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Artif
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renewable
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late
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wa
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e
rg
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p
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K
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w
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AI
in
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b
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Dec
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tim
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Pre
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m
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Sm
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rticle
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CC B
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li
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se
.
C
o
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r
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s
p
o
nd
ing
A
uth
o
r
:
I
an
B
.
B
en
itez
R
esear
ch
Of
f
ice,
FEU
I
n
s
titu
te
o
f
T
ec
h
n
o
lo
g
y
Pad
r
e
Par
ed
es St.,
Sam
p
alo
c,
Ma
n
ila,
1
0
1
5
Me
tr
o
Ma
n
ila,
Ph
ilip
p
in
es
E
m
ail:
ib
b
en
itez@
f
eu
tech
.
ed
u
.
p
h
1.
I
NT
RO
D
UCT
I
O
N
Fo
s
s
il
f
u
els,
cu
r
r
en
tly
p
r
ev
alen
t
en
er
g
y
s
o
u
r
ce
s
,
ar
e
p
r
im
ar
ily
ch
ar
ac
ter
ized
b
y
h
ig
h
e
m
is
s
io
n
s
o
f
C
O2
an
d
o
th
er
p
o
llu
tan
ts
,
u
n
ev
en
d
is
tr
ib
u
tio
n
,
an
d
ex
p
en
s
iv
e
p
r
o
d
u
ctio
n
[
1
]
,
[
2
]
.
Ach
iev
in
g
C
O2
n
eu
tr
ality
is
n
o
w
th
e
m
o
s
t
cr
itical
g
lo
b
al
ac
tiv
ity
in
th
e
f
ig
h
t
ag
ain
s
t
clim
ate
ch
an
g
e,
d
r
iv
i
n
g
all
co
u
n
tr
ies
to
s
witch
to
clea
n
er
en
er
g
y
to
m
ee
t
in
ter
n
atio
n
al
g
o
als
[
3
]
.
Ar
tific
ial
i
n
tellig
en
ce
(
AI
)
h
as
b
ec
o
m
e
a
cr
itical
en
ab
ler
in
o
p
tim
izin
g
r
en
ewa
b
le
en
er
g
y
s
y
s
tem
s
.
T
h
is
r
ev
iew
a
n
s
wer
s
h
o
w
AI
ca
n
o
v
er
c
o
m
e
th
e
i
n
h
er
en
t
v
ar
iab
ilit
y
o
f
r
en
ewa
b
le
s
o
u
r
ce
s
,
esp
ec
ially
s
o
lar
an
d
win
d
,
wh
ich
p
o
s
e
s
ig
n
if
ican
t
ch
allen
g
es
to
g
r
id
s
tab
ilit
y
an
d
ef
f
icie
n
t
en
er
g
y
u
tili
za
tio
n
.
AI
m
itig
a
tes
th
is
b
y
im
p
r
o
v
in
g
en
e
r
g
y
g
e
n
er
atio
n
f
o
r
ec
asts
an
d
o
p
tim
izin
g
s
y
s
tem
o
p
er
atio
n
s
,
th
er
eb
y
e
n
h
an
cin
g
r
eliab
ilit
y
an
d
ef
f
icien
c
y
[
4
]
,
[
5
]
.
AI
-
d
r
iv
en
p
r
e
d
ictiv
e
m
ain
ten
an
ce
f
u
r
th
e
r
en
s
u
r
es
r
o
b
u
s
t
ass
et
m
an
ag
em
en
t
b
y
d
etec
tin
g
eq
u
ip
m
e
n
t
an
o
m
alies
ea
r
ly
,
p
r
ev
e
n
tin
g
d
o
wn
tim
e,
an
d
r
ed
u
cin
g
co
s
ts
[
6
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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20
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6
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276
So
lar
en
er
g
y
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co
llected
t
h
r
o
u
g
h
p
h
o
to
v
o
ltaic
(
PV)
p
an
els,
p
lay
s
a
k
e
y
r
o
le
in
p
r
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v
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s
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ab
le
an
d
r
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n
ewa
b
le
en
er
g
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o
lu
ti
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f
f
e
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in
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en
v
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o
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en
tally
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r
ien
d
ly
an
d
c
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t
-
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f
ec
t
iv
e
alter
n
ativ
e
to
tr
ad
itio
n
al
en
e
r
g
y
s
o
u
r
ce
s
,
t
h
u
s
r
ed
u
cin
g
d
ep
e
n
d
en
ce
o
n
f
o
s
s
il
f
u
els
an
d
co
n
tr
ib
u
tin
g
to
e
n
v
ir
o
n
m
en
ta
l
p
r
o
tectio
n
[
7
]
,
[
8
]
.
