCH
I
T
E
C
T
UR
E
Glo
b
al
w
in
d
d
ata
at
a
2
.
5
-
d
eg
r
ee
r
eso
lu
tio
n
h
av
e
b
ee
n
p
u
b
lis
h
ed
b
y
th
e
Nat
io
n
al
C
en
tr
es
f
o
r
E
n
v
ir
o
n
m
e
n
tal
I
n
f
o
r
m
atio
n
,
n
atio
n
al
o
ce
a
n
ic
a
n
d
at
m
o
s
p
h
er
ic
ad
m
i
n
is
tr
atio
n
(
NO
AA
)
[
3
8
]
.
Daily
w
i
n
d
-
v
elo
cit
y
-
a
v
er
ag
e
s
s
in
ce
1
9
4
8
h
a
v
e
b
ee
n
g
i
v
e
n
i
n
t
h
e
d
at
aset
p
r
o
v
id
e
d
b
y
N
O
AA
.
I
n
o
r
d
er
to
s
i
m
u
late
a
r
ea
lis
tic
s
i
tu
at
io
n
,
a
d
ata
g
e
n
er
atio
n
m
o
d
el
w
as
d
e
s
ig
n
ed
u
s
in
g
t
h
e
d
ail
y
av
er
a
g
e
w
i
n
d
-
v
elo
cit
y
v
al
u
e
s
f
r
o
m
th
e
d
atase
t
a
n
d
g
e
n
er
ated
r
an
d
o
m
v
al
u
es
h
a
v
i
n
g
t
h
e
f
i
n
al
a
v
er
ag
e
v
al
u
e
w
a
s
s
till
co
n
s
is
t
en
t
w
i
th
t
h
e
d
ataset
.
W
e
h
er
eb
y
i
n
ter
p
o
lated
th
e
d
ail
y
av
er
a
g
e
v
a
lu
e
s
to
m
i
n
u
te
-
by
-
m
i
n
u
te
v
a
lu
e
s
.
T
h
e
d
ata
g
en
er
atio
n
m
o
d
el
was
d
esig
n
ed
in
Si
m
u
l
in
k
a
n
d
is
s
h
o
w
n
i
n
Fi
g
u
r
e
1
.
I
n
th
is
m
o
d
el,
d
at
a
g
en
er
ated
b
y
th
e
m
e
th
o
d
ex
p
l
ain
ed
ab
o
v
e
w
er
e
ap
p
lied
to
a
P
I
D
co
n
tr
o
l
s
y
s
te
m
w
h
ich
co
n
tr
o
ls
a
D
C
m
o
to
r
e
m
u
lati
n
g
th
e
w
i
n
d
-
t
u
r
b
in
e.
A
q
u
ad
r
atu
r
e
e
n
co
d
er
w
a
s
co
n
n
ec
ted
to
th
e
r
o
to
r
m
ea
s
u
r
in
g
t
h
e
r
o
tatio
n
al
s
p
ee
d
o
f
t
h
e
m
o
to
r
.
I
n
ter
p
o
lated
w
i
n
d
-
d
ata
w
er
e
s
i
m
u
lated
b
y
t
h
is
ac
tu
al
s
y
s
te
m
a
n
d
4
h
o
u
r
s
o
f
r
ea
l
-
ti
m
e
d
ata
w
er
e
co
lle
cted
f
o
r
ea
ch
d
a
y
o
f
ea
ch
y
ea
r
.
T
h
er
ef
o
r
e,
w
e
co
u
ld
b
e
ab
le
to
g
e
n
er
ate
m
o
r
e
d
etailed
d
ata
th
a
t
i
s
co
n
s
is
ten
t
w
i
th
th
e
d
atase
t.
I
n
th
e
Si
m
u
li
n
k
m
o
d
el
g
i
v
e
n
b
el
o
w
,
E
n
co
d
er
b
lo
ck
m
ea
s
u
r
ed
t
h
e
an
g
u
lar
v
elo
cit
y
o
f
th
e
r
o
to
r
.
An
g
u
lar
v
elo
cit
y
th
a
n
w
as
f
ed
to
t
h
e
P
I
D
C
o
n
tr
o
ller
b
lo
ck
as
th
e
f
ee
d
b
ac
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m
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te
ti
m
e
in
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er
v
al
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also
ac
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p
tab
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h
e
m
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i
m
p
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if
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ce
s
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et
w
ee
n
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h
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atasets
w
h
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h
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er
e
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o
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n
in
Fi
g
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r
e
s
4
an
d
5
,
w
er
e
er
r
o
r
r
ates
an
d
s
p
ac
e
co
m
p
lex
i
t
y
.
