I
nte
rna
t
io
na
l J
o
urna
l o
f
P
o
w
er
E
lect
ro
nics
a
nd
Driv
e
Sy
s
t
e
m
s
(
I
J
P
E
DS
)
Vo
l.
1
2
,
No
.
3
,
Sep
tem
b
er
202
1
,
p
p
.
1
8
2
3
~
1
8
3
1
I
SS
N:
2088
-
8694
,
DOI
: 1
0
.
1
1
5
9
1
/
i
j
p
ed
s
.
v
1
2
.i
3
.
pp
1
8
2
3
-
1
8
3
1
1823
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
p
e
d
s
.
ia
esco
r
e.
co
m
Wind spee
d
m
o
de
ling
bas
ed on
m
ea
sure
m
ent
data
t
o
predict
fut
ure
w
ind speed w
ith
m
o
dified
Ra
y
leig
h
m
o
del
Su
w
a
rno
,
Ro
ha
na
De
p
a
rtme
n
t
o
f
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
,
Un
iv
e
rsitas
M
u
h
a
m
m
a
d
i
y
a
h
S
u
m
a
tera
Uta
ra
,
M
e
d
a
n
,
In
d
o
n
e
s
ia
Art
icle
I
nfo
AB
ST
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
A
p
r
1
,
2
0
2
1
R
ev
i
s
ed
J
u
n
2
7
,
2
0
2
1
A
cc
ep
ted
J
u
l 1
2
,
2
0
21
T
h
e
d
e
v
e
lo
p
m
e
n
t
o
f
m
o
d
e
li
n
g
w
i
n
d
s
p
e
e
d
p
lay
s
a
v
e
r
y
i
m
p
o
rtan
t
in
h
e
l
p
in
g
to
o
b
tain
th
e
a
c
tu
a
l
w
in
d
sp
e
e
d
d
a
ta
f
o
r
t
h
e
b
e
n
e
f
it
o
f
th
e
p
o
w
e
r
p
lan
t
p
lan
n
in
g
in
th
e
f
u
tu
re
.
T
h
e
w
in
d
sp
e
e
d
in
th
is
p
a
p
e
r
is
o
b
tain
e
d
f
ro
m
a
P
CE
-
F
W
S
2
0
ty
p
e
m
e
a
su
rin
g
in
stru
m
e
n
t
w
it
h
a
d
u
ra
ti
o
n
o
f
3
0
m
in
u
tes
w
h
ich
is
a
c
c
u
m
u
late
d
in
to
m
o
n
th
ly
d
a
ta
fo
r
o
n
e
y
e
a
r
(2
0
1
9
).
De
sp
it
e
th
e
m
a
n
y
w
in
d
sp
e
e
d
m
o
d
e
li
n
g
t
h
a
t
h
a
s
b
e
e
n
d
o
n
e
b
y
re
se
a
r
c
h
e
rs.
M
o
d
e
li
n
g
w
in
d
sp
e
e
d
s
p
ro
p
o
se
d
in
th
is
stu
d
y
we
re
o
b
tain
e
d
f
ro
m
th
e
m
o
d
if
ied
Ra
y
lei
g
h
d
istri
b
u
ti
o
n
.
I
n
t
h
is
stu
d
y
,
th
e
Ra
y
le
ig
h
sc
a
le
f
a
c
to
r
(
C
r
)
a
n
d
m
o
d
if
ied
Ra
y
leig
h
s
c
a
le
fa
c
to
r
(
C
m
)
we
r
e
c
a
lcu
late
d
.
T
h
e
o
b
se
rv
e
d
w
in
d
sp
e
e
d
is
c
o
m
p
a
re
d
w
it
h
th
e
p
re
d
icte
d
win
d
c
h
a
ra
c
teristics
.
T
h
e
d
a
ta
f
it
tes
t
u
se
d
c
o
rre
latio
n
c
o
e
f
f
icie
n
t
(R
2
),
ro
o
t
m
e
a
n
s
sq
u
a
re
e
rro
r
(RM
S
E),
a
n
d
m
e
a
n
a
b
so
lu
te
p
e
rc
e
n
tag
e
e
rro
r
(M
A
P
E).
T
h
e
re
su
lt
s
o
f
th
e
p
ro
p
o
se
d
m
o
d
if
ied
Ra
y
leig
h
m
o
d
e
l
p
ro
v
id
e
v
e
ry
g
o
o
d
re
su
lt
s f
o
r
u
se
rs.
K
ey
w
o
r
d
s
:
Me
asu
r
e
m
en
t d
ata
T
est f
it
-
o
f
d
ata
W
in
d
s
p
ee
d
W
in
d
s
p
ee
d
m
o
d
eli
n
g
T
h
is
is
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Su
w
ar
n
o
Dep
ar
t
m
en
t o
f
E
lectr
ical
E
n
g
i
n
ee
r
in
g
,
Fac
u
lt
y
o
f
E
n
g
i
n
ee
r
in
g
Un
i
v
er
s
ita
s
Mu
h
a
m
m
ad
i
y
a
h
S
u
m
a
ter
a
Utar
a,
St.
Den
ai
No
2
1
7
,
Me
d
an
(
2
0
3
7
1
)
,
I
n
d
o
n
esia
E
m
ail: s
u
w
ar
n
o
@
u
m
s
u
.
ac
.
id
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
w
in
d
s
p
ee
d
is
o
n
e
o
f
th
e
in
d
icato
r
s
f
o
r
m
ea
s
u
r
i
n
g
w
ea
th
er
s
o
m
e
w
h
er
e
an
d
i
s
in
d
icat
ed
b
y
t
h
e
s
teep
n
es
s
o
f
th
e
p
r
ess
u
r
e
d
if
f
er
en
ce
s
.
T
h
e
d
if
f
er
en
ce
i
n
p
r
ess
u
r
e
af
f
ec
ts
th
e
s
tr
o
n
g
a
n
d
w
ea
k
w
in
d
s
p
ee
d
s
.
A
cc
o
r
d
in
g
to
s
e
v
er
al
p
r
ev
io
u
s
r
esear
ch
er
s
[
1
]
,
[
2
]
an
d
o
th
er
s
h
a
v
e
p
r
ese
n
ted
w
i
n
d
s
p
ee
d
p
ar
am
eter
s
[
3
]
-
[
6
]
.
W
in
d
s
p
ee
d
ch
ar
ac
ter
i
s
tics
ar
e
an
al
y
ze
d
i
n
d
ep
th
to
i
m
p
r
o
v
e
th
e
p
er
f
o
r
m
a
n
ce
o
f
t
h
e
e
lect
r
icit
y
p
r
o
d
u
ctio
n
i
n
s
p
ec
if
ic
lo
ca
tio
n
s
[
7
]
-
[
1
0
]
.
A
W
eib
u
ll
d
is
tr
ib
u
tio
n
w
it
h
m
a
x
i
m
u
m
li
k
eli
h
o
o
d
,
en
er
g
y
p
at
ter
n
f
ac
to
r
,
an
d
R
2
w
er
e
an
al
y
ze
d
[
1
1
]
,
c
o
m
p
ar
in
g
t
h
e
f
o
u
r
th
ac
c
u
r
ac
y
is
a
W
eib
u
ll
d
is
tr
ib
u
tio
n
,
R
a
y
l
eig
h
,
Ga
m
m
a,
an
d
lo
g
n
o
r
m
a
l
[
1
2
]
,
co
m
p
ar
is
o
n
W
3
,
W
2
,
Gam
m
a,
L
o
g
n
o
r
m
al
h
as b
ee
n
an
al
y
ze
d
[
1
3
]
,
esti
m
ate
w
in
d
s
p
ee
d
u
s
i
n
g
th
e
W
eib
u
ll d
is
tr
ib
u
tio
n
f
u
n
cti
o
n
[
1
4
]
.
An
es
ti
m
ated
w
i
n
d
s
p
ee
d
est
i
m
at
io
n
tec
h
n
iq
u
e
b
ased
o
n
a
Kal
m
a
n
f
ilter
is
ap
p
lied
to
th
e
w
i
n
d
tu
r
b
in
e
h
as
b
ee
n
ca
r
e
f
u
l
l
y
r
ev
i
e
w
ed
[
1
5
]
.
W
in
d
s
p
ee
d
m
o
d
el
d
ev
elo
p
m
e
n
t
m
o
d
el
MM
OD
A
I
C
E
E
D
AN
-
ba
s
ed
s
i
m
u
lat
io
n
s
ca
n
ex
ce
ed
t
h
e
co
m
p
ar
ati
v
e
m
eth
o
d
[
1
6
]
.
W
eib
u
ll
p
ar
a
m
eter
s
ca
lc
u
lated
u
s
i
n
g
ten
d
i
f
f
er
e
n
t
m
et
h
o
d
s
an
d
a
co
m
p
ar
is
o
n
o
f
p
er
f
o
r
m
a
n
ce
th
a
t,
MO
is
th
e
b
est
m
et
h
o
d
to
f
in
d
th
e
W
eib
u
ll
p
ar
am
eter
s
f
o
r
th
e
en
tire
d
u
r
atio
n
o
f
t
h
e
m
ea
s
u
r
e
m
en
t
[
1
7
]
.
A
n
e
w
s
e
n
s
o
r
les
s
b
ased
w
i
n
d
s
p
ee
d
m
o
d
el
w
as
d
ev
elo
p
ed
u
s
in
g
SC
I
M
a
s
t
h
e
w
in
d
tu
r
b
i
n
e
s
i
m
u
lato
r
[
1
8
]
,
A
NN
m
o
d
el
s
wer
e
d
ev
elo
p
ed
,
th
e
r
es
u
lt
s
s
h
o
w
th
a
t
t
h
e
s
u
itab
ilit
y
o
f
th
e
u
s
e
o
f
A
N
N
in
t
h
e
ac
cu
r
ac
y
o
f
th
e
p
r
ed
icted
[
1
9
]
-
[
2
2
]
.
AR
I
M
A
m
o
d
elin
g
h
as
b
ee
n
f
r
eq
u
en
tl
y
u
s
ed
i
n
r
ec
e
n
t
d
ec
ad
es
to
m
o
d
el
w
i
n
d
s
p
ee
d
an
d
win
d
p
o
w
er
v
ar
iatio
n
o
v
er
lar
g
e
in
ter
v
al
s
,
u
s
u
all
y
o
n
th
e
o
r
d
er
o
f
o
n
e
h
o
u
r
[
2
3
]
,
[
2
4
]
,
an
d
th
is
m
o
d
el
h
as
th
e
ad
v
an
tag
e
o
f
th
e
co
m
p
u
tatio
n
al
co
s
t,
as
well
as
o
n
s
i
m
p
le
r
ep
etiti
v
e
p
r
o
ce
d
u
r
e
[
2
5
]
.
I
n
th
e
e
n
er
g
y
m
ar
k
et,
in
ter
m
s
o
f
in
v
e
s
t
m
e
n
t
s
tr
ateg
ie
s
,
m
o
d
elin
g
w
i
n
d
g
e
n
er
atio
n
o
u
tp
u
t
i
s
v
e
r
y
i
m
p
o
r
tan
t
[
2
6
]
,
in
c
lu
d
i
n
g
v
ar
iatio
n
s
in
en
er
g
y
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
694
I
n
t J
P
o
w
E
lec
&
Dr
i
S
y
s
t
,
Vo
l.
12
,
No
.
3
,
Sep
tem
b
er
202
1
:
182
3
–
183
1
1824
d
em
a
n
d
[
2
7
]
,
an
d
m
o
n
t
h
l
y
v
ar
iatio
n
s
i
n
w
in
d
p
o
w
er
[
2
8
]
.
