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T
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f
o
r
ec
asts
with
p
r
ac
ti
ca
l
en
er
g
y
esti
m
ates
to
ass
es
s
th
e
f
ea
s
ib
ilit
y
o
f
s
m
all
-
s
ca
le
tu
r
b
in
es in
ar
ea
s
lik
e
B
an
y
u
m
as R
eg
en
cy
.
T
ab
le
1
co
m
p
ar
es
th
is
s
tu
d
y
with
r
ec
en
t
r
elate
d
w
o
r
k
s
.
W
h
ile
ad
v
a
n
ce
d
d
ee
p
lear
n
in
g
h
y
b
r
id
s
lik
e
VM
D
-
GDPSO
-
T
C
N
-
B
iL
STM
an
d
DW
T
-
B
iLST
M
-
B
iG
R
U
ac
h
iev
e
h
ig
h
ac
cu
r
ac
y
,
th
ey
r
eq
u
ir
e
s
ig
n
if
ican
t
co
m
p
u
tatio
n
al
r
eso
u
r
ce
s
an
d
co
m
p
lex
ar
c
h
itectu
r
e
.
I
n
co
n
t
r
ast,
Sire
g
ar
an
d
Pu
tr
i
r
e
p
o
r
t
ed
a
lo
wer
R
MSE
(
0
.
1
9
m
/s
)
with
Pro
p
h
et;
h
o
w
ev
er
,
th
eir
u
s
e
o
f
m
o
n
th
l
y
-
ag
g
r
eg
ated
d
ata
s
m
o
o
th
e
d
o
u
t
v
o
latilit
y
,
s
ac
r
if
icin
g
th
e
g
r
a
n
u
lar
d
etail
n
ee
d
ed
f
o
r
o
p
er
atio
n
al
p
lan
n
in
g
.
L
iu
et
a
l
.
[
1
9
]
d
em
o
n
s
tr
ated
t
h
at,
f
o
r
s
h
o
r
t
-
ter
m
f
o
r
ec
asti
n
g
,
SAR
I
MA
ca
n
o
u
tp
er
f
o
r
m
co
m
p
lex
m
o
d
els
s
u
ch
as
L
STM
.
B
r
id
g
in
g
th
e
s
e
g
ap
s
,
th
is
s
tu
d
y
u
tili
ze
s
an
Op
tim
ized
SAR
I
M
A
m
o
d
el
o
n
wee
k
ly
d
ata,
m
ai
n
tain
in
g
co
m
p
etitiv
e
ac
cu
r
ac
y
(
R
MSE
0
.
5
1
6
m
/s
)
wh
ile
o
f
f
er
in
g
co
m
p
u
tatio
n
al
ef
f
icien
cy
an
d
in
ter
p
r
etab
ilit
y
ess
en
tial f
o
r
p
r
elim
in
ar
y
f
ea
s
ib
ilit
y
an
aly
s
is
.
T
h
e
n
o
v
elty
o
f
th
is
r
esear
ch
li
es in
th
r
ee
m
ain
asp
ec
ts
:
a.
T
h
e
ap
p
licatio
n
o
f
th
e
Seaso
n
al
AR
I
MA
m
eth
o
d
with
in
a
m
ac
h
in
e
lear
n
in
g
f
r
am
ewo
r
k
th
at
in
clu
d
es
p
ar
am
eter
o
p
tim
izatio
n
f
o
r
m
a
x
im
u
m
ac
cu
r
ac
y
.
b.
T
h
e
u
s
e
o
f
l
o
ca
l d
ata
f
r
o
m
B
a
n
y
u
m
as R
eg
en
cy
,
wh
ich
h
as u
n
iq
u
e
to
p
o
g
r
a
p
h
ic
ch
a
r
ac
ter
is
tics
.
c.
T
h
e
in
teg
r
atio
n
o
f
win
d
s
p
e
ed
p
r
e
d
ictio
n
r
esu
lts
with
q
u
an
titativ
e
esti
m
ates
o
f
p
o
w
er
an
d
elec
tr
ical
en
er
g
y
p
r
o
d
u
ctio
n
is
ap
p
licab
l
e
f
o
r
f
ea
s
ib
ilit
y
an
al
y
s
is
o
f
s
m
all
-
s
ca
le
win
d
p
o
wer
p
lan
t c
o
n
s
tr
u
ctio
n
.
T
ab
le
1
.
