I
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t
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l J
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t
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J
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AI
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Vo
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14
,
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.
3
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J
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2
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2
5
,
p
p
.
2229
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I
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8938
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DOI
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14
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Deep n
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PV
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p
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l
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trac
k
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g
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w
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r,
e
n
v
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m
e
n
ta
l
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it
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o
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s,
ti
m
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o
f
d
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y
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a
n
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th
e
a
n
g
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o
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m
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v
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m
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n
t
o
f
th
e
so
lar
tra
c
k
e
r
c
a
n
a
ffe
c
t
th
e
re
su
lt
in
g
p
o
w
e
r
o
u
tp
u
t
.
T
h
is
stu
d
y
a
i
m
s
to
p
re
d
ict
th
e
p
o
w
e
r
o
u
tp
u
t
o
f
d
u
a
l
-
a
x
is
so
lar
trac
k
in
g
in
o
r
d
e
r
to
m
a
in
tain
t
h
e
p
o
w
e
r’s
sta
b
il
it
y
a
n
d
q
u
a
li
ty
.
De
e
p
n
e
u
ra
l
n
e
tw
o
rk
s
(DN
N)
w
it
h
v
a
riatio
n
s
o
f
5
a
n
d
6
h
i
d
d
e
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lay
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h
a
v
e
b
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e
n
p
ro
p
o
se
d
.
T
h
e
d
a
tas
e
t
u
se
d
in
th
is
stu
d
y
w
a
s
o
b
tain
e
d
f
ro
m
o
b
se
rv
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ti
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n
re
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lt
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d
th
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to
8
0
%
train
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g
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ta
a
n
d
2
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e
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g
d
a
ta
.
A
s
e
ries
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f
a
l
g
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rit
h
m
s
a
re
u
se
d
to
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o
g
n
ize
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latio
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sh
ip
p
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p
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t
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d
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d
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tw
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a
s
w
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s
hi
d
d
e
n
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y
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rs
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n
d
o
u
tp
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t.
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tatisti
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a
l
re
s
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lt
s
sh
o
w
th
a
t
DN
N
w
it
h
a
v
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f
6
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id
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e
tt
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r
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t
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sti
m
a
ti
n
g
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lar
trac
k
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g
p
o
w
e
r
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u
tp
u
t
w
it
h
a
m
e
a
n
a
b
so
lu
te
p
e
rc
e
n
tag
e
e
rro
r
(
M
A
P
E
)
v
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lu
e
o
f
1
2
.
3
2
8
%
,
m
e
a
n
sq
u
a
re
e
rro
r
(
MSE
)
o
f
0
.
3
3
2
,
a
n
d
m
e
a
n
a
b
so
lu
te
e
rro
r
(
M
A
E
)
o
f
0
.
4
2
5
.
T
h
is
stu
d
y
c
a
n
b
e
u
se
d
a
s
a
re
f
e
r
e
n
c
e
in
u
ti
li
z
in
g
a
rti
f
icia
l
in
telli
g
e
n
c
e
to
p
re
d
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th
e
o
u
t
p
u
t
p
o
w
e
r
o
f
so
lar
p
a
n
e
ls
a
s
a
re
n
e
wa
b
le
e
n
e
rg
y
so
u
rc
e
.
K
ey
w
o
r
d
s
:
A
r
ti
f
icial
n
e
u
r
al
n
et
w
o
r
k
Dee
p
lear
n
in
g
Ou
tp
u
t p
o
w
er
P
r
ed
ictio
n
So
lar
tr
ac
k
er
T
h
is i
s
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
:
Hu
m
air
o
h
R
at
u
Ay
u
Dep
ar
t
m
en
t o
f
P
h
y
s
ics,
Fac
u
lt
y
o
f
Ma
t
h
e
m
a
tics
a
n
d
Natu
r
al
Scien
ce
s
,
Un
i
v
er
s
it
y
o
f
L
a
m
p
u
n
g
B
an
d
ar
L
a
m
p
u
n
g
,
I
n
d
o
n
es
ia
E
m
ail:
h
u
m
air
o
h
.
r
at
u
@
f
m
ip
a.
u
n
i
la.
ac
.
id
1.