W
in
d
en
er
g
y
,
f
o
r
in
s
tan
ce
,
is
a
clea
n
,
f
r
ee
,
an
d
in
ex
h
au
s
tib
le
s
o
u
r
ce
,
o
f
ten
ch
ar
ac
ter
ize
d
b
y
lo
wer
i
n
itial
in
v
estme
n
t
c
o
s
ts
[
9
]
,
[
1
0
]
.
AI
also
p
lay
s
an
im
p
o
r
tan
t
r
o
le
in
in
teg
r
atin
g
r
e
n
ewa
b
les
in
to
p
o
wer
g
r
id
s
.
AI
-
en
h
an
ce
d
s
m
ar
t
g
r
id
s
d
y
n
am
ically
m
an
a
g
e
en
er
g
y
d
is
tr
ib
u
tio
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an
d
s
to
r
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g
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s
u
r
in
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s
tab
le
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u
p
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ly
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esp
ite
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ar
iab
ilit
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[
1
1
]
.
B
ey
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d
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p
tim
izes
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er
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to
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tem
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im
p
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g
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atter
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an
a
g
em
en
t
a
n
d
e
x
te
n
d
in
g
life
s
p
a
n
[
6
]
.
AI
'
s
f
o
r
ec
a
s
tin
g
ab
ilit
y
s
u
p
p
o
r
ts
d
em
an
d
r
esp
o
n
s
e,
b
alan
ci
n
g
g
en
er
atio
n
with
co
n
s
u
m
p
tio
n
,
r
ed
u
cin
g
p
ea
k
lo
ad
s
,
a
n
d
im
p
r
o
v
in
g
e
f
f
icien
cy
[
4
]
.
Op
tim
izin
g
s
y
s
tem
o
p
er
atio
n
s
,
A
I
co
n
tr
ib
u
tes
to
lo
wer
ca
r
b
o
n
em
is
s
io
n
s
an
d
r
ed
u
ce
d
o
p
er
atio
n
al
co
s
ts
,
m
ak
in
g
r
en
ewa
b
le
en
er
g
y
p
r
o
jects
m
o
r
e
s
u
s
tain
ab
le
an
d
f
in
an
cially
v
ia
b
le
[
1
2
]
.
T
h
u
s
,
AI
is
ce
n
tr
al
to
t
h
e
g
lo
b
al
tr
an
s
itio
n
to
war
d
s
clea
n
er
,
m
o
r
e
ef
f
icien
t e
n
er
g
y
p
r
o
d
u
ctio
n
an
d
s
u
s
tai
n
ab
ilit
y
g
o
als.
Fig
u
r
e
1
s
u
m
m
ar
izes
AI
ap
p
li
ca
tio
n
s
in
r
e
n
ewa
b
le
en
e
r
g
y
,
en
co
m
p
ass
in
g
e
n
er
g
y
p
r
ed
ictio
n
,
s
y
s
tem
p
er
f
o
r
m
an
ce
,
g
r
id
in
teg
r
atio
n
,
en
er
g
y
s
to
r
a
g
e,
a
n
d
d
em
an
d
r
esp
o
n
s
e.
AI
'
s
ab
ilit
y
to
p
r
o
ce
s
s
co
m
p
lex
d
atasets
im
p
r
o
v
es
f
o
r
ec
asts
,
o
p
tim
izi
n
g
p
r
o
d
u
ctio
n
an
d
r
ed
u
ci
n
g
in
ef
f
icien
cies
[
1
2
]
.
Pre
d
i
ctiv
e
m
ain
ten
an
ce
m
in
im
izes
d
is
r
u
p
tio
n
s
b
y
i
d
en
tify
in
g
f
ailu
r
es
ea
r
ly
,
e
n
s
u
r
in
g
co
n
s
is
ten
t
p
o
wer
g
en
e
r
atio
n
[
1
3
]
.
I
n
g
r
id
m
an
ag
em
en
t,
A
I
co
o
r
d
in
ates
g
en
er
atio
n
an
d
d
is
tr
ib
u
tio
n
,
m
ain
tain
in
g
b
ala
n
ce
an
d
s
tab
ilit
y
[
1
4
]
.
AI
also
o
p
tim
izes
en
er
g
y
s
to
r
a
g
e
a
n
d
ad
ju
s
ts
p
r
o
d
u
ctio
n
b
ased
o
n
d
em
an
d
,
s
u
p
p
o
r
tin
g
r
e
d
u
ce
d
p
ea
k
lo
a
d
s
an
d
b
alan
ce
d
d
is
tr
ib
u
tio
n
[
1
5
]
.