Me
th
o
d
co
m
p
ar
is
o
n
No
A
u
t
h
o
r
(
R
e
f
)
M
e
t
h
o
d
R
e
g
i
o
n
B
e
st
A
c
c
u
r
a
c
y
Li
mi
t
a
t
i
o
n
s
/
N
o
t
e
s
1
C
h
e
n
e
t
a
l
.
(
2
0
2
5
)
[
1
5
]
V
M
D
-
H
y
b
r
i
d
D
L
O
f
f
sh
o
r
e
R
M
S
E
:
0
.
1
4
m
/
s
H
i
g
h
c
o
m
p
u
t
a
t
i
o
n
a
l
c
o
m
p
l
e
x
i
t
y
a
n
d
t
r
a
i
n
i
n
g
t
i
m
e
.
2
B
a
r
j
a
st
e
h
e
t
a
l
.
(
2
0
2
4
)
[
1
6
]
D
W
T
-
H
y
b
r
i
d
D
L
La
n
d
-
b
a
s
e
d
R
M
S
E
:
0
.
2
5
m
/
s
C
o
m
p
l
e
x
"
b
l
a
c
k
-
b
o
x
"
a
r
c
h
i
t
e
c
t
u
r
e
;
h
a
r
d
e
r
t
o
i
n
t
e
r
p
r
e
t
.
3
S
i
r
e
g
a
r
a
n
d
P
u
t
r
i
(
2
0
2
5
)
[
1
7
]
P
r
o
p
h
e
t
M
e
d
a
n
R
M
S
E
:
0
.
1
9
m
/
s
U
sed
m
o
n
t
h
l
y
d
a
t
a
;
l
o
s
e
s s
h
o
r
t
-
t
e
r
m
f
l
u
c
t
u
a
t
i
o
n
d
e
t
a
i
l
s
.
4
Li
u
e
t
a
l
.
(
2
0
2
1
)
[
1
8
]
S
A
R
I
M
A
v
s
LSTM
O
f
f
sh
o
r
e
R
M
S
E
:
1
.
4
3
m
/
s
P
r
o
v
e
d
S
A
R
I
M
A
i
s
c
o
mp
e
t
i
t
i
v
e
a
g
a
i
n
st
d
e
e
p
l
e
a
r
n
i
n
g
.
5
Th
i
s
S
t
u
d
y
O
p
t
i
mi
z
e
d
S
A
R
I
M
A
B
a
n
y
u
ma
s
R
M
S
E
:
0
.
5
1
6
m/
s
B
a
l
a
n
c
e
d
a
c
c
u
r
a
c
y
f
o
r
w
e
e
k
l
y
g
r
a
n
u
l
a
r
d
a
t
a
;
e
f
f
i
c
i
e
n
t
f
o
r
f
e
a
si
b
i
l
i
t
y
st
u
d
y
.
2.
M
E
T
H
O
D
2
.
1
.
F
l
o
wcha
rt
T
h
e
r
esear
ch
m
et
h
o
d
o
lo
g
y
f
o
llo
ws
a
s
tr
u
ctu
r
ed
f
r
am
ew
o
r
k
illu
s
tr
ated
in
Fig
u
r
e
1
,
b
e
g
in
n
in
g
with
th
e
s
y
s
tem
atic
ac
q
u
is
itio
n
o
f
h
is
to
r
ical
win
d
s
p
ee
d
d
ata
(
W
S1
0
M)
f
o
r
th
e
B
an
y
u
m
as
r
eg
i
o
n
f
r
o
m
th
e
NASA
POW
E
R
d
atab
ase.
T
h
is
r
aw
d
ataset
u
n
d
er
wen
t
r
ig
o
r
o
u
s
p
r
ep
r
o
ce
s
s
in
g
,
in
clu
d
in
g
f
o
r
war
d
-
f
ill
im
p
u
tatio
n
to
co
r
r
ec
t
an
o
m
alies,
wee
k
ly
r
e
s
am
p
lin
g
to
m
itig
ate
s
to
ch
ast
ic
n
o
is
e,
an
d
s
tatio
n
ar
ity
v
er
if
icatio
n
u
s
in
g
th
e
au
g
m
en
ted
Dick
ey
-
Fu
ller
(
A
DF)
test
to
estab
lis
h
a
r
eliab
le
s
tatis
t
ical
f
o
u
n
d
atio
n
.