I
NT
RO
D
UCT
I
O
N
So
lar
r
eso
u
r
ce
s
ar
e
a
r
en
ew
ab
le
en
er
g
y
s
o
u
r
ce
th
at
is
ab
u
n
d
an
t,
ea
s
y
to
u
ti
lize,
an
d
en
v
ir
o
n
m
e
n
tal
l
y
f
r
ien
d
l
y
[
1
]
.
T
h
e
av
ailab
ilit
y
o
f
s
o
lar
en
er
g
y
s
o
u
r
ce
s
i
s
s
ti
ll
b
ein
g
d
eter
m
i
n
ed
d
u
e
to
ch
an
g
es
in
t
h
e
s
u
n
's
p
o
s
itio
n
,
s
o
th
e
s
o
lar
ce
lls
'
o
u
tp
u
t
p
o
w
er
is
n
o
t
o
p
ti
m
a
l.
I
n
c
r
ea
s
in
g
th
e
o
u
tp
u
t
p
o
w
er
o
f
s
o
lar
p
an
els
ca
n
b
e
d
o
n
e
w
it
h
s
tatic
s
y
s
te
m
s
w
it
h
f
i
x
ed
an
g
le
s
[
2
]
an
d
s
in
g
le
-
a
x
is
[
3
]
–
[
5
]
o
r
d
u
al
-
ax
is
[
6
]
–
[
9
]
s
o
lar
tr
ac
k
er
s
.
T
h
e
m
o
s
t
e
f
f
ec
tiv
e
w
a
y
to
i
n
cr
ea
s
e
s
o
lar
ce
ll
en
er
g
y
is
to
u
s
e
d
u
al
-
ax
i
s
s
o
lar
tr
ac
k
i
n
g
b
y
2
5
.
5
%
w
h
ile
s
in
g
le
-
ax
is
is
o
n
l
y
1
6
.
5
%
co
m
p
ar
ed
to
a
f
i
x
ed
s
y
s
te
m
[
1
0
]
.
E
n
v
ir
o
n
m
en
tal
co
n
d
it
io
n
s
also
a
f
f
ec
t
th
e
p
o
w
er
o
u
tp
u
t
p
r
o
d
u
ce
d
,
s
u
ch
as
t
h
e
in
ten
s
it
y
o
f
s
o
lar
r
ad
iatio
n
a
n
d
e
n
v
ir
o
n
m
en
tal
te
m
p
er
atu
r
e.
W
h
en
t
h
e
w
ea
th
er
is
clo
u
d
y
,
th
e
r
ad
iatio
n
in
te
n
s
it
y
w
il
l d
ec
r
ea
s
e,
an
d
th
e
te
m
p
e
r
atu
r
e
w
ill b
e
lo
w
,
r
ed
u
c
in
g
t
h
e
r
eq
u
ir
ed
elec
tr
icit
y
s
u
p
p
l
y
[
1
1
]
.
T
h
e
m
ai
n
th
i
n
g
th
at
m
u
s
t
b
e
d
o
n
e
is
to
p
r
e
d
ict
th
e
o
u
tp
u
t
p
o
w
er
o
f
s
o
lar
ce
lls
[
1
2
]
to
m
ain
tai
n
th
e
s
tab
ili
t
y
a
n
d
q
u
alit
y
o
f
t
h
e
p
o
w
er
p
r
o
d
u
ce
d
[
1
]
.