T
h
ese
cr
iter
ia
s
h
o
u
l
d
b
e
k
ep
t
in
m
i
n
d
w
h
en
i
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te
g
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atin
g
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s
y
s
te
m
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n
to
ac
t
u
al
ap
p
licatio
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s
.
RE
F
E
R
E
NC
E
S
[1
]
N.
L
.
P
a
n
w
a
r,
S
.
C.
Ka
u
sh
ik
,
a
n
d
S
.
Ko
th
a
ri
,
“
R
o
le
o
f
Re
n
e
wa
b
le
En
e
rg
y
S
o
u
rc
e
s
in
En
v
iro
n
m
e
n
tal
P
ro
tec
ti
o
n
:
A
Re
v
ie
w
,
”
Ren
e
wa
b
le a
n
d
S
u
st
a
in
a
b
le E
n
e
rg
y
Rev
iews
,
v
o
l.
1
5
,
p
p
.
1
5
1
3
-
1
5
2
4
,
2
0
1
1
.
[2
]
S
.
H.
Ku
lk
a
rn
i,
T
.
R.
A
n
il
,
a
n
d
R.
D.
G
o
w
d
a
r
,
“
W
in
d
En
e
rg
y
De
v
e
lo
p
m
e
n
t
in
I
n
d
ia
a
n
d
a
M
e
th
o
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g
y
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o
r
Ev
a
lu
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ti
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g
P
e
rf
o
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n
c
e
o
f
W
in
d
F
a
rm
Clu
ste
rs,”
J
o
u
rn
a
l
o
f
Ren
e
w
a
b
le E
n
e
rg
y
,
v
o
l.
2
0
1
6
,
p
p
.
1
-
16
,
2
0
1
6
.
[3
]
M
.
Ju
n
g
in
g
e
r,
e
t
a
l.
,
“
Of
fsh
o
re
w
in
d
e
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e
rg
y
(Ch
a
p
ter
7
)
in
T
e
c
h
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g
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e
a
rn
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ra
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o
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-
Ca
rb
o
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En
e
rg
y
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y
ste
m
,
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d
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mic
Pre
ss
,
p
p
.
1
0
3
-
1
1
7
,
2
0
2
0
.
[4
]
K.
P
a
rv
in
,
e
t
a
l.
,
“
P
a
rti
c
le
S
w
a
r
m
Op
ti
m
iza
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se
d
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u
z
z
y
L
o
g
ic
M
P
P
T
In
v
e
rter
Co
n
tro
ll
e
r
f
o
r
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rid
Co
n
n
e
c
ted
W
in
d
T
u
rb
in
e
,
”
I
n
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Re
n
e
wa
b
le E
n
e
rg
y
Res
e
a
r
c
h
,
v
o
l.
9
,
n
o
.
1
,
p
p
.
1
6
4
-
1
7
4
,
2
0
1
9
.
[5
]
N.
P
e
ra
in
o
,
e
t
a
l.
,
“
F
e
a
sib
il
it
y
o
f
P
o
w
e
rin
g
A
ll
V
e
h
icle
s
w
it
h
El
e
c
tri
c
it
y
f
ro
m
S
o
lar
a
n
d
W
in
d
En
e
r
g
y
,
”
J
o
u
rn
a
l
o
f
En
e
rg
y
a
n
d
N
a
t
u
ra
l
Res
o
u
rc
e
s,
v
o
l.
8
,
n
o
.
4
,
p
p
.
1
2
7
-
1
3
6
,
2
0
1
9
.
[6
]
E.
A
L
ó
p
e
z
,
C.
Á
n
g
e
le
s
-
Ca
m
a
c
h
o
,
a
n
d
F
.
Ba
ñ
u
e
lo
s
-
Ru
e
d
a
s
,
“
A
d
v
a
n
c
e
d
M
e
th
o
d
o
lo
g
y
f
o
r
F
e
a
sib
il
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y
S
tu
d
ies
o
n
Bu
il
d
i
n
g
-
M
o
u
n
te
d
W
in
d
T
u
rb
in
e
s
In
sta
ll
a
ti
o
n
i
n
Urb
a
n
En
v
ir
o
n
m
e
n
t:
A
p
p
ly
in
g
CF
D
A
n
a
l
y
sis,”
E
n
e
rg
y
,
v
o
l.