Su
b
s
eq
u
en
tly
,
th
e
d
ata
wer
e
ch
r
o
n
o
lo
g
ically
p
ar
titi
o
n
e
d
i
n
to
tr
ain
i
n
g
a
n
d
test
in
g
s
u
b
s
ets
to
s
u
p
p
o
r
t
r
o
b
u
s
t
m
o
d
e
l
d
ev
elo
p
m
en
t.
An
au
to
m
ated
g
r
id
s
ea
r
ch
was
th
en
ex
ec
u
ted
to
id
e
n
tify
th
e
o
p
tim
al
SAR
I
MA
p
ar
am
eter
s
b
y
m
in
im
izin
g
th
e
Ak
aik
e
in
f
o
r
m
atio
n
cr
iter
io
n
(
AI
C
)
,
with
p
r
ed
ictiv
e
ac
cu
r
ac
y
v
alid
ated
u
s
in
g
R
MSE
an
d
MA
E
m
etr
ics.
I
n
th
e
f
in
al
s
tag
e,
th
e
o
p
tim
ize
d
m
o
d
el
g
en
e
r
ated
a
1
3
-
wee
k
win
d
s
p
ee
d
f
o
r
ec
ast,
wh
ich
was
s
ea
m
less
ly
in
teg
r
ated
in
to
win
d
k
i
n
etic
en
er
g
y
eq
u
atio
n
s
to
q
u
a
n
titativ
ely
ass
es
s
th
e
p
o
wer
an
d
en
e
r
g
y
p
r
o
d
u
ctio
n
p
o
ten
tial
f
o
r
r
esid
en
tial
-
s
ca
le
tu
r
b
in
es in
th
e
r
eg
io
n
.
2
.
2
.
Da
t
a
s
et
T
h
e
d
ataset
u
s
ed
in
th
is
s
tu
d
y
is
h
is
to
r
ical
t
im
e
-
s
er
ies
d
ata
f
r
o
m
th
e
NASA
POW
E
R
d
at
ab
ase.
T
h
e
d
ata
co
v
er
s
d
aily
clim
ato
lo
g
i
ca
l
v
ar
iab
les
in
B
an
y
u
m
as
R
eg
en
cy
f
r
o
m
ea
r
l
y
2
0
2
2
to
m
id
-
2
0
2
5
,
to
talin
g
1
,
2
7
7
r
o
ws.
T
h
e
p
r
im
ar
y
v
ar
ia
b
le
u
s
ed
is
win
d
s
p
ee
d
at
1
0
m
eter
s
(
W
S1
0
M)
.
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
Win
d
s
p
ee
d
p
r
ed
ictio
n
a
n
d
en
erg
y
esti
ma
tio
n
u
s
in
g
th
e
S
A
R
I
MA
…
(
A
b
d
u
l H
a
kim
P
r
ima
Yu
n
ia
r
to
)
1427
S
T
A
R
T
PR
E
-
PR
O
C
E
S
S
I
N
G
D
A
T
A
S
P
L
I
T
H
Y
P
E
R
P
A
R
A
M
E
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R
T
U
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I
N
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FI
N
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H
M
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D
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L
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A
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I
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P
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A
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C
U
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A
T
I
O
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D
A
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A
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E
T
Fig
u
r
e
1
.
R
esear
ch
f
lo
wc
h
ar
t
2
.
3
.
P
re
pro
ce
s
s
ing
da
t
a
Data
p
r
ep
r
o
ce
s
s
in
g
s
tep
s
ar
e
ca
r
r
ied
o
u
t
to
en
s
u
r
e
d
ata
q
u
a
lity
an
d
r
ea
d
in
ess
b
ef
o
r
e
t
h
e
m
o
d
ellin
g
p
r
o
ce
s
s
[
2
1
]
,
wh
ich
c
o
n
s
is
t o
f
:
a.
Date
co
lu
m
n
ar
r
an
g
em
en
t:
t
h
e
y
ea
r
(
YE
AR
)
,
m
o
n
t
h
(
MO
)
,
an
d
d
ay
(
DY)
co
l
u
m
n
s
ar
e
c
o
m
b
in
ed
in
to
a
s
in
g
le
'
Date
'
co
lu
m
n
to
f
ac
ilit
ate
tim
e
s
er
ies an
aly
s
is
.
b.