R
esea
r
ch
r
e
lat
e
d
t
o
f
o
r
ec
asti
n
g
p
h
o
to
v
o
lt
ai
c
(
P
V
)
o
u
tp
u
t
p
o
w
er
b
ase
d
o
n
c
o
m
p
u
tati
o
n
al
i
n
tellig
en
ce
alg
o
r
ith
m
s
h
as
b
ee
n
w
id
ely
ca
r
r
ie
d
o
u
t;
s
ev
er
al
r
ese
ar
ch
er
s
u
s
e
th
e
ar
t
if
ici
al
n
eu
r
al
n
etw
o
r
k
(
A
NN)
m
eth
o
d
t
o
p
r
e
d
ic
t
cu
r
r
en
t
s
o
l
ar
r
a
d
iat
i
o
n
,
s
h
o
r
t
-
te
r
m
an
d
l
o
n
g
-
te
r
m
p
r
e
d
ic
ti
o
n
s
[
1
3
]
,
A
NN
w
ith
in
p
u
t
n
am
ely
m
eteo
r
o
l
o
g
ic
al
c
o
n
d
iti
o
n
s
,
cli
m
ate,
an
d
r
a
d
i
o
m
etr
i
c,
in
clu
d
in
g
w
in
d
s
p
ee
d
an
d
r
el
ativ
e
h
u
m
id
ity
as
w
ell
as
o
u
t
p
u
t
in
th
e
f
o
r
m
o
f
lo
ca
l
s
o
lar
p
an
e
l
en
e
r
g
y
o
u
tp
u
t
[
1
4
]
,
in
p
u
t
in
th
e
f
o
r
m
o
f
w
ea
th
er
co
n
d
iti
o
n
s
an
d
PV
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8938
I
n
t J
A
r
ti
f
I
n
tell
,
Vo
l.
1
4
,
No
.
3
,
J
u
n
e
2
0
2
5
:
2
2
2
9
-
2235
2230
m
o
d
u
le
ch
ar
ac
t
e
r
is
ti
cs
[
1
5
]
,
in
p
u
t in
th
e
f
o
r
m
o
f
en
v
ir
o
n
m
en
t
al
f
ac
t
o
r
s
s
u
ch
as ir
r
a
d
i
an
ce
(
G)
,
t
em
p
er
atu
r
e
(
T
)
,
h
u
m
id
ity
(
H)
,
w
in
d
s
p
e
ed
(
W
)
[
1
1
]
,
in
p
u
t
b
ase
d
o
n
en
v
ir
o
n
m
e
n
tal
f
a
ct
o
r
s
[
1
6
]
.
F
ee
d
-
f
o
r
w
ar
d
b
a
ck
p
r
o
p
ag
ati
o
n
n
eu
r
al
n
etw
o
r
k
(
F
B
A
NN)
[
1
7
]
.
Su
p
p
o
r
t
v
e
ct
o
r
m
ac
h
in
e
(
S
VM
)
[
1
8
]
,
l
o
n
g
s
h
o
r
t
-
te
r
m
m
e
m
o
r
y
(
L
ST
M
)
[
1
9
]
,
s
u
p
p
o
r
t
v
e
ct
o
r
m
ac
h
in
e
r
eg
r
es
s
io
n
(
SVMR
)
[
2
0
]
,
r
ec
u
r
r
en
t
n
eu
r
a
l
n
etw
o
r
k
(
R
NN
)
[
2
1
]
,
f
u
z
zy
r
eg
r
ess
io
n
(
FR
)
[
2
2
]
,
p
a
r
ti
cle
s
w
ar
m
o
p
tim
izat
io
n
(
P
SO
-
Fu
z
zy
)
[
1
]
,
an
d
P
S
O
-
ad
a
p
tiv
e
n
eu
r
o
f
u
zz
y
in
f
er
en
ce
s
y
s
te
m
(
A
NFI
S
)
[
2
3
]
.
Am
o
n
g
th
e
c
o
m
p
u
tati
o
n
al
m
eth
o
d
o
l
o
g
i
es
u
s
e
d
,
A
NN
i
s
a
s
u
p
er
io
r
m
eth
o
d
in
p
r
e
d
ic
ti
n
g
s
o
la
r
c
ell
p
o
w
er
o
u
t
p
u
t
c
o
m
p
ar
e
d
t
o
f
u
zz
y
lo
g
ic
[
2
4
]
an
d
m
u
ltip
l
e
l
in
ea
r
r
e
g
r
ess
i
o
n
[
2
5
]
.
B
ase
d
o
n
th
is
e
x
p
lan
a
ti
o
n
,
A
NN
i
s
co
n
s
i
d
e
r
e
d
m
o
r
e
ef
f
ec
tiv
e
in
p
r
e
d
i
ctin
g
th
e
o
u
t
p
u
t
p
o
w
er
o
f
s
o
la
r
p
an
e
ls
.