Sev
er
al
ap
p
r
o
ac
h
es
h
av
e
b
e
en
u
s
ed
to
f
o
r
ec
ast
w
i
n
d
p
o
w
er
b
y
d
ev
elo
p
i
n
g
a
n
alg
o
r
ith
m
ic
m
o
d
el
to
an
ticip
at
e
th
e
lev
e
l
o
f
u
n
ce
r
tai
n
t
y
an
d
v
ar
iab
ilit
y
o
f
w
i
n
d
g
en
er
atio
n
[
2
9
]
.
Yu
r
i
et
a
l.
[
3
0
]
,
m
o
d
eli
n
g
win
d
s
p
ee
d
u
s
i
n
g
Sla
s
h
ed
-
R
a
y
leig
h
,
w
h
er
e
t
h
e
r
atio
b
et
w
ee
n
t
h
e
t
w
o
in
d
ep
en
d
en
t
r
a
n
d
o
m
v
ar
iab
les
,
R
a
y
lei
g
h
d
is
tr
ib
u
tio
n
in
t
h
e
n
u
m
er
ato
r
,
an
d
th
e
p
o
w
er
o
f
a
r
an
d
o
m
v
ar
iab
le
u
n
i
f
o
r
m
in
t
h
e
d
en
o
m
i
n
ato
r
w
h
er
e
R
a
y
lei
g
h
s
liced
to
p
r
o
v
id
e
a
b
etter
m
atc
h
t
h
an
t
h
e
d
is
tr
ib
u
tio
n
o
f
s
la
s
h
-
W
eib
u
ll.
R
a
s
h
ad
et
a
l.
[
3
1
]
,
m
o
d
eled
th
e
w
i
n
d
s
p
ee
d
u
s
i
n
g
t
h
e
R
a
y
lei
g
h
u
n
it
d
is
tr
ib
u
ti
o
n
to
es
ti
m
ate
t
h
e
u
n
iq
u
e
u
n
k
n
o
w
n
p
ar
a
m
eter
.
Kac
h
n
ia
an
d
Sze
w
cz
y
k
[
3
2
]
,
m
o
d
eled
th
e
R
a
y
lei
g
h
d
is
tr
i
b
u
tio
n
w
h
ic
h
w
as
ap
p
lied
to
th
e
h
y
s
ter
e
s
is
cir
cle
o
f
m
a
g
n
e
tic
m
a
ter
ials
.
Yo
lan
d
a
et
a
l.
[
3
3
]
,
m
o
d
eli
n
g
w
i
n
d
s
p
ee
d
w
it
h
R
a
y
le
ig
h
-
L
in
d
le
y
w
it
h
t
h
e
E
M
alg
o
r
ith
m
as
a
n
alter
n
ati
v
e
s
o
lu
tio
n
.
Go
r
la
et
a
l.
[
3
4
]
,
R
a
y
lei
g
h
d
i
s
tr
ib
u
tio
n
m
o
d
el
f
o
r
w
i
n
d
f
ar
m
s
a
n
d
th
e
m
o
n
t
h
l
y
o
u
tp
u
t
i
s
ex
p
ec
ted
t
o
co
n
s
id
er
th
e
s
ea
s
o
n
al
e
f
f
ec
t
o
f
th
e
w
in
d
s
p
ee
d
ca
n
b
e
u
s
ed
.
Gen
er
all
y
,
s
o
m
e
p
r
ev
io
u
s
r
esear
ch
er
s
h
av
e
d
o
n
e
a
m
a
th
e
m
atica
l
m
o
d
elin
g
ap
p
r
o
ac
h
to
th
e
ch
ar
ac
ter
is
tic
s
o
f
w
i
n
d
s
p
ee
d
b
u
t
n
ee
d
to
d
ev
elo
p
o
th
er
m
o
d
els
to
ad
d
to
th
ei
r
k
n
o
w
led
g
e
.
R
a
y
lei
g
h
m
o
d
el
o
f
th
e
p
r
o
p
o
s
ed
m
o
d
if
icat
io
n
s
ai
m
ed
at
m
i
n
i
m
izi
n
g
d
ef
ec
t
c
h
a
r
ac
ter
is
tics
o
b
tai
n
ed
f
r
o
m
a
p
r
ev
io
u
s
.
T
o
g
et
w
i
n
d
s
p
ee
d
m
o
d
elin
g
t
h
at
is
clo
s
er
t
o
th
e
ac
t
u
al
ch
ar
ac
ter
is
tics
,
it
i
s
n
ec
e
s
s
ar
y
to
h
a
v
e
a
m
o
d
el
t
h
at
is
s
u
itab
le
f
o
r
a
c
er
tain
ar
ea
an
d
is
e
x
p
ec
ted
to
b
e
u
s
ed
in
th
e
p
r
o
ce
s
s
o
f
ass
es
s
in
g
t
h
e
p
o
ten
tial
f
o
r
f
u
tu
r
e
w
i
n
d
en
er
g
y
.
R
esear
ch
er
s
co
n
d
u
cted
t
h
e
d
ev
elo
p
m
e
n
t
o
f
m
o
d
eli
n
g
w
i
n
d
s
p
ee
d
w
ith
a
m
o
d
if
ied
R
a
y
lei
g
h
d
is
tr
ib
u
tio
n
m
o
d
el
ap
p
r
o
ac
h
f
o
r
eli
m
i
n
at
in
g
d
e
f
ec
ts
ch
ar
ac
ter
i
s
ti
cs.
A
p
ar
t
f
r
o
m
t
h
e
o
b
s
er
v
ed
ch
ar
ac
ter
is
tics
o
f
th
e
R
a
y
le
ig
h
d
is
tr
ib
u
tio
n
f
u
n
ct
io
n
,
th
e
s
u
i
tab
ilit
y
o
f
t
h
e
m
ea
s
u
r
ed
/r
ec
o
r
d
ed
d
ata
an
d
th
e
m
o
d
elin
g
d
ata
is
al
s
o
an
al
y
ze
d
.
T
h
is
s
t
u
d
y
ai
m
ed
t
o
o
b
tain
a
n
e
w
m
o
d
el
o
f
th
e
m
o
d
i
f
ied
R
a
y
le
ig
h
d
is
tr
ib
u
t
i
o
n
an
d
an
al
y
z
e
t
h
e
s
u
itab
il
it
y
o
f
th
e
c
h
ar
ac
ter
is
tic
s
o
f
w
i
n
d
s
p
ee
d
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
T
h
e
u
s
e
o
f
w
in
d
s
p
ee
d
d
ata
o
b
s
er
v
ed
in
th
i
s
s
t
u
d
y
w
as
o
b
tain
ed
f
r
o
m
t
h
e
m
ea
s
u
r
i
n
g
i
n
s
tr
u
m
e
n
t
P
C
E
-
FW
S
2
0
.
T
h
e
p
r
o
p
o
s
ed
m
o
d
eli
n
g
w
i
n
d
s
p
ee
d
is
ap
p
r
o
ac
h
ed
w
it
h
t
h
e
m
ea
s
u
r
e
m
en
t
d
ata
r
ec
o
r
d
ed
b
y
th
e
d
ev
ice.
B
ased
o
n
th
e
m
ea
s
u
r
e
d
d
ata,
th
en
d
o
th
e
m
o
d
el
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
to
o
b
tain
d
ata
t
h
at
w
ill
b
e
u
s
ed
f
o
r
s
i
m
u
lated
an
d
test
ed
f
o
r
co
m
p
lian
ce
w
it
h
t
h
e
m
ea
s
u
r
ed
d
ata,
th
en
test
in
g
to
en
s
u
r
e
co
n
f
o
r
m
it
y
o
f
th
e
p
r
o
p
o
s
ed
m
o
d
el
o
f
w
i
n
d
s
p
ee
d
.
T
h
e
s
u
itab
ilit
y
test
u
s
es
t
h
e
co
r
r
elatio
n
co
ef
f
ic
ien
t
(
R
2
)
,
r
o
o
t
m
ea
n
s
s
q
u
ar
e
er
r
o
r
(
R
MSE
)
,
an
d
m
ea
n
ab
s
o
lu
te
p
er
ce
n
ta
g
e
er
r
o
r
(
MA
P
E
)
.
2
.
1
.
Wind
s
peed
da
t
a
re
co
rder
P
C
E
-
FW
S
2
0
is
a
w
ir
eless
wea
th
er
s
tatio
n
t
h
at
is
v
er
s
atile
,
as
it
allo
w
s
th
e
ac
c
u
r
ate
r
ec
o
r
d
in
g
o
f
w
i
n
d
d
ir
ec
tio
n
,
w
in
d
f
o
r
ce
,
te
m
p
er
at
u
r
e,
r
elati
v
e
h
u
m
id
it
y
,
an
d
r
ain
f
all.
W
ea
t
h
er
d
ata
i
s
s
en
t
u
p
to
1
0
0
m
eter
s
v
ia
a
r
ad
io
s
ig
n
al
to
th
e
m
ain
s
tatio
n
,
eq
u
ip
p
ed
w
it
h
th
e
late
s
t
tech
n
o
lo
g
y
i
n
w
ea
th
er
an
al
y
s
is
an
d
p
o
w
er
ed
b
y
s
o
lar
p
an
els
a
n
d
b
atter
ies
.
W
i
th
a
U
SB
in
ter
f
ac
e
an
d
t
h
e
i
n
clu
d
ed
USB
ca
b
le,
th
e
w
ea
t
h
er
d
ata
ca
n
b
e
s
en
t
d
ir
ec
tl
y
f
r
o
m
t
h
e
w
ir
ele
s
s
w
ea
th
er
s
ta
tio
n
to
a
P
C
o
r
lap
to
p
.
All
t
h
ese
d
ata
ar
e
s
ta
m
p
ed
with
t
h
e
ti
m
e
/d
ate
to
b
e
s
et
ev
en
a
f
ter
a
lo
n
g
er
p
er
io
d
an
d
w
ea
th
er
d
ata
ca
n
b
e
s
to
r
ed
in
d
ef
in
i
tel
y
.
T
h
e
an
al
y
s
i
s
s
o
f
t
w
ar
e
p
r
o
v
id
ed
m
a
k
es
it
p
o
s
s
ib
le
to
o
b
s
er
v
e
an
d
co
m
p
ar
e
th
e
w
ea
th
er
o
v
e
r
a
lo
n
g
er
p
er
io
d
u
s
in
g
c
h
ar
ts
.
T
h
e
P
C
E
-
FW
S
2
0
W
ea
th
er
Statio
n
all
o
w
s
h
i
g
h
ac
c
u
r
ac
y
d
etec
tio
n
o
f
w
in
d
d
ir
ec
tio
n
,
w
i
n
d
s
p
ee
d
,
te
m
p
er
atu
r
e,
r
elat
iv
e
h
u
m
id
it
y
,
a
n
d
r
ain
f
all.
T
h
e
P
C
E
-
FW
S 2
0
s
tatio
n
is
s
h
o
wn
in
Fig
u
r
e
1
.
(
a)
(
b
)
Fig
u
r
e
1
.
d
ev
ice
P
C
E
-
FW
S 2
0
: (
a)
w
in
d
s
p
ee
d
d
etec
to
r
,
(
b
)
w
i
n
d
s
p
ee
d
r
ec
o
r
d
in
g
d
ev
ice
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
P
o
w
E
lec
&
Dr
i
S
y
s
t
I
SS
N:
2088
-
8
694
Win
d
s
p
ee
d
mo
d
elin
g
b
a
s
ed
o
n
mea
s
u
r
eme
n
t d
a
ta
to
p
r
ed
ict
fu
tu
r
e
w
in
d
s
p
ee
d
… (
S
u
w
a
r
n
o
)
1825
2
.
2
.
Wind
s
peed
da
t
a
W
in
d
s
p
ee
d
d
ata
r
ec
o
r
d
in
g
is
tak
en
b
ased
o
n
th
e
d
u
r
atio
n
o
f
3
0
m
in
u
te
s
in
s
talled
an
d
p
r
o
c
ess
ed
in
to
m
o
n
t
h
l
y
w
i
n
d
s
p
ee
d
d
ata,
f
r
o
m
J
an
u
ar
y
to
Dec
e
m
b
er
2
0
1
9
.