An
o
m
alo
u
s
v
alu
e
h
a
n
d
lin
g
:
A
v
alu
e
o
f
-
9
9
9
,
wh
ich
i
n
d
i
ca
tes
u
n
av
ailab
le
d
ata,
is
co
n
v
er
ted
to
No
t
a
Nu
m
b
er
(
NaN
)
a
n
d
th
e
n
im
p
u
ted
u
s
in
g
th
e
p
r
ev
io
u
s
v
alid
v
a
lu
e
(
f
o
r
war
d
f
ill).
c.
Data
R
esam
p
lin
g
:
T
o
r
ed
u
ce
n
o
is
e
a
n
d
co
m
p
u
tatio
n
al
b
u
r
d
en
,
d
aily
d
ata
is
r
esam
p
le
d
to
a
wee
k
ly
f
r
eq
u
e
n
cy
b
y
av
er
a
g
in
g
o
v
er
7
d
ay
s
.
d.
Data
s
tatio
n
ar
ity
is
test
ed
u
s
i
n
g
th
e
ADF
test
to
en
s
u
r
e
th
at
th
e
d
ata'
s
s
ta
tis
t
ical
p
r
o
p
er
t
ies,
s
u
ch
as
th
e
m
ea
n
an
d
v
ar
ian
ce
,
d
o
n
o
t
ch
a
n
g
e
o
v
e
r
tim
e.
I
f
th
e
d
ata
is
n
o
t
s
tatio
n
ar
y
,
a
d
if
f
er
en
cin
g
p
r
o
ce
s
s
is
ap
p
lied
to
s
tab
ilize
th
e
m
ea
n
.
I
n
th
is
s
tu
d
y
,
ac
h
iev
in
g
s
tatio
n
ar
ity
is
a
cr
itical
p
r
e
r
eq
u
is
ite
f
o
r
t
h
e
S
AR
I
MA
m
o
d
el
to
p
r
o
d
u
ce
r
eliab
le
a
n
d
m
at
h
e
m
atica
lly
v
alid
f
o
r
ec
asts
.
2
.
4
.
M
o
delin
g
a
nd
ev
a
lua
t
io
n
T
h
e
m
o
d
ellin
g
p
r
o
ce
s
s
b
eg
in
s
b
y
d
iv
id
i
n
g
th
e
d
ataset
in
to
8
0
%
f
o
r
tr
ain
in
g
an
d
2
0
%
f
o
r
t
esti
n
g
,
in
tim
e
-
s
eq
u
en
tial
o
r
d
e
r
.
Sin
ce
t
h
e
d
ata
s
h
o
ws
an
an
n
u
al
s
ea
s
o
n
al
p
atter
n
,
th
e
m
o
d
el
u
s
ed
is
SAR
I
MA
[
2
2
]
.
T
h
e
o
p
tim
al
p
ar
a
m
eter
s
f
o
r
th
e
SAR
I
MA
m
o
d
el
(
p
,
d
,
q
)
an
d
th
e
s
ea
s
o
n
al
p
ar
am
eter
s
(
P,
D,
Q,
s
)
ar
e
d
eter
m
in
ed
th
r
o
u
g
h
a
h
y
p
er
p
ar
am
eter
tu
n
in
g
p
r
o
ce
s
s
u
s
in
g
th
e
Gr
id
Sear
ch
m
eth
o
d
,
with
th
e
Ak
aik
e
in
f
o
r
m
atio
n
cr
iter
io
n
(
AI
C
)
as th
e
r
e
f
er
en
c
e
m
etr
ic
f
o
r
m
o
d
el
e
f
f
icien
cy
[
2
3
]
.
T
h
e
tr
ain
ed
m
o
d
el
is
th
en
ev
alu
ated
o
n
test
d
ata
u
s
in
g
tw
o
m
ain
m
etr
ics:
r
o
o
t
m
ea
n
s
q
u
ar
ed
er
r
o
r
(
R
MSE
)
an
d
m
ea
n
ab
s
o
lu
te
er
r
o
r
(
MA
E
)
.
L
o
we
r
R
MS
E
an
d
MA
E
v
alu
es
in
d
icate
h
ig
h
er
p
r
e
d
ictio
n
ac
cu
r
ac
y
.
T
h
e
m
o
d
el
with
th
e
b
est ac
cu
r
ac
y
is
th
en
u
s
ed
f
o
r
th
e
p
r
ed
ictio
n
s
tag
e
[
2
4
]
,
[
2
5
]
.
2
.