H
o
w
ev
er
,
n
o
r
es
ea
r
ch
h
as
p
r
e
d
i
cte
d
th
e
p
o
w
er
o
u
tp
u
t
o
f
d
u
a
l
-
ax
is
s
o
la
r
tr
ac
k
in
g
u
s
in
g
a
d
ee
p
n
eu
r
al
n
etw
o
r
k
(
DN
N)
w
ith
in
p
u
t
p
a
r
am
ete
r
s
o
f
tim
e,
tilt
an
g
le
,
s
o
la
r
r
a
d
i
ati
o
n
in
t
en
s
ity
an
d
en
v
i
r
o
n
m
en
tal
t
em
p
e
r
atu
r
e
.
T
h
is
p
a
p
e
r
an
aly
s
es
th
e
im
p
ac
t
o
f
h
id
d
en
lay
er
s
v
a
r
i
ati
o
n
in
DN
N
m
o
d
el
t
o
r
e
ac
h
th
e
b
est
p
er
f
o
r
m
an
ce
b
as
ed
o
n
h
is
t
o
r
ic
al
d
ata
.
DNN
h
av
e
s
h
o
w
n
r
e
m
ar
k
ab
le
ca
p
ab
ilit
ies
in
v
ar
io
u
s
tas
k
s
,
i
n
clu
d
i
n
g
p
er
ce
p
tio
n
-
r
elate
d
o
n
es
s
u
ch
as
i
m
a
g
e
a
n
d
s
p
ee
ch
r
ec
o
g
n
itio
n
[
2
6
]
.
T
h
ese
m
o
d
els ca
n
lear
n
i
n
cr
ea
s
i
n
g
l
y
ab
s
tr
ac
t,
h
ig
h
er
-
le
v
el
r
ep
r
esen
tatio
n
s
o
f
th
e
i
n
p
u
t
d
ata,
an
d
h
av
e
b
ee
n
s
u
cc
ess
f
u
ll
y
ap
p
lied
to
m
ed
icin
e
an
d
h
ea
lth
ca
r
e
[
2
7
]
.
On
e
o
f
t
h
e
cr
itical
ar
ch
itect
u
r
al
ad
v
an
tag
e
s
o
f
d
e
ep
lear
n
in
g
is
t
h
e
u
s
e
o
f
m
a
n
y
h
id
d
en
n
e
u
r
o
n
s
a
n
d
la
y
er
s
,
t
y
p
icall
y
m
o
r
e
t
h
an
t
w
o
,
w
h
ic
h
allo
w
s
f
o
r
ex
te
n
s
i
v
e
co
v
er
a
g
e
o
f
th
e
r
a
w
d
ata
at
h
a
n
d
[
2
8
]
.
Nev
er
th
eles
s
,
t
h
e
d
eter
m
in
at
io
n
o
f
t
h
e
o
p
tim
a
l
n
u
m
b
er
o
f
h
id
d
en
la
y
er
s
is
a
cr
u
cial
asp
ec
t
in
t
h
e
d
esig
n
o
f
DNN
m
o
d
els,
as
it
d
i
r
ec
tl
y
i
m
p
ac
t
s
th
eir
p
er
f
o
r
m
a
n
ce
a
n
d
g
en
er
aliza
tio
n
ca
p
ab
ilit
ies.
2.
M
E
T
H
O
D
2
.
1
.
Arc
hite
ct
ure
o
f
deep
ne
ura
l net
w
o
rk
T
h
e
m
eth
o
d
a
p
p
li
e
d
in
th
is
p
a
p
e
r
t
o
p
r
e
d
i
ct
th
e
o
u
t
p
u
t
p
o
w
e
r
o
f
a
d
u
al
-
ax
is
s
o
l
ar
t
r
a
ck
er
i
s
an
A
NN
w
ith
m
an
y
h
id
d
en
l
ay
er
s
,
als
o
ca
l
le
d
a
DNN
.
T
h
e
r
e
is
a
t
r
a
in
in
g
p
r
o
c
ess
c
a
r
r
i
e
d
o
u
t
t
o
p
r
o
d
u
c
e
th
e
d
esi
r
e
d
o
u
t
p
u
t.