T
h
is
w
i
n
d
s
p
ee
d
d
ata
is
a
b
en
ch
m
ar
k
f
o
r
t
h
e
p
r
o
p
o
s
ed
w
in
d
s
p
ee
d
m
o
d
eli
n
g
an
d
is
a
n
al
y
ze
d
an
d
ev
al
u
ate
d
.
2
.
3
.
M
o
dified
Ra
y
leig
h dis
t
ributio
n
T
h
e
R
a
y
lei
g
h
d
is
tr
ib
u
tio
n
i
s
o
f
ten
u
s
ed
in
p
h
y
s
ics
w
h
e
n
it
co
m
e
s
to
m
o
d
elin
g
p
r
o
ce
s
s
es
s
u
c
h
as
s
o
u
n
d
an
d
l
ig
h
t
r
ad
iatio
n
,
w
a
v
e
h
ei
g
h
t,
an
d
w
in
d
s
p
ee
d
.
I
n
ad
d
itio
n
to
t
h
e
W
eib
u
l
l
d
is
tr
ib
u
tio
n
,
R
a
y
lei
g
h
d
is
tr
ib
u
tio
n
is
al
s
o
a
d
is
tr
ib
u
tio
n
d
ee
m
ed
ap
p
r
o
p
r
iate
to
d
escr
ib
e
t
h
e
d
is
tr
ib
u
tio
n
o
f
w
in
d
s
p
ee
d
.
T
h
is
d
is
tr
ib
u
tio
n
i
s
u
s
ed
w
h
e
n
th
e
W
eib
u
ll d
is
tr
ib
u
tio
n
ar
ea
is
co
n
s
id
er
ed
less
ac
c
u
r
ate
to
ap
p
ly
.
T
h
e
W
eib
u
ll d
is
tr
ib
u
tio
n
f
o
r
Pd
f
an
d
C
d
f
i
s
g
iv
e
n
b
y
(
)
[
(
)
]
(
1
)
*
+
(
2
)
B
y
g
i
v
i
n
g
th
e
s
h
ap
e
p
ar
a
m
ete
r
v
alu
e
(
k
)
o
f
k
=
2
i
n
t
h
e
W
ei
b
u
ll
d
is
tr
ib
u
tio
n
,
t
h
e
p
r
o
b
ab
ilit
y
d
en
s
it
y
f
u
n
ctio
n
s
o
f
t
h
e
R
a
y
lei
g
h
d
is
tr
ib
u
tio
n
(
P
d
f
r
)
an
d
C
d
f
r
ar
e
s
tat
ed
as:
[
(
)
]
(
3
)
*
+
(
4
)
w
h
er
e
v
is
th
e
w
in
d
s
p
ee
d
(
m
/
s
)
,
c
is
th
e
s
ca
le
p
ar
a
m
e
ter
.
T
h
e
p
ar
am
eter
c
is
a
f
u
n
ctio
n
o
f
v
w
h
en
t
h
e
cu
r
v
e
r
ea
ch
es
it
s
p
ea
k
.
B
y
ta
k
i
n
g
t
h
e
d
er
iv
ati
v
e
o
f
P
d
f
r
co
n
ce
r
n
i
n
g
v
a
n
d
s
etti
n
g
it to
z
er
o
an
d
s
o
lv
in
g
(
3
)
,
th
en
v
is
o
b
tain
ed
,
n
a
m
e
l
y
;
√
(5
)
o
r
√
(
6
)
w
it
h
C
m
,
th
e
s
ca
le
p
ar
a
m
eter
o
f
th
e
R
a
y
lei
g
h
m
o
d
el
is
m
o
d
if
ied
an
d
th
e
v
a
lu
e
o
f
v
i
s
esti
m
ated
s
o
th
at
t
h
e
s
h
ap
e
o
f
t
h
e
en
tire
c
u
r
v
e
a
n
d
its
ar
ea
ca
n
b
e
d
eter
m
i
n
ed
to
v
.
T
h
e
p
r
ev
io
u
s
f
o
r
m
u
la
s
h
o
w
s
t
h
e
s
tan
d
ar
d
d
is
tr
ib
u
t
io
n
,
s
p
ec
i
f
icall
y
,
t
h
e
to
tal
ar
ea
u
n
d
er
th
e
P
d
f
cu
r
v
e
is
1
.
I
n
ac
tu
al
ap
p
licatio
n
s
,
th
e
co
n
s
tan
t
K
is
m
u
ltip
lied
b
y
(
3
)
an
d
(
4
)
,
w
h
er
e
K
i
s
th
e
to
ta
l
n
u
m
b
er
o
f
d
ef
ec
ts
o
r
th
e
to
tal
cu
m
u
lati
v
e
d
a
m
a
g
e
r
ate.
Su
b
s
ti
tu
t
in
g
th
e
v
a
lu
e
o
f
(
6
)
in
to
(
3
)
an
d
(
4
)
a
n
d
to
d
eter
m
in
e
t
h
e
m
o
d
el
o
f
a
s
et
o
f
d
ata
p
o
in
ts
,
K
an
d
v
ar
e
p
ar
a
m
eter
s
t
h
at
n
ee
d
to
b
e
esti
m
ated
,
s
o
th
at
t
h
e
P
d
f
m
an
d
C
d
f
m
f
o
r
m
s
f
o
r
th
e
p
r
o
p
o
s
ed
R
a
y
lei
g
h
m
o
d
el
ar
e;
[
(
√
)
[
(
√
)
]
]
o
r
*
*
(
)
+
+
(
7
)
[
(
(
√
)
)
]
o
r
*
(
(
)
)
+
(
8
)
T
h
e
p
r
o
p
o
s
ed
m
o
d
i
f
ied
R
a
y
le
ig
h
m
o
d
el
to
eli
m
i
n
ate
th
e
est
i
m
ated
w
in
d
s
p
ee
d
ch
ar
ac
ter
is
tic
d
ef
ec
t
s
is
s
h
o
w
n
i
n
(
7
)
an
d
(
8
)
,
w
h
er
e
in
th
e
s
t
u
d
y
t
h
e
K
v
a
lu
e
i
s
ar
o
u
n
d
1
.
1
5
.
2
.
4
.
Wind
s
peed
m
o
deli
ng
T
h
e
R
ay
leig
h
d
is
tr
ib
u
t
io
n
s
ca
le
p
ar
am
eter
is
o
b
tain
ed
u
s
i
n
g
th
e
m
a
x
i
m
u
m
l
ik
eli
h
o
o
d
esti
m
ato
r
as
ex
p
r
ess
ed
b
y
(
9
)
as;
√
∑
(
9
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
694
I
n
t J
P
o
w
E
lec
&
Dr
i
S
y
s
t
,
Vo
l.
12
,
No
.
3
,
Sep
tem
b
er
202
1
:
182
3
–
183
1
1826
w
h
er
e
C
r
is
t
h
e
R
a
y
lei
g
h
s
ca
l
e
p
ar
a
m
eter
an
d
v
i
is
th
e
w
i
n
d
s
p
ee
d
at
t
h
e
i
th
ti
m
e.
T
h
e
av
e
r
ag
e
o
f
th
e
R
a
y
le
ig
h
d
is
tr
ib
u
tio
n
f
u
n
ctio
n
is
d
eter
m
in
ed
b
y
(
1
0
)
.
√
(
1
0
)
w
h
er
e
is
o
n
th
e
av
er
ag
e
o
f
t
h
e
R
a
y
lei
g
h
d
is
tr
ib
u
tio
n
f
u
n
c
ti
o
n
.
T
h
e
w
i
n
d
s
p
ee
d
m
o
d
elin
g
d
ev
elo
p
ed
in
th
i
s
s
t
u
d
y
i
s
a
m
o
d
if
ied
R
a
y
lei
g
h
d
is
tr
ib
u
tio
n
a
n
d
is
s
ta
ted
as;
√
∑
(
1
1
)
w
h
er
e
N
is
th
e
a
m
o
u
n
t
o
f
d
ata;
v
i
is
th
e
m
ea
s
u
r
ed
(
r
ec
o
r
d
ed
)
w
i
n
d
s
p
ee
d
d
ata;
v
m
is
a
p
r
o
p
o
s
ed
w
in
d
s
p
ee
d
m
o
d
eli
n
g
.
2
.
5
.
St
a
t
is
t
ica
l a
na
ly
s
is
o
f
di
s
t
ributio
ns
Mo
d
el
s
elec
tio
n
h
as
b
ec
o
m
e
an
i
m
p
o
r
ta
n
t
f
o
cu
s
in
r
ec
e
n
t
y
ea
r
s
i
n
s
ta
tis
t
ical
lear
n
in
g
,
m
ac
h
in
e
lear
n
in
g
,
an
d
b
ig
d
ata
a
n
al
y
tics
[
3
5
]
-
[
3
7
]
.
C
u
r
r
en
tl
y
,
t
h
e
r
e
ar
e
s
ev
er
al
cr
iter
ia
in
th
e
m
o
d
el
s
elec
tio
n
liter
atu
r
e.
Ma
n
y
r
esear
ch
er
s
[
3
8
]
,
[
3
9
]
h
av
e
s
t
u
d
ied
t
h
e
p
r
o
b
lem
p
r
i
m
ar
il
y
v
ar
iab
le
r
eg
r
ess
io
n
e
lectio
n
i
n
th
r
ee
d
ec
ad
es.
T
h
e
s
tati
s
tical
s
ig
n
if
ican
ce
o
f
t
h
e
m
o
d
el
co
m
p
ar
is
o
n
ca
n
b
e
d
eter
m
i
n
ed
b
ase
d
o
n
th
e
s
u
itab
ilit
y
cr
iter
ia
in
th
e
liter
at
u
r
e
[
4
0
]
.
W
in
d
s
p
ee
d
d
ata
m
o
d
eli
n
g
f
o
r
th
e
R
a
y
lei
g
h
d
is
tr
ib
u
t
io
n
f
u
n
ctio
n
[
4
1
]
.
Dev
iatio
n
s
w
i
n
d
s
p
ee
d
d
is
tr
ib
u
tio
n
u
s
i
n
g
th
e
R
o
o
t
Me
an
Sq
u
ar
e
E
r
r
o
r
(
R
MSE
)
an
d
an
n
u
a
l
en
er
g
y
p
r
o
d
u
ctio
n
(
A
E
P
)
[
4
2
]
.
A
s
tati
s
tical
tes
t i
n
th
e
ca
s
e
o
f
th
is
s
t
u
d
y
i
s
s
h
o
w
n
i
n
T
ab
le
1
.
T
ab
le
1
.
P
r
esen
ts
a
s
tati
s
tical
t
est in
t
h
e
ca
s
e
o
f
t
h
i
s
s
t
u
d
y
N
o
.
C
r
i
t
e
r
i
a
F
o
r
mu
l
a
Ex
p
l
a
n
a
t
i
o
n
1.
R
2
∑
∑
M
e
a
su
r
e
s t
h
e
a
mo
u
n
t
o
f
v
a
r
i
a
t
i
o
n
a
c
c
o
u
n
t
e
d
f
o
r
t
h
e
f
i
t
t
e
d
mo
d
e
l
2.
R
M
S
E
√
∑
T
h
e
sq
u
a
r
e
r
o
o
t
o
f
t
h
e
me
a
su
r
e
s t
h
e
d
e
v
i
a
t
i
o
n
b
e
t
w
e
e
n
t
h
e
f
i
t
t
e
d
v
a
l
u
e
s w
i
t
h
t
h
e
a
c
t
u
a
l
d
a
t
a
o
b
se
r
v
a
t
i
o
n
3.