1
6
7
,
n
o
.
1
5
,
p
p
.
1
8
1
-
1
8
8
,
2
0
1
9
.
[7
]
T.
F
.
Ish
u
g
a
h
,
e
t
a
l.
,
“
A
d
v
a
n
c
e
s
in
W
in
d
En
e
rg
y
Re
so
u
rc
e
Ex
p
lo
it
a
t
io
n
in
Urb
a
n
E
n
v
iro
n
m
e
n
t:
A
R
e
v
ie
w
,
”
Ren
e
wa
b
le
a
n
d
S
u
sta
in
a
b
le E
n
e
rg
y
Rev
iews
,
v
o
l.
3
7
,
p
p
.
6
1
3
-
6
2
6
,
2
0
1
4
.
[8
]
I.
Ha
sh
e
m
,
M
o
h
a
m
e
d
H.
M
o
h
a
m
e
d
,
a
n
d
A
id
a
A
.
H
a
f
iz
,
“
Ae
r
o
-
A
c
o
u
stics
No
ise
As
se
ss
m
e
n
t
f
o
r
W
in
d
-
L
e
n
s
T
u
rb
in
e
,”
En
e
rg
y
,
v
o
l.
1
1
8
,
p
p
.
3
4
5
-
3
6
8
,
2
0
1
7
.
[
9
]
D
.
M
.
M
a
g
n
u
s
,
L
.
L
.
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f
i
t
s
c
h
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r
,
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n
d
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.
C
.
S
c
h
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l
a
u
,
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s
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,
p
p
.
1
-
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1
9
.
[1
0
]
G
.
H.
Riah
y
,
a
n
d
M
.
A
b
e
d
i,
“
S
h
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m
W
in
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p
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d
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stin
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in
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p
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ti
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sin
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L
in
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r
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re
d
ictio
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M
e
t
h
o
d
,
”
Ren
e
wa
b
le
En
e
rg
y
,
v
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l.
3
3
,
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o
.
1
,
p
p
.
3
5
–
4
1
,
2
0
0
8
.
[1
1
]
Y.
Ji
a
n
g
,
e
t
a
l.
,
“
V
e
r
y
S
h
o
rt
-
T
e
r
m
W
in
d
S
p
e
e
d
F
o
re
c
a
stin
g
w
it
h
Ba
y
e
sia
n
S
tru
c
tu
ra
l
Bre
a
k
M
o
d
e
l,
”
Ren
e
wa
b
le
En
e
rg
y
,
v
o
l.
5
0
,
p
p
.
6
3
7
-
6
4
7
,
2
0
1
3
.
[1
2
]
Y.
Ho
n
g
,
Z.
S
o
n
g
,
a
n
d
A
.
Ku
sia
k
,
“
Ho
u
r
-
A
h
e
a
d
W
in
d
P
o
w
e
r
a
n
d
S
p
e
e
d
F
o
re
c
a
stin
g
Us
in
g
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im
u
lt
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n
e
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s
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e
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rb
a
ti
o
n
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to
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h
a
s
ti
c
A
p
p
ro
x
i
m
a
ti
o
n
(S
P
S
A
)
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lg
o
rit
h
m
a
n
d
Ne
u
ra
l
Ne
tw
o
rk
w
it
h
F
u
z
z
y
In
p
u
ts,”
E
n
e
rg
y
,
v
o
l.
3
5
,
n
o
.
9
,
p
p
.
3
8
7
0
-
3
8
7
6
,
2
0
1
0
.
[1
3
]
R.
G
.
Ka
v
a
ss
e
ri,
a
n
d
K.
S
e
e
th
a
ra
m
a
n
,
“
Da
y
-
A
h
e
a
d
W
in
d
S
p
e
e
d
F
o
re
c
a
stin
g
Us
in
g
F
-
A
RIM
A
M
o
d
e
ls,”
Ren
e
wa
b
le
En
e
rg
y
,
v
o
l.
3
4
,
n
o
.
6
,
p
p
.
1
3
8
8
-
1
3
9
3
,
2
0
0
9
.
[1
4
]
H.
L
iu
,
e
t
a
l.