5
.
E
lect
rica
l
po
wer
a
nd
ener
g
y
ca
lcula
t
io
n
T
h
e
r
esu
lts
o
f
th
e
win
d
s
p
ee
d
p
r
ed
ictio
n
ar
e
u
s
ed
to
esti
m
ate
th
e
p
o
ten
tial
elec
tr
ical
p
o
w
er
th
at
ca
n
b
e
g
en
er
ate
d
b
y
th
e
win
d
t
u
r
b
i
n
e.
T
h
is
ca
lcu
latio
n
u
s
es th
e
p
h
y
s
ics eq
u
atio
n
f
o
r
win
d
k
in
et
ic
en
er
g
y
[
2
6
]
:
=
1
2
3
wh
er
e
:
e
lectr
ical
p
o
wer
g
en
er
ated
(
watt
)
,
:
a
ir
d
en
s
ity
(
1
.
2
2
5
k
g
/m
³)
,
:
p
r
o
p
eller
s
wep
t
ar
ea
,
ass
u
m
ed
t
o
b
e
7
.
0
7
m
²
(
3
m
d
iam
eter
r
es
id
en
tial
tu
r
b
i
n
e)
,
:
W
in
d
s
p
ee
d
(
m
/s
)
,
p
r
ed
icted
r
esu
lt
,
:
Po
wer
co
ef
f
icien
t
(
tu
r
b
in
e
e
f
f
icien
cy
)
,
ass
u
m
ed
t
o
b
e
3
5
%
.
T
h
is
ca
lcu
latio
n
also
tak
es
in
to
ac
co
u
n
t
th
e
tu
r
b
in
e'
s
o
p
er
atio
n
al
lim
itatio
n
s
,
s
p
ec
if
icall
y
a
cu
t
-
in
s
p
ee
d
o
f
3
m
/s
,
th
e
m
in
im
u
m
s
p
ee
d
r
eq
u
ir
ed
to
g
e
n
er
ate
elec
tr
icity
,
an
d
a
r
ated
ca
p
ac
ity
o
f
1
0
0
0
watt
s
,
th
e
m
ax
im
u
m
p
o
wer
lim
it
[
2
7
]
.
B
y
ap
p
l
y
in
g
th
ese
co
n
s
tr
ain
ts
,
th
e
esti
m
atio
n
m
o
d
el
p
r
ev
en
ts
u
n
r
ea
lis
tic
o
v
er
esti
m
atio
n
s
d
u
r
in
g
p
er
io
d
s
o
f
e
x
tr
em
e
win
d
s
p
ee
d
s
.
Fin
ally
,
th
e
to
tal
esti
m
ated
e
lectr
ical
en
er
g
y
in
k
ilo
watt
-
h
o
u
r
s
(
k
W
h
)
is
d
eter
m
in
ed
b
y
m
u
ltip
ly
in
g
t
h
e
r
es
u
ltin
g
p
o
wer
(
P)
b
y
t
h
e
to
tal
d
u
r
atio
n
o
f
a
wee
k
(
1
6
8
h
o
u
r
s
)
an
d
d
iv
id
in
g
b
y
1
0
0
0
[
2
8
]
.
=
×
1000
wh
er
e
is
e
n
er
g
y
p
r
o
d
u
ce
d
(
k
W
h
)
,
is
e
lectr
ical
p
o
wer
(
wa
tt)
,
an
d
is
tim
e/d
u
r
atio
n
in
h
o
u
r
s
d
u
r
in
g
a
wee
k
(
1
6
8
h
o
u
r
s
)
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
1
6
,
No
.
3
,
J
u
n
e
20
2
6
:
1
4
2
5
-
1
4
3
3
1428
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
3
.
1
.
H
y
perpa
ra
m
e
t
er
o
ptim
i
za
t
io
n a
nd
bes
t
m
o
del selec
t
io
n
T
o
id
en
tify
th
e
o
p
tim
al
f
o
r
e
ca
s
tin
g
m
o
d
el,
a
g
r
id
s
ea
r
ch
o
p
tim
izatio
n
was
p
er
f
o
r
m
e
d
o
v
er
th
e
SAR
I
MA
(
p
,
d
,
q
)
×
(
P,
D,
Q)
5
2
p
a
r
am
eter
s
p
ac
e
with
in
a
m
a
ch
in
e
lear
n
in
g
f
r
am
ew
o
r
k
.