M
A
P
E
∑
|
|
M
A
P
E
i
s
p
e
r
c
e
n
t
a
g
e
s,
c
o
m
p
a
r
e
s t
h
e
m
b
e
t
w
e
e
n
se
t
s,
a
n
d
c
a
n
e
a
si
l
y
u
n
d
e
r
st
a
n
d
a
n
d
i
n
t
e
r
p
r
e
t
p
e
r
c
e
n
t
a
g
e
s
w
h
er
e
y
i
is
t
h
e
i
th
d
ata;
is
t
h
e
m
ea
n
d
ata
to
i
th
;
is
th
e
a
v
e
r
ag
e
d
ata
n
is
th
e
n
u
m
b
er
o
f
m
o
d
el
o
b
s
er
v
atio
n
s
;
k
i
s
th
e
e
s
ti
m
ate
d
n
u
m
b
er
w
h
er
e
A
t
ar
e
ac
tu
als
an
d
F
t
co
r
r
esp
o
n
d
in
g
f
o
r
ec
ast
s
o
r
p
r
ed
ictio
n
s
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
3
.
1
.
Ra
y
leig
h
pa
ra
m
et
er
s
a
nd
pr
o
ba
bil
it
y
dis
t
ributio
n f
u
nct
io
ns
R
a
y
le
ig
h
s
ca
le
p
ar
a
m
eter
(
C
r
)
m
ea
s
u
r
ed
w
i
n
d
s
p
ee
d
is
ca
lc
u
lated
b
ased
o
n
th
e
eq
u
atio
n
o
f
t
h
e
(
9
)
,
w
h
er
ea
s
t
h
e
m
o
d
if
ied
R
a
y
leig
h
s
ca
le
p
ar
a
m
eter
(
C
m
)
i
s
b
as
ed
o
n
(
6
)
.
T
h
e
R
a
y
leig
h
p
r
o
b
ab
ilit
y
d
is
tr
ib
u
tio
n
f
u
n
ctio
n
(
P
d
f
r
)
a
n
d
t
h
e
m
o
d
if
i
ed
R
a
y
leig
h
d
is
tr
ib
u
t
io
n
f
u
n
c
t
io
n
(
P
d
f
m
)
ar
e
s
h
o
w
n
i
n
(
3
)
an
d
(
7
)
,
r
esp
ec
tiv
el
y
.
R
a
y
le
ig
h
s
ca
le
p
ar
a
m
eter
o
f
t
h
e
w
in
d
s
p
ee
d
d
ata
i
s
r
ec
o
r
d
ed
an
d
a
m
o
d
i
f
ied
R
a
y
lei
g
h
s
ca
le
p
ar
am
e
ter
a
m
o
u
n
t
o
f
5
.
2
4
9
2
an
d
6
.
2
4
2
4
,
r
esp
ec
tiv
el
y
.
T
h
e
p
ar
a
m
eter
s
s
ca
le
f
o
r
R
a
y
lei
g
h
a
n
d
R
a
y
lei
g
h
m
o
d
if
ied
ar
e
s
h
o
w
n
in
T
ab
le
2
.
T
h
e
d
if
f
er
e
n
ce
in
m
in
i
m
u
m
,
m
a
x
i
m
u
m
,
a
n
d
av
er
ag
e
b
et
w
ee
n
R
a
y
lei
g
h
a
n
d
R
a
y
lei
g
h
p
r
o
b
ab
ilit
y
f
u
n
ctio
n
i
s
m
o
d
i
f
ied
b
y
-
0
.
0
0
9
5
,
0
.
0
2
7
7
,
an
d
0
.
0
8
4
4
,
r
esp
ec
tiv
el
y
,
a
n
d
th
e
ch
ar
ac
ter
is
ti
cs
o
f
th
e
R
a
y
lei
g
h
p
r
o
b
a
b
ilit
y
f
u
n
ctio
n
ar
e
s
h
o
wn
in
Fig
u
r
e
2
.
T
h
e
co
m
p
a
r
is
o
n
o
f
t
h
e
m
ea
n
er
r
o
r
v
alu
e
b
et
w
ee
n
th
e
m
o
d
i
f
ied
R
a
y
lei
g
h
a
n
d
R
a
y
l
eig
h
s
ca
le
p
ar
am
eter
s
is
ab
o
u
t
-
1
8
.
9
4
%
(
<0
.
0
%),
th
is
i
n
d
icate
s
t
h
at
t
h
e
p
r
o
p
o
s
ed
m
o
d
el
h
as
a
v
er
y
s
m
al
l
er
r
o
r
th
an
t
h
e
R
a
y
le
ig
h
s
ca
le
f
ac
to
r
m
o
d
el.
Fig
u
r
e
2
s
h
o
w
s
a
co
m
p
ar
is
o
n
b
et
w
ee
n
t
h
e
p
r
o
b
ab
ilit
y
f
u
n
cti
o
n
o
f
t
h
e
m
ea
s
u
r
ed
d
ata
an
d
th
e
p
r
ed
ictio
n
t
h
at
at
w
i
n
d
s
p
ee
d
s
g
r
ea
ter
th
a
n
3
m
/
s
,
th
e
m
o
d
if
ied
R
a
y
leig
h
m
o
d
el
w
ill
g
iv
e
a
b
etter
P
d
f
v
alu
e
w
h
en
co
m
p
ar
ed
to
th
e
R
a
y
lei
g
h
m
o
d
el
b
ef
o
r
e
it
w
as
m
o
d
if
ied
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
P
o
w
E
lec
&
Dr
i
S
y
s
t
I
SS
N:
2088
-
8
694
Win
d
s
p
ee
d
mo
d
elin
g
b
a
s
ed
o
n
mea
s
u
r
eme
n
t d
a
ta
to
p
r
ed
ict
fu
tu
r
e
w
in
d
s
p
ee
d
… (
S
u
w
a
r
n
o
)
1827
T
ab
le
2
.
P
ar
am
eter
s
s
ca
le
R
a
y
leig
h
(
C
r
)
an
d
m
o
d
i
f
ied
R
a
y
lei
g
h
(
C
m
)
M
o
n
t
h
C
r
C
m
Jan
u
a
r
y
5
.
0
4
5
.
9
9
F
e
b
r
u
a
r
y
5
.
1
9
6
.
1
7
M
a
r
c
h
5
.
0
2
5
.
9
7
A
p
r
i
l
5
.
1
5
6
.
1
3
M
a
y
5
.
1
7
6
.
1
5
Ju
n
e
5
.
4
5
6
.
4
9
Ju
l
y
5
.
1
3
6
.
1
0
A
u
g
u
st
5
.
3
9
6
.
4
2
S
e
p
t
e
mb
e
r
5
.
4
2
6
.
4
4
O
c
t
o
b
e
r
5
.
4
6
6
.
4
9
N
o
v
e
mb
e
r
5
.
3
4
6
.
3
6
D
e
c
e
mb
e
r
5
.
6
0
6
.
6
5
Y
e
a
r
5
.
2
5
6
.
2
4
Fig
u
r
e
2
.
T
h
e
d
if
f
er
en
ce
b
et
wee
n
th
e
t
w
o
m
o
d
el
s
o
f
R
a
y
lei
g
h
3
.
2
.
Wind
s
peed
da
t
a
re
co
rding
B
ased
o
n
th
e
r
es
u
lt
s
o
f
d
ata
r
e
co
r
d
in
g
w
it
h
P
C
E
-
FW
S
2
0
,
af
ter
p
r
o
ce
s
s
in
g
th
e
r
ec
o
r
d
in
g
d
ata
w
ith
a
d
u
r
atio
n
o
f
3
0
m
i
n
u
te
s
in
to
d
ail
y
a
n
d
m
o
n
th
l
y
d
ata,
t
h
e
r
e
s
u
lt
s
ar
e
s
h
o
w
n
i
n
F
ig
u
r
e
3
.
Fig
u
r
e
3
s
h
o
w
s
t
h
e
w
i
n
d
s
p
ee
d
f
l
u
ct
u
ates
b
et
w
ee
n
2
.
4
m
/s
to
7
.
4
m
/
s
.
T
h
e
m
i
n
i
m
u
m
,
m
a
x
i
m
u
m
an
d
av
er
a
g
e
w
i
n
d
s
p
ee
d
s
ar
e
2
.
3
7
m
/s
,
7
.
3
9
m
/
s
,
an
d
5
.
0
6
m
/s
,
r
esp
ec
ti
v
el
y
.
3
.
3
.
Wind
s
peed
da
t
a
m
o
delin
g
B
ased
o
n
(
1
1
)
,
th
e
o
b
tain
ed
r
esu
lt
s
o
f
m
o
d
eli
n
g
w
i
n
d
s
p
ee
d
ar
e
s
h
o
w
n
i
n
Fi
g
u
r
e
4
.
Fig
u
r
e
4
s
h
o
w
s
th
e
w
in
d
s
p
ee
d
f
l
u
ct
u
ates
b
et
w
ee
n
3
.
6
m
/
s
to
6
.
3
m
/s
.
T
h
e
m
i
n
i
m
u
m
,
m
a
x
i
m
u
m
a
n
d
av
er
ag
e
w
in
d
s
p
ee
d
s
ar
e
3
.
6
2
m
/s
,
6
.
3
8
m
/
s
,
an
d
5
.
2
5
m
/s
,
r
esp
ec
ti
v
el
y
.
Fig
u
r
e
3
.
W
in
d
s
p
e
ed
m
ea
s
u
r
e
Fig
u
r
e
4
.
W
in
d
s
p
ee
d
m
o
d
elin
g
3
.
4
.
Co
m
pa
ri
s
o
n o
f
w
ind
s
peed
m
o
de
lin
g
a
nd
m
ea
s
ure
m
ent
C
o
m
p
ar
is
o
n
o
f
t
h
e
w
i
n
d
s
p
ee
d
o
f
t
h
e
r
ec
o
r
d
ed
d
ata
an
d
m
o
d
elin
g
ar
e
s
h
o
w
n
i
n
Fi
g
u
r
e
5
.
Fig
u
r
e
5
s
h
o
w
s
a
co
m
p
ar
i
s
o
n
b
et
w
ee
n
t
h
e
m
ea
s
u
r
e
m
en
t
d
ata
a
n
d
m
o
d
elin
g
b
ased
o
n
a
g
r
ap
h
,
w
h
er
e
th
e
co
lo
r
„
b
lu
e
‟
o
f
th
e
m
ea
s
u
r
e
m
e
n
t
d
ata,
w
h
ile
th
e
co
lo
r
„
g
r
ee
n
‟
f
o
r
d
ata
m
o
d
elin
g
.
T
h
e
co
m
p
ar
is
o
n
o
f
th
e
t
w
o
d
ata
s
h
o
w
s
a
d
if
f
er
e
n
ce
b
et
w
ee
n
th
e
m
i
n
i
m
u
m
,
m
ax
i
m
u
m
,
an
d
av
er
ag
e
v
alu
es
o
f
0
.
5
2
5
,
0
.
1
3
6
,
an
d
0
.
0
3
7
,
r
esp
ec
tiv
el
y
.
T
h
e
m
e
a
s
u
r
ed
w
i
n
d
s
p
ee
d
an
d
th
e
m
o
d
if
ied
w
i
n
d
s
p
ee
d
m
o
d
el
ar
e
s
h
o
w
n
i
n
Fi
g
u
r
e
6
,
w
h
er
e
b
o
th
h
a
v
e
s
i
m
ilar
s
h
ap
es,
b
u
t th
e
p
r
o
p
o
s
ed
m
o
d
el
lo
o
k
s
b
etter
.
0
5
10
15
-
0
.
0
2
0
0
.
0
2
0
.
0
4
0
.
0
6
0
.
0
8
0
.
1
0
.
1
2
0
.
1
4
0
.