T
h
ese
ap
p
licatio
n
s
alig
n
with
g
l
o
b
al
ef
f
o
r
ts
to
war
d
s
ca
r
b
o
n
n
eu
tr
ality
an
d
clim
ate
ch
an
g
e
m
itig
atio
n
,
as
AI
to
o
ls
en
h
an
ce
th
e
s
u
s
tain
ab
ilit
y
an
d
ec
o
n
o
m
ic
v
iab
ilit
y
o
f
r
en
ewa
b
le
en
er
g
y
s
y
s
tem
s
.
T
h
r
o
u
g
h
th
ese
a
p
p
lic
atio
n
s
,
AI
is
im
p
o
r
tan
t
f
o
r
a
d
v
an
cin
g
r
en
ewa
b
le
e
n
er
g
y
p
er
f
o
r
m
an
ce
,
m
ak
in
g
it
ce
n
tr
al
to
f
u
t
u
r
e
en
e
r
g
y
s
tr
ateg
ies.
Desp
ite
th
ese
ad
v
an
ce
m
en
ts
,
s
ig
n
if
ican
t
ch
allen
g
es
p
er
s
is
t
t
h
at
lim
it
AI
'
s
f
u
ll
p
o
ten
tial
in
r
en
ewa
b
le
en
er
g
y
.
Key
is
s
u
es
in
clu
d
e
c
o
n
ce
r
n
s
r
elate
d
to
d
ata
q
u
ality
an
d
av
ailab
ilit
y
,
th
e
cr
itical
n
ee
d
f
o
r
m
o
d
el
in
ter
p
r
etab
ilit
y
an
d
tr
u
s
two
r
t
h
in
ess
to
f
ac
ilit
ate
o
p
er
ato
r
a
d
o
p
tio
n
,
an
d
p
er
v
asiv
e
eth
ica
l
an
d
cy
b
er
s
ec
u
r
ity
co
n
ce
r
n
s
with
in
in
ter
co
n
n
ec
te
d
en
er
g
y
in
f
r
astru
ctu
r
es.
Ad
d
r
ess
in
g
th
ese
u
n
r
eso
lv
ed
p
r
o
b
lem
s
f
o
r
m
s
a
co
r
e
f
o
cu
s
o
f
t
h
is
r
ev
iew.
T
h
is
r
ev
iew
a
d
o
p
ts
a
s
tr
u
ctu
r
ed
ap
p
r
o
ac
h
,
p
r
o
v
id
in
g
a
d
eta
iled
an
d
f
o
c
u
s
ed
a
n
aly
s
is
o
f
s
p
ec
if
ic
AI
tech
n
iq
u
es
an
d
th
eir
tr
an
s
f
o
r
m
ativ
e
r
o
le
i
n
o
p
tim
izin
g
s
o
lar
an
d
win
d
en
er
g
y
s
y
s
te
m
s
.
Ou
r
d
is
tin
ctiv
e
co
n
tr
ib
u
tio
n
lies
in
s
y
n
th
esizin
g
th
e
cu
r
r
en
t
s
tate
-
of
-
th
e
-
a
r
t
s
p
ec
if
ically
with
in
s
o
lar
an
d
win
d
co
n
tex
ts
,
id
en
tify
in
g
a
n
d
elab
o
r
atin
g
o
n
cr
itical
ch
allen
g
es
(
d
ata
q
u
ality
,
m
o
d
el
in
ter
p
r
etab
ilit
y
,
cy
b
er
s
ec
u
r
ity
)
,
an
d
o
f
f
er
in
g
f
u
tu
r
e
-
o
r
ien
ted
p
e
r
s
p
ec
tiv
es
an
d
r
ec
o
m
m
en
d
atio
n
s
to
d
r
iv
e
th
e
s
tr
ateg
ic
an
d
ef
f
ec
tiv
e
u
s
e
o
f
av
ailab
le
AI
tech
n
iq
u
es
in
t
h
e
r
en
ewa
b
le
en
er
g
y
s
ec
to
r
.
T
h
i
s
wo
r
k
o
f
f
er
s
a
tar
g
eted
k
n
o
w
led
g
e
b
ase
f
o
r
b
o
th
r
esear
ch
er
s
an
d
p
r
ac
titi
o
n
e
r
s
,
s
h
o
win
g
h
o
w
a
n
ew
f
r
a
m
ewo
r
k
,
s
y
n
t
h
esis
,
an
d
p
er
s
p
ec
tiv
e
will
p
r
o
v
id
e
n
o
v
el
in
s
ig
h
ts
in
to
o
v
er
c
o
m
in
g
t
h
e
v
ar
iab
ilit
y
an
d
o
p
er
atio
n
al
c
o
m
p
lex
ities
o
f
r
en
ewa
b
le
e
n
er
g
y
.