A
t
o
tal
o
f
1
6
d
i
f
f
er
en
t
m
o
d
el
co
m
b
in
atio
n
s
we
r
e
tr
ain
ed
an
d
ev
al
u
ated
to
s
y
s
tem
atica
lly
d
eter
m
in
e
th
e
c
o
n
f
ig
u
r
atio
n
th
at
b
est
ca
p
tu
r
es th
e
win
d
s
p
ee
d
ch
ar
a
cter
is
tics
in
B
an
y
u
m
as.
T
h
e
s
elec
tio
n
p
r
o
ce
s
s
p
r
io
r
iti
ze
d
m
in
im
izin
g
er
r
o
r
m
etr
ics
(
R
MSE
an
d
MA
E
)
to
en
s
u
r
e
p
r
ed
ictiv
e
ac
cu
r
ac
y
.
W
h
ile
th
e
AI
C
is
tr
a
d
itio
n
ally
u
s
ed
as a
m
ea
s
u
r
e
o
f
s
tatis
t
ical
ef
f
icien
cy
an
d
m
o
d
el
p
ar
s
im
o
n
y
[
2
9
]
,
r
ec
en
t
s
tu
d
ies
em
p
h
asize
th
at
,
f
o
r
p
r
ac
tical
r
en
ewa
b
le
en
er
g
y
f
ea
s
ib
ilit
y
a
n
aly
s
is
,
m
in
im
izin
g
th
e
p
h
y
s
ical
p
r
ed
ictio
n
er
r
o
r
is
m
o
r
e
cr
itic
al
f
o
r
r
ed
u
cin
g
o
p
er
atio
n
al
r
i
s
k
[
3
0
]
.
C
o
n
s
eq
u
en
tly
,
alth
o
u
g
h
s
ev
er
al
m
o
d
els
ex
h
ib
ited
co
m
p
etitiv
e
AI
C
s
c
o
r
es,
th
e
SAR
I
MA
(
1
,
0
,
0
)
×
(
0
,
1
,
1
,
5
2
)
co
n
f
ig
u
r
atio
n
was
s
elec
ted
a
s
th
e
o
p
tim
al
m
o
d
el
b
ec
au
s
e
it y
ield
ed
th
e
lo
west R
MSE
an
d
MA
E
v
alu
es.
T
o
r
ig
o
r
o
u
s
ly
e
v
alu
ate
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
p
r
o
p
o
s
e
d
m
o
d
el
,
it
was
co
m
p
a
r
ed
ag
ain
s
t
two
b
aselin
e
m
o
d
els:
a
s
tan
d
a
r
d
AR
I
MA
(
1
,
0
,
0
)
with
o
u
t
s
ea
s
o
n
ality
an
d
a
s
ea
s
o
n
al
Naiv
e
m
o
d
el.
T
h
e
co
m
p
ar
ativ
e
p
er
f
o
r
m
a
n
ce
r
es
u
lts
ar
e
s
u
m
m
ar
ize
d
in
T
ab
le
2
.
As
s
h
o
wn
i
n
T
a
b
le
2
,
th
e
p
r
o
p
o
s
ed
SAR
I
MA
m
o
d
el
o
u
tp
er
f
o
r
m
s
b
o
th
b
ase
lin
es
ac
r
o
s
s
all
ev
alu
atio
n
m
e
tr
ics.
T
h
e
m
o
d
el
ac
h
iev
ed
th
e
lo
west
p
r
ed
ictio
n
er
r
o
r
s
with
an
R
MSE
o
f
0
.
5
1
6
0
m
/s
an
d
a
MA
PE
o
f
1
9
.
4
3
%,
s
ig
n
if
ican
tly
o
u
tp
er
f
o
r
m
in
g
t
h
e
Seaso
n
al
Naiv
e
b
en
ch
m
ar
k
(
R
MSE
0
.
7
3
3
6
m
/s
,
MA
PE
2
6
.
7
0
%).
I
t
is
im
p
o
r
tan
t
to
n
o
te
th
at
th
e
co
ef
f
icien
t
o
f
d
eter
m
in
atio
n
(
R
2
)
v
alu
es
f
o
r
all
m
o
d
els
was
n
eg
ativ
e.