1
6
W
i
n
d
s
p
e
e
d
(
m
/
s
)
P
r
o
b
a
b
i
l
i
t
y
f
u
n
c
t
i
o
n
R
a
y
l
e
i
g
h
M
e
a
s
u
r
e
m
e
n
t
R
a
y
l
e
i
g
h
M
o
d
i
f
i
e
d
D
i
f
f
e
r
e
n
c
e
P
r
o
b
a
b
i
l
i
t
y
0
50
100
150
200
250
300
350
400
2
3
4
5
6
7
8
day
W
i
n
d
s
p
e
e
d
(
m
/
s
)
W
i
n
d
s
p
e
e
d
m
e
a
s
u
r
e
d
0
50
100
150
200
250
300
350
400
3
.
5
4
4
.
5
5
5
.
5
6
6
.
5
day
W
i
n
d
s
p
e
e
d
(
m
/
s
)
W
i
n
d
s
p
e
e
d
A
p
r
o
x
i
m
a
t
i
o
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
694
I
n
t J
P
o
w
E
lec
&
Dr
i
S
y
s
t
,
Vo
l.
12
,
No
.
3
,
Sep
tem
b
er
202
1
:
182
3
–
183
1
1828
Fig
u
r
e
7
s
h
o
w
s
a
co
m
p
ar
is
o
n
o
f
m
ea
s
u
r
ed
w
in
d
s
p
ee
d
d
ata
an
d
m
o
d
eli
n
g
w
it
h
m
o
d
if
ie
d
R
ay
leig
h
w
it
h
m
i
n
i
m
u
m
,
m
ax
i
m
u
m
an
d
m
ea
n
v
alu
e
s
o
f
-
1
.
0
0
5
9
,
1
.
2
4
5
4
,
an
d
0
.
0
2
3
6
,
r
esp
ec
tiv
el
y
.
Fi
g
u
r
e
7
,
co
lo
r
'
b
l
u
e
'
m
ea
s
u
r
ed
w
i
n
d
s
p
ee
d
d
ata,
th
e
co
lo
r
'
r
ed
'
is
a
p
r
e
d
ic
ted
w
in
d
s
p
ee
d
d
ata
an
d
th
e
co
lo
r
'
g
r
ee
n
'
is
th
e
d
if
f
er
e
n
ce
b
et
w
ee
n
t
h
e
m
ea
s
u
r
ed
w
i
n
d
s
p
ee
d
d
ata
w
ith
p
r
ed
icted
d
ata
.
Fig
u
r
e
5
.
C
o
m
p
ar
is
o
n
o
f
m
ea
s
u
r
e
m
e
n
t a
n
d
f
o
r
ec
ast
w
i
n
d
s
p
ee
d
(a
)
(b
)
Fig
u
r
e
6
.
Me
asu
r
ed
an
d
p
r
o
p
o
s
ed
w
i
n
d
s
p
ee
d
: (
a)
w
i
n
d
s
p
ee
d
m
ea
s
u
r
ed
,
(
b
)
w
i
n
d
s
p
ee
d
m
o
d
if
ied
Fig
u
r
e
7
.
C
o
m
p
ar
is
o
n
o
f
m
ea
s
u
r
e
m
e
n
t a
n
d
f
o
r
ec
ast
w
i
n
d
s
p
ee
d
0
50
100
150
200
250
300
350
400
2
3
4
5
6
7
8
day
W
i
n
d
s
p
e
e
d
(
m
/
s
)
W
i
n
d
s
p
e
e
d
m
e
a
s
u
r
e
d
W
i
n
d
s
p
e
e
d
A
p
r
o
x
i
m
a
t
i
o
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
P
o
w
E
lec
&
Dr
i
S
y
s
t
I
SS
N:
2088
-
8
694
Win
d
s
p
ee
d
mo
d
elin
g
b
a
s
ed
o
n
mea
s
u
r
eme
n
t d
a
ta
to
p
r
ed
ict
fu
tu
r
e
w
in
d
s
p
ee
d
… (
S
u
w
a
r
n
o
)
1829
3
.
5
.
Sta
t
is
t
ica
l t
est
re
s
ults
B
ased
o
n
th
e
r
es
u
lt
s
o
f
th
e
s
u
itab
ilit
y
test
o
f
t
h
e
m
ea
s
u
r
e
m
en
t
a
n
d
ap
p
r
o
ac
h
w
i
n
d
s
p
ee
d
d
ata
w
i
th
th
e
co
r
r
elatio
n
co
e
f
f
icien
t (
R
2
)
,
r
o
o
t m
ea
n
s
q
u
ar
e
er
r
o
r
(
R
M
SE)
,
an
d
m
ea
n
ab
s
o
lu
te
p
er
ce
n
tag
e
er
r
o
r
(
MA
P
E
)
ar
e
s
h
o
w
n
i
n
T
ab
le
3
as f
o
llo
w
s
;
T
ab
le
3
.
Statis
tic
an
al
y
s
i
s
f
o
r
m
o
n
t
h
l
y
o
f
R
2
,
R
MSE
,
a
n
d
MA
P
E
R
2
R
M
S
E
M
A
P
E
Jan
u
a
r
y
0
.
9
9
6
8
0
.
1
1
2
6
-
4
0
.
2
1
F
e
b
r
u
a
r
y
0
.
9
9
6
0
0
.
1
1
9
1
-
2
7
.
1
7
M
a
r
c
h
0
.
9
9
5
6
0
.
1
3
0
3
-
3
9
.
6
5
A
p
r
i
l
0
.
9
9
8
8
0
.
1
0
5
5
-
3
1
.
1
1
M
a
y
0
.
9
9
7
1
0
.
1
0
7
3
-
2
7
.
9
8
Ju
n
e
0
.
9
9
7
6
0
.
0
9
8
6
-
2
.
6
8
6
Ju
l
y
0
.
9
9
8
5
0
.
0
9
0
2
-
3
4
.
3
3
A
u
g
u
st
0
.
9
9
9
4
0
.
0
9
5
0
-
9
.
6
6
4
S
e
p
t
e
mb
e
r
0
.
9
9
7
9
0
.
0
7
9
3
-
7
.
4
3
9
O
c
t
o
b
e
r
0
.
9
9
8
9
0
.
0
9
3
8
-
1
.
5
7
1
N
o
v
e
mb
e
r
0
.
9
9
6
9
0
.
0
9
2
1
-
1
3
.
8
0
D
e
c
e
mb
e
r
0
.
9
9
8
7
0
.
0
9
4
3
1
0
.
5
8
3
A
v
e
r
a
g
e
0
.
9
1
4
5
0
.
1
0
1
5
-
1
8
.
7
5
2
8
T
ab
le
3
,
s
h
o
w
s
t
h
at
t
h
e
co
r
r
elatio
n
co
ef
f
icie
n
t
te
s
t
(
R
2
)
e
v
er
y
m
o
n
t
h
i
s
b
et
w
ee
n
0
.
9
9
5
6
-
0
.
9
9
9
4
w
it
h
an
av
er
a
g
e
o
f
0
.
9
1
4
5
,
th
is
r
esu
lt
g
i
v
es
a
g
o
o
d
m
ea
n
in
g
b
ec
a
u
s
e
i
t
is
c
lo
s
e
to
1
.
W
h
ile
t
h
e
m
o
n
t
h
l
y
R
MSE
te
s
t
is
b
et
w
ee
n
0
.
0
7
9
3
-
0
.
1
3
0
3
an
d
w
ith
an
a
v
er
ag
e
o
f
0
.
1
0
1
5
,
th
is
r
e
s
u
l
t
g
iv
e
s
a
g
o
o
d
m
ea
n
i
n
g
b
ec
au
s
e
c
lo
s
e
to
ze
r
o
.
W
h
ile
th
e
M
A
P
E
test
ev
er
y
m
o
n
t
h
is
b
et
w
ee
n
-
4
0
.
2
1
-
1
0
.
5
8
3
,
w
ith
a
n
av
er
ag
e
o
f
-
1
8
.
7
5
2
8
,
th
is
r
esu
lt
g
iv
e
s
a
v
er
y
g
o
o
d
m
ea
n
in
g
b
ec
au
s
e
<1
0
%.
4.
CO
NCLU
SI
O
N
T
h
e
p
r
o
p
o
s
ed
w
i
n
d
s
p
ee
d
m
o
d
elin
g
h
a
s
f
u
l
f
il
led
th
e
s
tati
s
t
ical
test
r
eq
u
ir
e
m
e
n
ts
,
ac
co
r
d
in
g
to
t
h
e
co
r
r
elatio
n
co
ef
f
icien
t
(
R
2
)
,
R
MSE
a
n
d
,
M
A
P
E
.
T
h
e
test
r
esu
lt
d
ata
b
y
m
o
n
th
l
y
s
tati
s
tics
a
n
d
a
v
er
ag
e
s
in
d
icate
t
h
at
t
h
e
m
o
d
eli
n
g
ap
p
r
o
ac
h
co
r
r
elatio
n
co
ef
f
icie
n
t
(
R
2
)
o
f
0
.
9
1
4
5
,
th
e
test
r
es
u
lts
w
it
h
R
MSE
o
f
0
.
1
0
1
5
,
an
d
test
r
esu
lt
s
w
it
h
MA
P
E
o
f
-
1
8
.
7
5
2
8
.
T
h
e
r
esu
lts
o
f
t
h
e
t
h
r
ee
test
s
in
d
icate
th
at
t
h
e
p
r
o
p
o
s
ed
m
o
d
el
i
s
w
ell
r
ec
eiv
ed
.
ACK
NO
WL
E
D
G
E
M
E
NT
S
T
h
e
au
th
o
r
s
th
a
n
k
t
h
e
c
h
ie
f
ed
ito
r
an
d
h
i
s
s
taf
f
,
a
s
w
el
l
as
t
h
e
E
x
ec
u
ti
v
e
B
o
ar
d
Mu
h
a
m
m
ad
i
y
a
h
Un
i
v
er
s
it
y
o
f
No
r
th
Su
m
atr
a
w
h
ich
h
a
s
p
r
o
v
id
ed
th
e
o
p
p
o
r
tu
n
it
y
to
p
u
b
lis
h
th
e
r
es
u
lts
o
f
th
o
u
g
h
t
an
d
r
esear
ch
,
w
h
ic
h
m
a
y
b
e
u
s
ef
u
l
f
o
r
ev
er
y
t
h
i
n
g
.
RE
F
E
R
E
NC
E
S
[1
]
W
.
W
e
ib
u
ll
,
“
A
sta
ti
stica
l
d
istri
b
u
ti
o
n
f
u
n
c
ti
o
n
o
f
w
id
e
a
p
p
li
c
a
b
il
it
y
,
”
J
.
Ap
p
l.
M
e
c
h
.
Ap
p
l.
M
e
c
h
.
,
v
o
l.
1
8
,
n
o
3
,
p
p
.
2
9
3
–
2
9
7
,
1
9
5
1
,
d
o
i:
1
0
.
1
1
1
5
/
1
.
4
0
1
0
3
3
7
.
[2
]
W
.
W
e
ib
u
ll
,
T
h
e
p
h
e
n
o
me
n
o
n
o
f
ru
p
tu
re
i
n
so
li
d
s
,
A
n
g
e
n
io
rs V
e
te
n
sk
a
p
s
Ak
a
d
e
m
ien
Ha
n
d
li
n
g
a
r,
1
9
3
9
.
[3
]
S
u
w
a
rn
o
,
I.
Y
u
su
f
,
M
.
Irw
a
n
to
,
a
n
d
A
.
Hie
n
d
r
o
,
“
A
n
a
ly
sis
o
f
w
in
d
sp
e
e
d
c
h
a
ra
c
teristics
u
sin
g
d
if
fe
r
e
n
t
d
istri
b
u
ti
o
n
m
o
d
e
ls
in
M
e
d
a
n
Cit
y
,
In
d
o
n
e
sia
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
Po
we
r
El
e
c
tro
n
ics
a
n
d
Dr
ive
S
y
ste
ms
(
IJ
PE
DS
)
,
v
o
l.