T
h
e
t
r
ain
in
g
p
r
o
ce
s
s
u
s
es
a
s
er
ies
o
f
alg
o
r
ith
m
s
to
r
ec
o
g
n
ize
r
e
lat
io
n
s
h
i
p
p
a
tte
r
n
s
b
e
tw
ee
n
in
p
u
t
an
d
h
id
d
en
lay
er
s
as
in
(
1
)
,
h
id
d
e
n
lay
er
1
to
th
e
n
ex
t
h
id
d
en
la
y
er
as
in
(
2
)
,
an
d
h
i
d
d
en
lay
er
to
o
u
t
p
u
t
as
in
(
3
)
[
2
9
]
.
I
n
th
e
f
in
al
s
tag
e
,
th
e
n
eu
r
o
n
a
p
p
lies
a
t
r
an
s
f
e
r
f
u
n
cti
o
n
to
o
b
ta
in
o
u
t
p
u
t [
1
4
]
.
T
h
er
ef
o
r
e,
th
e
p
e
r
f
o
r
m
an
ce
o
f
DN
N
d
e
p
en
d
s
o
n
th
e
w
o
r
k
o
f
n
eu
r
o
n
s
[
1
1
]
.
T
h
e
d
ev
e
lo
p
e
d
DNN
a
r
ch
it
ec
tu
r
e
is
s
h
o
w
n
in
Fig
u
r
e
1
.
ℎ
̅
1
=
Φ
(
1
̅
)
(
1
)
ℎ
̅
+
1
=
Φ
(
+
1
ℎ
̅
)
∀
∈
{
1
…
−
1
}
(
2
)
̅
=
Φ
(
+
1
ℎ
̅
)
(
3
)
W
h
e
r
e
ℎ
̅
1
is
th
e
f
i
r
s
t h
i
d
d
en
lay
er
,
is
th
e
w
eig
h
t,
Φ
is
th
e
a
ctiv
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ti
o
n
f
u
n
ctio
n
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an
d
̅
is
th
e
o
u
t
p
u
t.
Fig
u
r
e
1
.
A
r
ch
itectu
r
e
o
f
D
N
N
T
h
e
ac
tiv
at
io
n
f
u
n
cti
o
n
f
u
n
ctio
n
s
to
r
e
ce
iv
e
an
d
s
en
d
s
ig
n
al
s
b
etw
ee
n
lay
er
s
[
1
5
]
.
Sev
e
r
al
ac
tiv
a
ti
o
n
f
u
n
ctio
n
s
a
r
e
o
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s
e
d
,
n
a
m
el
y
T
an
h
,
L
in
ea
r
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d
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ec
t
i
f
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lin
e
ar
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it
(
R
eL
u
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,
b
u
t
t
h
e
R
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u
ac
t
iv
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o
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u
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p
r
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i
d
es
th
e
b
es
t
r
esu
lts
am
o
n
g
th
e
t
w
o
[
1
4
]
.
D
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r
m
in
in
g
th
e
n
u
m
b
er
o
f
n
eu
r
o
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s
in
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id
d
en
lay
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Hu
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2231
is
b
ase
d
o
n
tr
ial
an
d
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r
r
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r
,
b
ec
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s
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f
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s
in
a
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[
1
5
]
.
2
.
2
.
Da
t
a
c
o
llect
io
n
T
h
e
d
ataset
u
s
ed
i
n
t
h
is
r
e
s
ea
r
ch
w
as
o
b
tai
n
ed
f
r
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m
o
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s
er
v
atio
n
s
m
ad
e
f
r
o
m
0
8
.
3
0
to
1
6
.
3
0
.
T
im
e
p
ar
am
eter
s
ar
e
d
iv
id
ed
in
to
tw
o
ca
teg
o
r
ies,
n
a
m
e
l
y
a
m
a
n
d
p
m
.
T
em
p
er
atu
r
e
(
°
C
)
is
th
e
en
v
ir
o
n
m
en
ta
l
te
m
p
er
atu
r
e
m
ea
s
u
r
ed
d
u
r
in
g
o
b
s
er
v
atio
n
s
a
s
w
ell
as
th
e
r
ad
iatio
n
p
ar
am
e
ter
(
W
⁄m
2
)
.