,
“
A
H
y
b
rid
S
tatisti
c
a
l
M
e
th
o
d
t
o
P
re
d
ict
W
in
d
S
p
e
e
d
a
n
d
W
in
d
P
o
w
e
r,
”
Ren
e
wa
b
le
En
e
rg
y
,
v
o
l.
3
5
,
n
o
.
8
,
p
p
.
1
8
5
7
-
1
8
6
,
2
0
1
0
.
[1
5
]
W.
K.
W
o
n
g
,
M.
X
ia,
a
n
d
W
.
C.
Ch
u
,
“
A
d
a
p
ti
v
e
N
e
u
ra
l
Ne
t
w
o
rk
M
o
d
e
l
f
o
r
T
i
m
e
-
S
e
ries
F
o
re
c
a
sti
n
g
,
”
Eu
ro
p
e
a
n
J
o
u
rn
a
l
o
f
Op
e
ra
ti
o
n
a
l
Res
e
a
rc
h
,
v
o
l.
2
0
7
,
n
o
.
2
,
p
p
.
8
0
7
-
8
1
6
,
2
0
1
0
.
[1
6
]
E.
Ca
d
e
n
a
s
a
n
d
W
.
Riv
e
ra
,
“
W
in
d
S
p
e
e
d
F
o
re
c
a
stin
g
in
T
h
re
e
Diff
e
r
e
n
t
Re
g
io
n
s
o
f
M
e
x
ico
,
U
sin
g
A
H
y
b
rid
A
RIM
A
–
A
NN
M
o
d
e
l,
”
Ren
e
wa
b
le E
n
e
rg
y
,
v
o
l.
3
5
,
n
o
.
1
2
,
p
p
.
2
7
3
2
-
2
7
3
8
,
2
0
1
0
.
[1
7
]
Ku
sia
k
,
e
t
a
l.
,
“
A
n
ti
c
ip
a
to
ry
Co
n
tro
l
o
f
W
in
d
T
u
rb
in
e
s
w
it
h
Da
ta
-
Driv
e
n
P
re
d
ictiv
e
M
o
d
e
ls,”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
E
n
e
rg
y
Co
n
v
e
rs
io
n
,
v
o
l.
2
4
,
p
p
.
7
6
6
-
7
7
4
,
2
0
0
9
.
[
1
8
]
I
.
C
o
l
a
k
,
e
t
a
l
.
,
“
M
u
l
t
i
-
t
i
m
e
s
e
r
i
e
s
a
n
d
t
i
m
e
s
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l
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m
o
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e
l
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n
g
f
o
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n
d
s
p
e
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d
a
n
d
w
i
n
d
p
o
w
e
r
f
o
r
e
c
a
s
t
i
n
g
p
a
r
t
i
:
s
t
a
t
i
s
t
i
c
a
l
m
e
t
h
o
d
s
,
v
e
r
y
s
h
o
r
t
-
t
e
r
m
a
n
d
s
h
o
r
t
-
t
e
r
m
a
p
p
l
i
c
a
t
i
o
n
s
,
”
I
n
t
.
C
o
n
f
.
o
n
R
e
n
e
w
a
b
l
e
E
n
e
r
g
y
R
e
s
e
a
r
c
h
a
n
d
A
p
p
l
.
,
2015.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
10
,
No
.
6
,
Decem
b
er
2020
:
5
7
7
9
-
5
7
8
4
5784
[1
9
]
J.
Zh
a
n
g
,
a
n
d
B.
M
.
H
o
d
g
e
,
“
F
o
r
e
c
a
sta
b
il
it
y
a
s
A
De
si
g
n
Crit
e
rio
n
in
W
in
d
Re
so
u
rc
e
A
ss
e
ss
m
e
n
t
,”
Pro
c
e
e
d
in
g
s
o
f
th
e
8
t
h
I
n
ter
n
a
t
io
n
a
l
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o
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fer
e
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e
o
n
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o
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n
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ti
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C
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ter
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sig
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[2
0
]
E.
Ya
ti
y
a
n
a
,
S
.
Ra
ja
k
a
ru
n
a
,
a
n
d
A
.
G
h
o
sh
,
“
W
in
d
S
p
e
e
d
a
n
d
Dire
c
ti
o
n
F
o
re
c
a
stin
g
f
o
r
W
in
d
P
o
w
e
r
G
e
n
e
ra
ti
o
n
Us
in
g
A
RIM
A
M
o
d
e
l,
”
Au
str
a
la
s
ia
n
U
n
ive
rs
it
ies
Po
we
r
En
g
in
e
e
ri
n
g
C
o
n
fer
e
n
c
e
,
2
0
1
7
.