1
2
,
n
o
.
2
,
p
p
.
1
1
0
2
-
1
1
1
3
,
2
0
2
1
,
d
o
i:
1
0
.
1
1
5
9
1
/
ij
p
e
d
s
.
v
1
2
.
i
2
.
p
p
1
1
0
2
-
1
1
1
3
.
[4
]
Y.
M
.
Ka
n
tar,
İ.
Us
ta,
İ.
Ye
n
il
m
e
z
,
a
n
d
İ.
A
ri
k
,
“
A
S
tu
d
y
o
n
Esti
m
a
ti
o
n
o
f
W
in
d
S
p
e
e
d
Distrib
u
ti
o
n
b
y
Us
in
g
th
e
M
o
d
if
ied
W
e
ib
u
ll
Distrib
u
ti
o
n
,
”
BİL
İŞ
İM
T
e
k
n
o
l.
DERGİ
S
İ
,
v
o
l
.
9
,
n
o
.
2
,
p
p
.
6
3
-
7
0
,
2
0
1
6
.
[5
]
H.
Bid
a
o
u
i,
I.
El
A
b
b
a
si,
A
.
El
B
o
u
a
rd
i
,
a
n
d
A
.
Da
rc
h
e
rif
,
“
W
in
d
sp
e
e
d
d
a
ta
a
n
a
ly
sis
u
sin
g
W
e
ib
u
l
l
a
n
d
Ra
y
lei
g
h
d
istri
b
u
ti
o
n
f
u
n
c
ti
o
n
s,
c
a
se
stu
d
y
:
F
iv
e
c
it
ies
n
o
rth
e
rn
m
o
ro
c
c
o
,
”
Pro
c
e
d
ia
M
a
n
u
f
a
c
tu
rin
g
,
v
o
l.
3
2
,
p
p
.
7
8
6
-
7
9
3
,
2
0
1
9
,
d
o
i
:
1
0
.
1
0
1
6
/j
.
p
ro
m
fg
.
2
0
1
9
.
0
2
.
2
8
6
.
[6
]
G
.
D.
Na
g
e
,
“
A
n
a
l
y
sis
o
f
w
in
d
sp
e
e
d
d
istri
b
u
ti
o
n
:
C
o
m
p
a
ra
ti
v
e
st
u
d
y
o
f
Weib
u
ll
t
o
Ra
y
leig
h
p
ro
b
a
b
il
it
y
d
e
n
sity
f
u
n
c
ti
o
n
;
A
c
a
se
o
f
t
w
o
sites
in
E
th
io
p
ia,
”
Ame
ric
a
n
J
o
u
r
n
a
l
o
f
M
o
d
e
rn
En
e
rg
y
,
v
o
l.
2
,
n
o
.
3
,
p
p
.
1
0
–
1
6
,
2
0
1
6
,
d
o
i
:
1
0
.
1
1
6
4
8
/j
.
a
jm
e
.
2
0
1
6
0
2
0
3
.
1
1
.
[7
]
D.
M
a
z
z
e
o
,
G
.
Oliv
e
ti
,
a
n
d
E.
L
a
b
o
n
ia,
“
Esti
m
a
ti
o
n
o
f
w
in
d
sp
e
e
d
p
ro
b
a
b
il
it
y
d
e
n
sity
f
u
n
c
ti
o
n
u
si
n
g
a
m
ix
tu
re
o
f
tw
o
tru
n
c
a
ted
n
o
rm
a
l
d
istri
b
u
ti
o
n
s,
”
Ren
e
wa
b
le
En
e
rg
y
,
v
o
l.
1
1
5
,
p
p
.
1
2
6
0
-
1
2
8
0
,
2
0
1
8
,
d
o
i
:
1
0
.
1
0
1
6
/
j.
re
n
e
n
e
.
2
0
1
7
.
0
9
.
0
4
3
.
[8
]
N
.
A
.
S
a
tw
i
k
a
,
R.
Ha
n
to
ro
,
E.
S
e
p
ty
a
n
in
g
ru
m
,
a
n
d
A
.
W
.
M
a
h
m
a
sh
a
n
i,
“
A
n
a
l
y
sis
o
f
w
in
d
e
n
e
rg
y
p
o
ten
ti
a
l
a
n
d
w
in
d
e
n
e
rg
y
d
e
v
e
lo
p
m
e
n
t
to
e
v
a
lu
a
te
p
e
rf
o
rm
a
n
c
e
o
f
w
in
d
tu
rb
in
e
in
sta
ll
a
ti
o
n
i
n
Ba
li
,
In
d
o
n
e
sia
,
”
J
o
u
rn
a
l
o
f
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
694
I
n
t J
P
o
w
E
lec
&
Dr
i
S
y
s
t
,
Vo
l.
12
,
No
.
3
,
Sep
tem
b
er
202
1
:
182
3
–
183
1
1830
M
e
c
h
a
n
ica
l
E
n
g
in
e
e
rin
g
a
n
d
S
c
ien
c
e
s
(
J
M
ES
).
,
v
o
l.
1
3
,
n
o
.
1
,
p
p
.
4
4
6
1
–
4
4
7
6
,
2
0
1
9
,
d
o
i
:
1
0
.
1
5
2
8
2
/j
m
e
s.1
3
.
1
.
2
0
1
9
.
0
9
.
0
3
7
9
.
[9
]
V
.
Ka
ti
n
a
s,
M
M
a
rc
iu
sk
a
it
is,
G
G
e
c
e
v
iciu
s
,
a
n
d
A
M
a
rk
e
v
iciu
s,
“
S
tatisti
c
a
l
a
n
a
l
y
sis
o
f
w
in
d
c
h
a
ra
c
teristics
b
a
se
d
o
n
W
e
ib
u
ll
m
e
th
o
d
s
f
o
r
e
stim
a
ti
o
n
o
f
p
o
w
e
r
g
e
n
e
ra
ti
o
n
i
n
L
it
u
a
n
ia,
”
Ren
e
wa
b
le
E
n
e
rg
y
,
v
o
l.
1
1
3
,
p
p
.
1
9
0
-
2
0
1
,
2
0
1
7
,
d
o
i
:
1
0
.
1
0
1
6
/j
.
re
n
e
n
e
.
2
0
1
7
.
0
5
.
0
7
1
.
[1
0]
A
.
K.
Az
a
d
,
M
.
G
.
Ra
su
l,
R.
Isla
m
,
a
n
d
R
S
.
Im
ru
l,
“
A
n
a
l
y
sis
o
f
w
in
d
e
n
e
rg
y
p
ro
sp
e
c
t
f
o
r
p
o
w
e
r
g
e
n
e
ra
ti
o
n
b
y
th
re
e
W
e
ib
u
ll
d
istri
b
u
ti
o
n
m
e
th
o
d
s,
”
En
e
rg
y
Pro
c
e
d
ia
,
v
o
l
7
5
,
p
p
.
7
2
2
-
7
2
7
,
2
0
1
5
,
d
o
i
:
1
0
.
1
0
1
6
/
j.
e
g
y
p
ro
.
2
0
1
5
.
0
7
.
4
9
9
.
[1
1
]
W
.
Wera
p
u
n
,
Y
.
T
ira
w
a
n
i
c
h
a
k
u
l
,
a
n
d
J
.
W
a
e
w
sa
k
,
“
Co
m
p
a
ra
ti
v
e
stu
d
y
o
f
f
i
v
e
m
e
th
o
d
s
to
e
s
ti
m
a
te
Weib
u
ll
p
a
ra
m
e
ters
f
o
r
w
in
d
sp
e
e
d
o
n
P
h
a
n
g
a
n
Isla
n
d
,
T
h
a
il
a
n
d
,
”
En
e
rg
y
Pro
c
e
d
ia
,
v
o
l
.
7
9
,
p
p
.
9
7
6
-
9
8
1
,
2
0
1
5
,
d
o
i
:
1
0
.
1
0
1
6
/
j.
e
g
y
p
ro
.
2
0
1
5
.
1
1
.
5
9
6
.
[1
2
]
I.
T
iz
g
u
i,
F
.
El
G
u
e
z
a
r,
H.
Bo
u
z
a
h
ir
,
a
n
d
B.
Be
n
a
id
,
“
W
in
d
sp
e
e
d
d
istri
b
u
ti
o
n
m
o
d
e
li
n
g
f
o
r
w
in
d
p
o
w
e
r
e
sti
m
a
ti
o
n
:
Ca
se
o
f
Ag
a
d
ir
in
M
o
r
o
c
c
o
,
”
W
in
d
E
n
g
i
n
e
e
rin
g
,
v
o
l
.
4
3
,
n
o
.
2
,
p
p
.
1
9
0
-
2
0
0
,
2
0
1
8
,
d
o
i:
1
0
.
1
1
7
7
/
0
3
0
9
5
2
4
X
1
8
7
8
0
3
9
1
.
[1
3
]
I.
P
o
b
o
č
ík
o
v
á
.
Z.
S
e
d
li
a
č
k
o
v
á
,
a
n
d
M
.
M
ich
a
lk
o
v
á
,
“
A
p
p
li
c
a
ti
o
n
o
f
f
o
u
r
p
ro
b
a
b
il
it
y
d
istri
b
u
ti
o
n
s
f
o
r
w
in
d
sp
e
e
d
m
o
d
e
li
n
g
,
”
Pro
c
e
d
ia
E
n
g
in
e
e
rin
g
,
v
o
l.
1
9
2
,
p
p
.
7
1
3
-
7
1
8
,
2
0
1
7
,
d
o
i
:
1
0
.
1
0
1
6
/j
.
p
ro
e
n
g
.
2
0
1
7
.
0
6
.
1
2
3
.
[1
4
]
S
u
w
a
rn
o
,
L
.
J
.
H
w
a
i
,
M
.
F
.
Za
m
b
a
k
,
I
.
Nisja
,
a
n
d
Ro
h
a
n
a
,
“
A
s
se
ss
m
e
n
t
o
f
w
in
d
e
n
e
rg
y
p
o
ten
ti
a
l
u
sin
g
we
ib
u
ll
d
istri
b
u
ti
o
n
f
u
n
c
ti
o
n
a
s
w
in
d
p
o
w
e
r
p
lan
t
in
M
e
d
a
n
,
No
rt
h
S
u
m
a
tra,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
S
im
u
l
a
ti
o
n
:
S
y
ste
ms
,
S
c
ien
c
e
&
T
e
c
h
n
o
lo
g
y
,
v
o
l.
1
7
,
n
o
.
4
1
,
p
p
.
2
4
.
1
-
2
4
.
5
,
2
0
1
7
,
d
o
i
1
0
.
5
0
1
3
/i
jsss
t.
a
.
1
7
.
4
1
.
2
4
.
[1
5
]
M
.
N.
K
h
o
sh
r
o
d
i,
M
.
Ja
n
n
a
ti
,
an
d
T
.
S
u
ti
k
n
o
,
“
A
Re
v
ie
w
o
f
W
in
d
S
p
e
e
d
Esti
m
a
ti
o
n
f
o
r
W
in
d
T
u
rb
in
e
S
y
ste
m
s
Ba
se
d
o
n
Ka
lm
a
n
F
il
ter
T
e
c
h
n
iq
u
e
,
”
In
t.
J
.
El
e
c
tr.
C
o
mp
u
t.
E
n
g
.
,
v
o
l.
6
,
n
o
.
4
,
p
p
.
1
4
0
6
-
1
4
1
1
,
2
0
1
6
,
d
o
i
:
1
0
.
1
1
5
9
1
/i
jec
e
.
v
6
i4
.
p
p
1
4
0
6
-
1
4
1
1
.
[1
6
]
Z.