T
h
is
is
a
co
m
m
o
n
p
h
e
n
o
m
en
o
n
in
h
ig
h
-
v
o
latilit
y
win
d
-
s
p
ee
d
f
o
r
ec
asti
n
g
wh
e
n
th
e
v
ar
ian
ce
o
f
th
e
p
r
ed
ictio
n
er
r
o
r
s
e
x
ce
ed
s
th
at
o
f
th
e
d
a
ta,
p
ar
ticu
lar
ly
in
wee
k
ly
-
a
g
g
r
eg
ated
d
ata.
Ho
we
v
er
,
th
e
p
r
o
p
o
s
ed
SAR
I
MA
m
o
d
el
ac
h
iev
e
d
th
e
h
i
g
h
est
R
2
v
alu
e
(
-
0
.
1
9
1
0
)
co
m
p
ar
ed
t
o
th
e
s
ig
n
if
ican
tly
lo
wer
v
alu
es
o
f
th
e
b
aselin
es
(
-
0
.
4
7
9
2
an
d
-
1
.
4
0
4
7
)
.
T
h
is
in
d
icate
s
th
at
th
e
p
r
o
p
o
s
ed
m
o
d
el
ca
p
tu
r
es
th
e
co
m
p
lex
s
ea
s
o
n
al
d
y
n
a
m
ics
o
f
th
e
B
an
y
u
m
as
win
d
p
r
o
f
ile
f
ar
m
o
r
e
ef
f
ec
tiv
ely
th
an
tr
ad
itio
n
a
l
m
eth
o
d
s
,
p
r
o
v
id
i
n
g
th
e
m
o
s
t
r
eliab
le
b
asis
f
o
r
en
er
g
y
esti
m
atio
n
.
T
ab
le
2
.
Per
f
o
r
m
an
ce
co
m
p
a
r
is
o
n
p
r
o
p
o
s
ed
m
o
d
el
an
d
b
aselin
es
M
o
d
e
l
Ty
p
e
P
a
r
a
me
t
e
r
s/
M
e
t
h
o
d
A
I
C
M
A
E
(
m
/
s)
R
M
S
E
(
m/
s)
M
A
P
E
(
%)
R2
P
r
o
p
o
se
d
S
A
R
I
M
A
(
1
,
0
,
0
)
×
(
0
,
1
,
1
,
5
2
)
8
3
.
8
1
6
0
.
4
4
0
9
0
.
5
1
6
1
9
.
4
3
-
0
.
1
9
1
B
a
se
l
i
n
e
1
A
R
I
M
A
(
1
,
0
,
0
)
(
n
o
n
-
se
a
s
o
n
a
l
)
2
1
9
.
5
1
2
0
.
4
8
4
9
0
.
5
7
5
3
2
1
.
2
-
0
.
4
7
9
2
B
a
se
l
i
n
e
2
S
e
a
so
n
a
l
N
a
i
v
e
N
/
A
0
.
6
0
0
5
0
.
7
3
3
6
2
6
.
7
-
1
.
4
0
4
7
3
.
2
.
Wind
s
peed
predict
io
n
re
s
ults
T
h
e
o
p
tim
al
SAR
I
MA
m
o
d
el
was
s
u
b
s
eq
u
en
tly
ap
p
lied
to
f
o
r
ec
ast
win
d
s
p
ee
d
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3
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2
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1
.
T
re
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T
h
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,
as
s
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u
r
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ar
e
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y
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e
m
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el
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th
at
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s
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g
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5
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atter
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ter
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f
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Sep
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SAR
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A
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o
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ter
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u
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em
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ates
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el'
s
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co
m
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t.
I
t p
r
o
v
id
es st
r
o
n
g
v
alid
ity
to
th
e
p
r
ed
ictio
n
r
esu
lts
[
3
1
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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&
C
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p
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I
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[
3
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.
3
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3
.
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s
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lies
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ased
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t 2
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ated
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ated
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atter
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ee
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f
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.
3
.
4
.
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s
t
im
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t
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l e
nerg
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(
k
Wh)
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m
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k
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m
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ld
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ates
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o
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r
s
in
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k
(
1
6
8
h
o
u
r
s
)
[
3
4
]
.
B
ased
o
n
th
e
ca
lcu
latio
n
s
i
n
T
ab
le
5
an
d
Fig
u
r
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4
,
th
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to
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d
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r
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e
3
-
m
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ased
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1
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4
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ir
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4
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E
s
tim
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D
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.
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3
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5
3
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
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8
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I
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Vo
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1430
Fig
u
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3
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5
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W
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