[2
1
]
G
.
W
.
Ch
a
n
g
,
e
t
a
l.
,
“
A
H
y
b
rid
M
o
d
e
l
f
o
r
F
o
re
c
a
stin
g
W
in
d
S
p
e
e
d
a
n
d
W
in
d
P
o
w
e
r
G
e
n
e
ra
ti
o
n
,
”
IEE
E
Po
we
r a
n
d
En
e
rg
y
S
o
c
iety
Ge
n
e
ra
l
M
e
e
ti
n
g
,
2
0
1
6
.
[2
2
]
S
.
S
.
S
a
n
z
,
e
t
a
l.
,
"
S
h
o
rt
-
T
e
rm
W
in
d
S
p
e
e
d
P
re
d
ictio
n
b
y
H
y
b
ri
d
izin
g
G
lo
b
a
l
a
n
d
M
e
so
sc
a
le
F
o
r
e
c
a
stin
g
M
o
d
e
ls
w
i
t
h
A
r
t
i
f
i
c
i
a
l
N
e
u
r
a
l
N
e
t
w
o
r
k
s
,
"
E
i
g
h
t
h
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
r
e
n
c
e
o
n
H
y
b
r
i
d
I
n
t
e
l
l
i
g
e
n
t
S
y
s
t
e
m
s
,
p
p
.
6
0
8
-
6
1
2
,
2
0
0
8
.
[
2
3
]
D
.
R
e
d
d
y
,
a
n
d
S
.
R
a
m
a
s
a
m
y
,
“
A
b
a
c
k
-
p
r
o
p
a
g
a
t
i
o
n
n
e
t
w
o
r
k
b
a
s
e
d
M
P
P
T
a
l
g
o
r
i
t
h
m
f
o
r
g
r
i
d
-
t
i
e
d
w
i
n
d
e
n
e
r
g
y
s
y
s
t
e
m
w
i
t
h
v
i
e
n
n
a
r
e
c
t
i
f
i
e
r
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
R
e
n
e
w
a
b
l
e
E
n
e
r
g
y
R
e
s
e
a
r
c
h
,
v
o
l
.
9
,
n
o
.
2
,
p
p
.
1
0
9
7
-
1
1
0
7
,
2
0
1
9
.
[2
4
]
S.
M
.
L
a
w
a
n
,
e
t
a
l.
,
“
D
e
v
e
lo
p
m
e
n
t
o
f
W
in
d
M
a
p
p
in
g
Ba
se
d
o
n
A
rti
f
icia
l
Ne
u
ra
l
Ne
t
w
o
rk
(
A
N
N)
f
o
r
En
e
rg
y
Ex
p
lo
ra
ti
o
n
i
n
S
a
ra
w
a
k
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Ren
e
wa
b
le E
n
e
rg
y
Res
e
a
rc
h
,
v
o
l.
4
,
n
o
.
3
,
p
p
.
6
1
8
-
6
2
7
,
2
0
1
.
[2
5
]
S
.
A
.
K
a
lo
g
iro
u
,
"
A
rti
f
icia
l
N
e
u
ra
l
Ne
t
w
o
rk
s
in
Re
n
e
wa
b
le
En
e
rg
y
S
y
ste
m
s
A
p
p
li
c
a
ti
o
n
s:
A
R
e
v
ie
w
,
"
Ren
e
wa
b
le
a
n
d
S
u
sta
i
n
a
b
le E
n
e
rg
y
Rev
iews
,
v
o
l.
5
,
n
o
.
4
,
p
p
.
3
7
3
-
4
0
1
,
2
0
0
1
.
[2
6
]
W
.
Qia
o
a
n
d
D.
L
u
,
“
A
S
u
rv
e
y
o
n
W
in
d
T
u
rb
in
e
Co
n
d
it
io
n
M
o
n
it
o
rin
g
a
n
d
F
a
u
lt
Dia
g
n
o
sis−
P
a
rt
II:
S
ig
n
a
ls
a
n
d
S
ig
n
a
l
P
ro
c
e
ss
in
g
M
e
th
o
d
s
,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s o
n
I
n
d
u
stria
l
El
e
c
tro
n
ics
,
v
o
l
.
6
2
,
n
o
.
1
0
,
p
p
.