L
iu
,
P
.
Jia
n
g
,
L
.
Zh
a
n
g
,
a
n
d
X
.
Ni
u
,
“
A
c
o
m
b
in
e
d
f
o
re
c
a
stin
g
m
o
d
e
l
f
o
r
ti
m
e
se
ri
e
s:
A
p
p
li
c
a
ti
o
n
t
o
sh
o
rt
-
term
w
in
d
sp
e
e
d
f
o
re
c
a
stin
g
,
”
Ap
p
li
e
d
En
e
rg
y
,
v
o
l.
2
5
9
,
p
p
.
1
-
2
5
,
2
0
2
0
,
d
o
i:
1
0
.
1
0
1
6
/j
.
a
p
e
n
e
rg
y
.
2
0
1
9
.
1
1
4
1
3
7
.
[1
7
]
S
.
S
.
Ku
tt
y
,
M
.
G
.
M
.
Kh
a
n
,
a
n
d
M
.
R.
A
h
m
e
d
,
“
Esti
m
a
ti
o
n
o
f
d
iffere
n
t
w
in
d
c
h
a
ra
c
teristics
p
a
ra
m
e
ters
a
n
d
a
c
c
u
ra
te
w
in
d
re
so
u
rc
e
a
ss
e
s
s
m
e
n
t
f
o
r
Ka
d
a
v
u
,
F
ij
i,
”
AIM
S
En
e
rg
y
,
v
o
l.
7
,
n
o
.
6
,
p
p
.
7
6
0
-
7
9
1
,
2
0
1
9
,
d
o
i
:
1
0
.
3
9
3
4
/e
n
e
rg
y
.
2
0
1
9
.
6
.
7
6
0
.
[1
8
]
A
.
G
.
A
b
o
-
Kh
a
li
l,
S
.
A
l
y
a
m
i,
K.
S
a
y
e
d
,
a
n
d
A
.
A
lh
e
jj
i,
“
D
y
n
a
m
i
c
m
o
d
e
li
n
g
o
f
w
in
d
tu
rb
i
n
e
s
b
a
se
d
o
n
e
stim
a
ted
w
in
d
sp
e
e
d
u
n
d
e
r
t
u
rb
u
len
t
c
o
n
d
i
ti
o
n
s,
”
En
e
rg
ies
,
v
o
l
.
1
2
,
n
o
.
1
0
,
p
p
.
1
-
2
5
,
2
0
1
9
,
d
o
i:
1
0
.
3
3
9
0
/en
1
2
1
0
1
9
0
7
.
[1
9
]
S
M
.
L
a
w
a
n
,
W
.
A
.
W
.
Z.
A
b
id
in
,
a
n
d
T
.
M
a
sri,
“
Im
p
le
m
e
n
tatio
n
o
f
a
t
o
p
o
g
ra
p
h
ic
a
rti
f
icia
l
n
e
u
r
a
l
n
e
tw
o
rk
w
in
d
sp
e
e
d
p
re
d
icti
o
n
m
o
d
e
l
f
o
r
a
ss
e
ss
in
g
o
n
sh
o
re
w
in
d
p
o
w
e
r
p
o
ten
ti
a
l
in
S
i
b
u
,
S
a
ra
w
a
k
,
”
T
h
e
Eg
y
p
t
ia
n
J
o
u
rn
a
l
o
f
Rem
o
te S
e
n
si
n
g
a
n
d
S
p
a
c
e
S
c
ien
c
e
,
v
o
l.
2
3
,
p
p
.
2
1
-
3
4
,
2
0
2
0
,
d
o
i:
1
0
.
1
0
1
6
/j
.
e
jrs.
2
0
1
9
.
0
8
.
0
0
3
.
[2
0
]
Z.
L
iu
,
D.
W
u
,
Y.
L
iu
,
Z.
Ha
n
,
L
.
L
u
n
,
J.
Ga
o
,
G
.
Jin
,
a
n
d
G
.
C
a
o
,
“
Ac
c
u
ra
c
y
a
n
a
l
y
se
s
a
n
d
m
o
d
e
l
c
o
m
p
a
riso
n
o
f
m
a
c
h
in
e
lea
rn
in
g
a
d
o
p
ted
i
n
b
u
i
ld
in
g
e
n
e
rg
y
c
o
n
su
m
p
ti
o
n
p
re
d
ic
ti
o
n
,
”
En
e
rg
y
Exp
l
o
ra
t
io
n
&
Ex
p
lo
it
a
ti
o
n
,
,
v
o
l
.
3
7
,
n
o
.
4
,
p
p
.
1
4
2
6
-
1
4
5
1
,
2
0
1
9
,
d
o
i:
1
0
.
1
1
7
7
/
0
1
4
4
5
9
8
7
1
8
8
2
2
4
0
0
.
[2
1
]
A
.
A
.
K
a
d
h
e
m
,
N.
I.
A
.
W
a
h
a
b
,
I
.
A
ris,
J.
J
a
sn
i
,
a
n
d
A
.
A
b
d
a
ll
a
,
“
A
d
v
a
n
c
e
d
w
in
d
sp
e
e
d
p
re
d
ictio
n
m
o
d
e
l
b
a
se
d
o
n
a
c
o
m
b
in
a
ti
o
n
o
f
W
e
ib
u
ll
d
istri
b
u
ti
o
n
a
n
d
a
n
a
rti
f
icia
l
n
e
u
ra
l
n
e
t
w
o
rk
,
”
En
e
rg
ies
,
v
o
l.
1
0
,
n
o
.
1
1
,
p
p
.
1
7
4
4
-
,
2
0
1
7
,
d
o
i:
1
0
.
3
3
9
0
/en
1
0
1
1
1
7
4
4
.
[2
2
]
K.
M
e
th
a
p
ra
y
o
o
n
,
C.
Y
in
g
v
iv
a
ta
n
a
p
o
n
g
,
W
.
Lee
,
a
n
d
J.
R.
L
iao
,
“
A
n
in
teg
ra
ti
o
n
o
f
A
NN
w
in
d
p
o
w
e
r
e
sti
m
a
ti
o
n
in
to
u
n
it
c
o
m
m
it
m
e
n
t
c
o
n
sid
e
rin
g
th
e
f
o
re
c
a
stin
g
u
n
c
e
rtain
t
y
,
”
I
EE
E
T
ra
n
s
a
c
ti
o
n
s
o
n
In
d
u
stry
A
p
p
li
c
a
ti
o
n
s
,
v
o
l.
4
3
,
n
o
.
6
,
p
p
.
1
4
4
1
-
1
4
4
8
,
No
v
.
-
d
e
c
.
2
0
0
7
,
d
o
i:
1
0
.
1
1
0
9
/T
IA
.
2
0
0
7
.
9
0
8
2
0
3
.
[2
3
]
B.
G
.
Bro
w
n
,
R.
W
.
Ka
t
z
,
a
n
d
A
H
M
u
rp
h
y
,
“
T
i
m
e
se
rie
s
m
o
d
e
ls
to
si
m
u
late
a
n
d
f
o
re
c
a
st
w
in
d
sp
e
e
d
a
n
d
w
in
d
p
o
w
e
r,
”
J
o
u
rn
a
l
o
f
A
p
p
l
ied
M
e
teo
ro
lo
g
y
a
n
d
Cl
ima
t
o
lo
g
y
,
v
o
l.
2
3
,
n
o
.
8
,
p
p
.
1
1
8
4
-
1
1
9
5
,
1
9
8
4
,
d
o
i:
1
0
.
1
1
7
5
/
1
5
2
0
-
0
4
5
0
(
1
9
8
4
)
0
2
3
<
1
1
8
4
:T
S
M
T
S
A
>
2
.
0
.
CO;
2
.
[2
4
]
O.
Kisi,
S
.
He
d
d
a
m
,
a
n
d
Z.
M
.
Ya
se
e
n
,
“
T
h
e
i
m
p
le
m
e
n
tatio
n
o
f
u
n
iv
a
riab
le
sc
h
e
m
e
-
b
a
se
d
a
ir
te
m
p
e
ra
tu
re
f
o
r
so
lar
ra
d
iatio
n
p
re
d
icti
o
n
:
Ne
w
d
e
v
e
l
o
p
m
e
n
t
o
f
d
y
n
a
m
ic
e
v
o
lv
in
g
n
e
u
ra
l
-
f
u
z
z
y
in
f
e
re
n
c
e
s
y
ste
m
m
o
d
e
l,
”
Ap
p
li
e
d
En
e
rg
y
,
v
o
l.
2
4
1
,
p
p
.
1
8
4
–
1
9
5
,
2
0
1
9
,
d
o
i:
1
0
.
1
0
1
6
/j
.
a
p
e
n
e
rg
y
.
2
0
1
9
.
0
3
.
0
8
9
.
[2
5
]
A
.
L
a
u
,
a
n
d
P
.
M
c
sh
a
rry
,
“
A
p
p
ro
a
c
h
e
s
f
o
r
m
u
lt
i
-
ste
p
d
e
n
sity
f
o
re
c
a
sts
w
it
h
a
p
p
li
c
a
ti
o
n
to
a
g
g
re
g
a
te
d
w
in
d
p
o
w
e
r,
”
T
h
e
An
n
a
ls
o
f
Ap
p
li
e
d
S
t
a
ti
stics
,
v
o
l.
4
,
p
p
.
1
3
1
1
-
1
3
4
1
,
2
0
1
0
,
d
o
i:
1
0
.
1
2
1
4
/
09
-
A
OA
S
3
2
0
.
[2
6
]
H.
M
.
G
h
a
d
ik
o
lae
i,
A
.
A
h
m
a
d
i,
J.
A
g
h
a
e
i
,
a
n
d
M
.
Na
jaf
i,
“
Risk
c
o
n
train
e
d
se
lf
-
sc
h
e
d
u
li
n
g
o
f
h
u
d
ro
/w
in
d
u
n
it
s
f
o
r
sh
o
rt
term
e
lec
tri
c
it
y
m
a
r
k
e
ts
c
o
n
sid
e
ri
n
g
in
term
it
ten
c
y
a
n
d
u
n
c
e
rtain
ty
,
”
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
6
,
p
p
.
4
7
3
4
-
4
7
4
3
,
2
0
1
2
,
d
o
i:
1
0
.
1
0
1
6
/
j.
rse
r.
2
0
1
2
.
0
4
.
0
1
9
.
[2
7
]
V
.
Ş
.
Ed
ig
e
r
,
a
n
d
S
.
A
k
a
r,
“
A
R
IM
A
f
o
re
c
a
stin
g
o
f
p
ri
m
a
r
y
e
n
e
rg
y
d
e
m
a
n
d
b
y
f
u
e
l
in
T
u
rk
e
y
,
”
En
e
rg
y
Po
l
icy
,
v
o
l.
2
5
,
p
p
.
6
6
7
-
6
7
6
,
2
0
0
7
,
d
o
i:
1
0
.
1
0
1
6
/j
.
e
n
p
o
l.
2
0
0
6
.
0
5
.
0
0
9
.
[2
8
]
P
.
Ch
e
n
,
T
.
P
e
d
e
rse
n
,
B
.
Ba
k
-
Je
n
se
n
,
a
n
d
Z.
Ch
e
n
,
“
A
RIM
A
-
b
a
se
d
ti
m
e
se
ries
m
o
d
e
l
o
f
sto
c
h
a
stic
w
in
d
p
o
w
e
r
g
e
n
e
ra
ti
o
n
,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
on
Po
we
r
S
y
ste
ms
,
v
o
l.
2
5
,
n
o
.
2
,
p
p
.
6
6
7
-
6
7
6
,
M
a
y
2
0
1
0
,
d
o
i
:
1
0
.
1
1
0
9
/T
P
W
RS
.
2
0
0
9
.
2
0
3
3
2
7
7
.
[2
9
]
C.