Me
an
w
h
ile,
t
h
e
tilt
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g
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(
°
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is
t
h
e
an
g
le
o
f
m
o
v
e
m
e
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t
o
f
t
h
e
s
o
lar
tr
ac
k
in
g
w
h
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s
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at
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ce
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t
ain
ti
m
e
d
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r
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g
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b
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v
atio
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[
3
0
]
.
B
ef
o
r
e
th
e
t
r
ain
in
g
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r
o
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s
s
,
th
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iv
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ata
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d
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in
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ata.
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h
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o
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ith
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w
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k
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ata
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icall
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al
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h
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r
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s
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th
e
A
d
a
m
a
x
o
p
tim
izer
w
ith
1
0
0
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o
ch
s
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2
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3
.
T
est
perf
o
rm
a
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o
f
m
o
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Me
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s
q
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ar
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o
r
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MSE
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u
s
ed
to
m
ea
s
u
r
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th
e
av
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a
g
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s
q
u
ar
ed
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r
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r
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i
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ize
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e
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r
o
r
as in
(
4
)
[
3
1
]
.
=
1
∑
(
−
̅
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2
=
1
(
4
)
W
ith
is
ac
tu
al
d
ata
an
d
n
r
ep
r
esen
ti
n
g
t
h
e
to
tal
n
u
m
b
er
o
f
s
a
m
p
les.
T
h
e
m
ea
n
ab
s
o
lu
te
e
r
r
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r
(
MA
E
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is
th
e
av
er
ag
e
o
f
t
h
e
ab
s
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lu
te
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r
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e
o
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ac
t
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al
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ata
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d
th
e
p
r
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icted
v
alu
e
as i
n
(
5
)
[
3
1
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.
=
1
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|
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̅
|
=
1
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Me
an
ab
s
o
lu
te
p
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ta
g
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er
r
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(
MA
P
E
)
ai
m
s
to
m
ea
s
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th
e
le
v
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f
m
o
d
el
ac
c
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r
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y
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lcu
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tin
g
t
h
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ab
s
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te
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if
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er
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ce
b
et
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ac
tu
al
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ata
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d
p
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ed
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al
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t
h
en
d
i
v
id
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b
y
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h
e
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tu
al
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alu
e,
t
h
e
n
m
u
ltip
l
y
in
g
b
y
1
0
0
to
ex
p
r
ess
it a
s
a
p
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ce
n
tag
e
as i
n
(
6
)
[
3
1
]
.
=
1
∑
|
−
̅
|
100
=
1
(
6
)
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
m
ai
n
o
b
j
ec
tiv
e
o
f
th
is
r
esear
ch
is
to
d
ev
elo
p
a
DNN
m
o
d
el
b
y
co
m
p
ar
i
n
g
5
h
id
d
en
l
a
y
er
s
,
an
d
6
h
id
d
en
la
y
er
s
to
p
r
ed
ict
t
h
e
o
u
tp
u
t
p
o
w
er
o
f
a
d
u
al
-
a
x
is
s
o
lar
tr
ac
k
er
w
it
h
t
h
e
i
n
p
u
t
p
ar
a
m
eter
s
o
f
ti
m
e,
tilt
an
g
le,
s
o
lar
r
ad
iatio
n
i
n
te
n
s
it
y
,
a
n
d
en
v
ir
o
n
m
en
ta
l
te
m
p
er
atu
r
e.
T
a
b
le
1
s
h
o
w
s
th
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
DNN
m
o
d
el
u
s
ed
w
it
h
v
ar
io
u
s
h
id
d
en
la
y
er
s
in
th
e
tr
ain
in
g
an
d
te
s
ti
n
g
p
r
o
ce
s
s
.
B
ased
o
n
T
ab
le
1
,
it
ca
n
b
e
s
ee
n
t
h
at
b
o
th
p
r
ed
icto
r
s
tr
a
ck
ac
tu
al
d
ata
an
d
ca
n
b
e
u
s
ed
f
o
r
esti
m
atio
n
,
b
u
t
t
h
e
DNN
m
o
d
el
w
it
h
6
h
id
d
en
la
y
er
s
h
a
v
e
t
h
e
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[
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[
2
7
]
C
.
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