6
5
4
6
-
6
5
5
7
,
2
0
1
5
.
[
2
7
]
K.
F
.
A
b
d
u
l
r
a
h
e
e
m
a
n
d
G
.
A
l
-
K
i
n
d
i
,
“
W
i
n
d
T
u
r
b
i
n
e
B
l
a
d
e
F
a
u
l
t
D
e
t
e
c
t
i
o
n
U
s
i
n
g
W
a
v
e
l
e
t
P
o
w
e
r
S
p
e
c
t
r
u
m
a
n
d
E
x
p
e
r
i
m
e
n
t
a
l
M
o
d
a
l
A
n
a
l
y
s
i
s
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
R
e
n
e
w
a
b
l
e
E
n
e
r
g
y
R
e
s
e
a
r
c
h
,
v
o
l
.
8
,
n
o
.
4
,
p
p
.
2
1
6
7
-
2
1
7
9
,
2
0
1
8
.
[2
8
]
K.
W
a
n
g
,
e
t
a
l.
,
“
De
e
p
b
e
li
e
f
n
e
tw
o
rk
-
b
a
s
e
d
k
-
m
e
a
n
s
c
lu
ste
r
a
p
p
ro
a
c
h
f
o
r
s
h
o
rt
-
term
w
in
d
p
o
w
e
r
f
o
re
c
a
stin
g
,
”
En
e
rg
y
,
v
o
l.
1
6
5
,
p
p
.
8
4
0
-
8
5
2
,
2
0
1
8
.
[2
9
]
A.
T
.
S
e
r
g
io
a
n
d
T
.
B.
L
u
d
e
r
m
ir,
“
De
e
p
L
e
a
rn
in
g
f
o
r
W
in
d
S
p
e
e
d
F
o
re
c
a
stin
g
in
No
rth
e
a
ste
rn
Re
g
io
n
o
f
Bra
z
il
,
”
Bra
zili
a
n
Co
n
fer
e
n
c
e
o
n
In
tell
ig
e
n
t
S
y
ste
ms
(
BR
ACIS
),
p
p
.
3
2
2
-
3
2
7
,
2
0
1
5
.
[3
0
]
R.
Y.
Ka
z
a
k
o
v
a
,
e
t
a
l.
,
“
Esti
m
a
ti
o
n
o
f
W
in
d
T
u
rb
i
n
e
G
e
n
e
ra
to
r
M
o
d
e
l
P
a
ra
m
e
ters
Us
in
g
A
rti
f
i
c
ial
In
telli
g
e
n
c
e
M
e
th
o
d
s,”
Ba
lka
n
J
o
u
rn
a
l
o
f
E
lec
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
,
v
o
l.
4
,
n
o
.
2
,
p
p
.
5
1
-
5
7
,
2
0
1
6
.
[3
1
]
M.
N.
Jy
o
th
i
a
n
d
P
.
R
.
Ra
o
,
“
V
e
ry
-
S
h
o
rt
T
e
r
m
W
in
d
P
o
w
e
r
F
o
re
c
a
stin
g
T
h
ro
u
g
h
W
a
v
e
l
e
t
Ba
se
d
A
NFIS
,”
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
P
o
we
r E
lec
tro
n
ics
a
n
d
Dr
ive
S
y
ste
ms
(
IJ
P
EDS
)
,
v
o
l.
9
,
n
o
.
1
,
p
p
.
3
9
7
-
4
0
5
,
2
0
1
8
.
[3
2
]
M.
M
.
T
u
m
a
ri,
e
t
a
l.
,
“
A
m
o
d
if
i
e
d
g
re
y
w
o
lf
o
p
ti
m
iz
e
r
f
o
r
i
m
p
ro
v
in
g
w
in
d
p
lan
t
e
n
e
r
g
y
p
ro
d
u
c
ti
o
n
,”
I
n
d
o
n
e
sia
n
J
o
u
rn
a
l
o
f
El
e
c
trica
l
En
g
in
e
e
rin
g
a
n
d
Co
m
p
u
ter
S
c
ien
c
e
(
IJ
EE
CS
)
,
v
o
l.
1
8
,
n
o
.
3
,
p
p
.
1
1
2
3
-
1
1
2
9
,
2
0
2
0
.
[3
3
]
S
.
Ba
rh
m
i
a
n
d
O.