L
.
A
n
d
e
rso
n
,
a
n
d
J.
B.
Ca
rd
e
ll
,
“
Re
d
u
c
in
g
t
h
e
v
a
riab
il
it
y
o
f
w
in
d
p
o
w
e
r
g
e
n
e
ra
ti
o
n
f
o
r
p
a
rt
icip
a
ti
o
n
in
d
a
y
a
h
e
a
d
e
lec
tri
c
it
y
m
a
r
k
e
ts,
”
in
Pr
o
c
e
e
d
in
g
s o
f
th
e
4
1
st A
n
n
u
a
l
Ha
wa
ii
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
S
y
ste
m S
c
ien
c
e
s
(
HICS
S
2
0
0
8
)
,
2
0
0
8
,
p
p
.
1
7
8
-
1
7
8
,
d
o
i:
1
0
.
1
1
0
9
/HIC
S
S
.
2
0
0
8
.
3
6
8
.
[3
0
]
Y.
A
Iria
rte,
H.
W
.
G
o
m
e
z
,
H.
V
a
re
la,
a
n
d
H
.
Bo
lf
a
rin
e
,
“
S
las
h
e
d
Ra
y
leig
h
d
istri
b
u
ti
o
n
,
”
Rev
ista
Co
lo
mb
ia
n
a
d
e
Esta
d
ísti
c
a
,
v
o
l
.
3
8
,
n
o
.
1
,
p
p
.
3
1
-
4
4
,
2
0
1
5
,
d
o
i:
1
0
.
1
5
4
4
6
/rce
.
v
3
8
n
1
.
4
8
8
0
0
.
[3
1
]
R.
A
.
R.
Ba
n
tan
e
t
a
l.
,
“
S
o
m
e
n
e
w
fa
c
ts
a
b
o
u
t
th
e
u
n
it
-
ra
y
leig
h
d
istri
b
u
ti
o
n
w
it
h
a
p
p
li
c
a
ti
o
n
s,
”
M
a
t
h
e
ma
ti
c
s
,
v
o
l.
8
,
n
o
.
1
1
,
p
p
.
1
-
2
3
,
2
0
2
0
,
d
o
i:
1
0
.
3
3
9
0
/m
a
th
8
1
1
1
9
5
4
.
[3
2
]
M
.
Ka
c
h
n
ia
,
a
n
d
R
.
S
z
e
w
c
z
y
k
,
“
S
tu
d
y
o
n
th
e
Ra
y
leig
h
h
y
st
e
re
sis
m
o
d
e
l
a
n
d
it
s
a
p
p
li
c
a
b
i
li
ty
in
m
o
d
e
li
n
g
m
a
g
n
e
ti
c
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
P
o
w
E
lec
&
Dr
i
S
y
s
t
I
SS
N:
2088
-
8
694
Win
d
s
p
ee
d
mo
d
elin
g
b
a
s
ed
o
n
mea
s
u
r
eme
n
t d
a
ta
to
p
r
ed
ict
fu
tu
r
e
w
in
d
s
p
ee
d
… (
S
u
w
a
r
n
o
)
1831
h
y
ste
r
e
sis p
h
e
n
o
m
e
n
o
n
i
n
f
e
rro
m
a
g
n
e
ti
c
m
a
te
rials,
”
Acta
Ph
y
sic
a
Po
lo
n
ica
A
,
v
o
l.
1
3
1
,
n
o
.
5
,
p
p
.
1
2
4
4
-
1
2
4
9
,
2
0
1
7
,
d
o
i:
1
0
.
1
2
6
9
3
/A
P
h
y
sP
o
lA
.
1
3
1
.
1
2
4
4
.
[3
3
]
Y.
M
.
G
o
m
e
z
,
D.
I.
G
a
ll
a
rd
o
,
Y.
Iriarte
,
a
n
d
H.
B
f
a
rli
n
e
,
“
T
h
e
Ra
y
leig
h
–
L
in
d
ley
m
o
d
e
l:
p
ro
p
e
rti
e
s
a
n
d
a
p
p
li
c
a
ti
o
n
s,
”
J
o
u
rn
a
l
o
f
Ap
p
li
e
d
S
t
a
ti
stics
,
v
o
l.
4
6
,
n
o
.
1
,
p
p
.
1
4
1
-
1
6
3
,
2
0
1
9
,
d
o
i:
1
0
.
1
0
8
0
/
0
2
6
6
4
7
6
3
.
2
0
1
8
.
1
4
5
8
8
2
5
.
[3
4
]
R
.
S
.
R.
G
o
rla,
M
.
K.
P
a
ll
ik
o
n
d
a
,
a
n
d
G
.
W
a
lu
n
j,
“
Us
e
o
f
Ra
y
l
e
ig
h
d
istri
b
u
ti
o
n
m
e
th
o
d
f
o
r
a
ss
e
ss
m
e
n
t
o
f
w
in
d
e
n
e
rg
y
o
u
tp
u
t
in
Clev
e
lan
d
–
Oh
i
o
,
”
Ren
e
wa
b
le
En
e
rg
y
Res
e
a
rc
h
a
n
d
Ap
p
li
c
a
t
io
n
,
v
o
l.
1
,
n
o
.
1
,
p
p
.
1
1
-
1
8
,
2
0
2
0
,
d
o
i:
1
0
.
2
2
0
4
4
/R
ERA
.
2
0
1
9
.
1
6
0
1
.
[3
5
]
K.
P
.
Bu
r
n
h
a
m
,
a
n
d
D.
R.
A
n
d
e
r
so
n
,
M
o
d
e
l
se
lec
ti
o
n
a
n
d
mu
lt
imo
d
e
l
i
n
fer
e
n
c
e
:
A
p
ra
c
ti
c
a
l
in
fo
r
ma
ti
o
n
th
e
o
re
ti
c
a
p
p
r
o
a
c
h
,
2
n
d
e
d
it
i
o
n
,
S
p
rin
g
e
r,
Be
rli
n
,
G
e
r
m
a
n
y
,
2
0
0
2
.
[3
6
]
K
.
P
.
Bu
r
n
h
a
m
,
D
.
R.
A
n
d
e
rso
n
,
a
n
d
K.
P
.
Hu
y
v
a
e
rt,
“
A
IC
m
o
d
e
l
se
lec
ti
o
n
a
n
d
m
u
lt
im
o
d
e
l
in
f
e
re
n
c
e
in
b
e
h
a
v
io
ra
l
e
c
o
lo
g
y
:
S
o
m
e
b
a
c
k
g
ro
u
n
d
,
o
b
se
rv
a
ti
o
n
s,
a
n
d
c
o
m
p
a
ra
ti
o
n
s,
”
Beh
a
v
io
ra
l
Eco
lo
g
y
a
n
d
S
o
c
io
b
i
o
lo
g
y
,
v
o
l.
6
5
,
p
p
.
23
-
3
5
,
2
0
1
1
,
d
o
i:
1
0
.
1
0
0
7
/s0
0
2
6
5
-
0
1
0
-
1
0
2
9
-
6.
[3
7
]
I.
G
u
y
o
n
,
a
n
d
A
.
El
isse
e
fff
,
“
An
in
tri
d
u
c
ti
o
n
t
o
v
a
riab
le
a
n
d
f
e
a
tu
re
se
lec
ti
o
n
,
”
J
o
u
rn
a
l
o
f
M
a
c
h
in
e
L
e
a
r
n
in
g
Res
e
a
rc
h
,
v
o
l.
3
,
p
p
.
1
1
5
7
-
1
1
8
2
,
2
0
0
3
,
d
o
i:
1
0
.
1
1
6
2
/
1
5
3
2
4
4
3
0
3
3
2
2
7
5
3
6
1
6
.
[3
8
]
H.
Ak
a
ik
e
,
“
In
f
o
r
m
a
ti
o
n
th
e
o
ry
a
n
d
a
n
e
x
ten
sio
n
o
f
th
e
m
a
x
i
m
u
m
li
k
e
li
h
o
o
d
p
ri
n
c
ip
le,
”
i
n
In
Pr
o
c
e
e
d
in
g
s
o
f
th
e
S
e
c
o
n
d
I
n
ter
n
a
ti
o
n
a
l
S
y
mp
o
si
u
m
o
n
In
f
o
rm
a
ti
o
n
T
h
e
o
ry
;
P
e
tr
o
v
.
B.
N.,
Ca
sk
i.
F
.
,
Ed
s.;
A
k
a
d
e
m
iai
Ki
a
d
o
;
Bu
d
a
p
e
st,
H
u
n
g
a
ry
,
1
9
7
3
,
p
p
.
2
6
7
-
2
8
1
,
d
o
i:
1
0
.
1
0
0
7
/
9
7
8
-
1
-
4
6
1
2
-
1
6
9
4
-
0
_
1
5
.
[3
9
]
E
.
J
.
W
a
g
e
n
m
a
k
e
rs
,
a
n
d
S
.
F
a
rre
l
l,
“
A
IC
m
o
d
e
l
se
lec
ti
o
n
u
sin
g
A
k
a
ik
e
w
e
ig
h
ts,
”
Psy
c
h
o
n
o
mic
B
u
ll
e
ti
n
&
Re
v
iew
,
v
o
l.
1
1
,
p
p
.
1
9
2
-
1
9
6
,
2
0
0
4
,
d
o
i:
1
0
.
3
7
5
8
/BF
0
3
2
0
6
4
8
2
.
[4
0
]
K.
Y.
S
o
n
g
,
I.
H.
Ch
a
n
g
,
a
n
d
H.
P
h
a
m
,
“
A
te
stin
g
c
o
v
e
ra
g
e
m
o
d
e
l
b
a
se
d
o
n
NH
P
P
so
f
tw
a
re
re
li
a
b
il
it
y
c
o
n
sid
e
rin
g
th
e
so
f
twa
re
o
p
e
ra
ti
n
g
e
n
v
iro
m
e
n
t
a
n
d
th
e
se
n
siti
v
it
y
a
n
a
l
y
sis,
”
M
a
th
e
ma
ti
c
s
,
v
o
l.
7
,
p
.
4
5
0
,
2
0
1
9
,
d
o
i
:
1
0
.
3
3
9
0
/m
a
th
7
0
5
0
4
5
0
.
[4
1
]
A
.
Da
v
id
,
“
Ra
y
lei
g
h
d
istri
b
u
ti
o
n
-
b
a
se
d
m
o
d
e
l
f
o
r
p
re
d
icti
o
n
o
f
w
in
d
e
n
e
rg
y
p
o
ten
ti
a
l
o
f
Ca
m
e
ro
o
n
,
”
En
e
rg
y
Rev
iew
,
v
o
l.
1
,
n
o
.
2
,
p
p
.
2
6
-
4
3
,
2
0
1
4
,
d
o
i:
1
0
.
1
8
4
8
8
/
jo
u
rn
a
l.
8
1
/
2
0
1
4
.
1
.
1
/8
1
.
1
.
2
6
.
4
3
.
[4
2
]
N
.
Y
.
Yu
ru
se
n
,
a
n
d
J
.
J
M
e
lero
,
“
P
r
o
b
a
b
i
li
ty
d
e
n
sit
y
f
u
n
c
ti
o
n
se
lec
ti
o
n
b
a
se
d
o
n
th
e
c
h
a
ra
c
teristics
o
f
w
in
d
sp
e
e
d
d
a
ta,
”
in
T
h
e
S
c
ien
c
e
o
f
M
a
k
i
n
g
T
o
rq
u
e
fro
m
W
in
d
(
T
ORQU
E
2
0
1
6
)
,
2
0
1
6
,
p
p
.
1
-
1
1
,
d
o
i
:
1
0
.
1
0
8
8
/1
7
4
2
-
6
5
9
6
/
7
5
3
/3
/
0
3
2
0
6
7
.
Evaluation Warning : The document was created with Spire.PDF for Python.