El
F
a
tn
i,
“
Ho
u
rly
W
in
d
S
p
e
e
d
F
o
re
c
a
stin
g
B
a
se
d
o
n
S
u
p
p
o
rt
V
e
c
to
r
M
a
c
h
in
e
a
n
d
A
rti
f
ici
a
l
Ne
u
ra
l
Ne
tw
o
rk
s
,”
In
te
rn
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Arti
fi
c
i
a
l
I
n
telli
g
e
n
c
e
,
v
o
l.
8
,
n
o
.
3
,
p
p
.
2
8
6
-
2
9
1
,
2
0
1
9
.
[3
4
]
M
.
Kh
o
d
a
y
a
r,
O.
Ka
y
n
a
k
,
a
n
d
M
.
E.
Kh
o
d
a
y
a
r.
,
“
Ro
u
g
h
De
e
p
Ne
u
ra
l
A
rc
h
it
e
c
tu
re
f
o
r
S
h
o
rt
-
T
e
rm
W
in
d
S
p
e
e
d
F
o
re
c
a
stin
g
,
”
IEE
E
T
r
a
n
sa
c
t
io
n
s
o
n
I
n
d
u
stria
l
In
f
o
rm
a
ti
c
s
,
v
o
l
.
1
3
,
n
o
.
6
,
p
p
.
2
2
7
0
-
2
7
7
9
,
2
0
1
7
.
[3
5
]
X
.
L
u
o
,
e
t
a
l.
,
“
S
h
o
r
t
-
T
e
r
m
W
i
n
d
S
p
e
e
d
F
o
re
c
a
stin
g
v
ia
S
tac
k
e
d
Ex
trem
e
L
e
a
rn
in
g
M
a
c
h
in
e
w
it
h
G
e
n
e
ra
li
z
e
d
Co
rre
n
tro
p
y
,”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
In
d
u
stria
l
In
fo
rm
a
ti
c
s
,
v
o
l.
1
4
,
n
o
.
1
1
,
p
p
.
4
9
6
3
-
4
9
7
1
,
2
0
1
8
.
[3
6
]
J.
F
u
,
e
t
a
l.
,
“
Co
n
d
it
i
o
n
M
o
n
it
o
rin
g
o
f
W
in
d
T
u
rb
in
e
G
e
a
rb
o
x
Be
a
rin
g
Ba
s
e
d
o
n
De
e
p
Lea
rn
in
g
M
o
d
e
l,
”
IEE
E
Acc
e
ss
,
v
o
l.
7
,
p
p
.
5
7
0
7
8
-
5
7
0
8
7
,
2
0
1
9
.
[3
7
]
B.
Dh
a
v
a
l
a
n
d
A
.
De
sh
p
a
n
d
e
,
"
S
h
o
rt
-
T
e
rm
L
o
a
d
F
o
re
c
a
sti
n
g
w
it
h
Us
in
g
M
u
lt
ip
le
L
in
e
a
r
Re
g
re
ss
io
n
,
"
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
E
lec
t
ric
a
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
,
v
o
l.
1
0
,
n
o
.
4
,
p
p
.
3
9
1
1
-
3
9
1
7
,
2
0
2
0
.
[3
8
]
“
NO
AA
'
s
Na
ti
o
n
a
l
Op
e
ra
ti
o
n
a
l
M
o
d
e
l
A
rc
h
iv
e
a
n
d
Distrib
u
ti
o
n
S
y
st
e
m
(N
OMADS)
,
”
NOAA
,
[
On
li
n
e
]
.
A
v
a
i
la
b
le:
h
tt
p
s:/
/www
.
n
c
d
c
.
n
o
a
a
.
g
o
v
/d
a
ta
-
a
c
c
e
s
s/
m
o
d
e
l
-
d
a
ta/m
o
d
e
l
-
d
a
tas
e
ts.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
G
o
k
h
a
n
Er
d
e
m
ir
re
c
e
i
v
e
d
h
is
B
.
S
c
.
,
M
.
S
c
.
a
n
d
P
h
.
D.
d
e
g
re
e
s
f
ro
m
M
a
r
m
a
ra
Un
iv
e
rsit
y
,
T
u
rk
e
y
,
re
sp
e
c
ti
v
e
l
y
.
Du
rin
g
h
is
P
h
.
D.
,
w
e
w
o
rk
e
d
a
s
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re
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ic
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p
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,
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k
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ra
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(I
T
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,
T
u
rk
e
y
.
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