Fig
u
r
e
1
.
Ov
e
r
v
iew
o
f
AI
ap
p
l
icatio
n
s
in
r
en
ewa
b
le
en
e
r
g
y
s
y
s
tem
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
p
l Po
wer
E
n
g
I
SS
N:
2252
-
8
7
9
2
A
r
tifi
cia
l in
tellig
en
ce
fo
r
o
p
timiz
in
g
r
en
ewa
b
le
en
erg
y
s
yste
ms:
tech
n
iq
u
es
,
a
p
p
lica
tio
n
s
,
… (
I
a
n
B
.
B
en
itez
)
277
2.
M
E
T
H
O
DO
L
O
G
Y
T
h
is
n
ar
r
ativ
e
an
d
th
em
ati
c
r
ev
iew
s
y
s
tem
atica
lly
s
y
n
th
esizes
ex
is
tin
g
k
n
o
wled
g
e
o
n
AI
'
s
tr
an
s
f
o
r
m
ativ
e
r
o
le
in
o
p
tim
izin
g
r
en
ewa
b
le
en
er
g
y
s
y
s
tem
s
,
with
a
p
ar
ticu
lar
f
o
cu
s
o
n
s
o
lar
an
d
win
d
en
er
g
y
.
T
h
e
r
esear
ch
g
o
al
was
to
id
en
tify
an
d
ca
teg
o
r
ize
k
ey
AI
tech
n
iq
u
es
an
d
th
eir
a
p
p
licatio
n
s
in
p
o
wer
f
o
r
ec
asti
n
g
,
s
y
s
tem
o
p
tim
izati
o
n
,
a
n
d
p
r
e
d
ictiv
e
m
ain
ten
a
n
c
e;
d
is
cu
s
s
ass
o
ciate
d
ch
allen
g
es
an
d
lim
itatio
n
s
;
an
d
p
r
o
p
o
s
e
f
u
tu
r
e
r
esear
ch
d
ir
ec
tio
n
s
an
d
r
ec
o
m
m
e
n
d
ati
o
n
s
.
T
h
is
ap
p
r
o
ac
h
em
p
h
asizes
tr
an
s
p
ar
en
cy
an
d
r
ep
licab
ilit
y
d
esp
ite
its
n
a
r
r
at
iv
e
n
atu
r
e
,
e
n
s
u
r
in
g
a
c
o
m
p
r
eh
en
s
iv
e
an
d
r
eliab
le
s
y
n
th
es
is
o
f
th
e
liter
atu
r
e,
p
r
o
v
id
i
n
g
th
e
n
ec
ess
ar
y
f
o
u
n
d
atio
n
f
o
r
v
alid
atin
g
t
h
e
m
eth
o
d
s
em
p
lo
y
ed
an
d
c
o
n
f
ir
m
in
g
t
h
e
f
in
d
in
g
s
.
2
.
1
.
L
it
er
a
t
ure
s
ea
rc
h str
a
t
e
g
y
T
h
e
p
r
im
ar
y
d
atab
ase
f
o
r
lite
r
atu
r
e
id
en
tific
atio
n
was
Sco
p
u
s
,
s
elec
ted
f
o
r
its
ex
ten
s
iv
e
co
v
e
r
ag
e
an
d
q
u
ality
co
n
tr
o
l.
T
h
e
s
ea
r
ch
was
co
n
d
u
cted
p
e
r
io
d
ically
u
p
to
th
e
f
in
aliza
tio
n
o
f
th
is
m
an
u
s
cr
ip
t
to
en
s
u
r
e
in
clu
s
io
n
o
f
th
e
m
o
s
t
r
ec
en
t
ad
v
an
ce
m
e
n
ts
.
A
s
tr
u
ctu
r
ed
s
ea
r
ch
s
tr
in
g
co
m
b
in
ed
k
e
y
wo
r
d
s
r
elate
d
to
AI
tech
n
iq
u
es
(
"a
r
tific
ial
in
tellig
en
ce
,
"
"m
ac
h
in
e
lear
n
in
g
,
"
"d
ee
p
lear
n
i
n
g
,
"
"r
ein
f
o
r
ce
m
en
t
lear
n
in
g
,
"
"
n
eu
r
al
n
etwo
r
k
")
,
r
en
ewa
b
le
en
e
r
g
y
t
y
p
es
(
"so
lar
en
er
g
y
,
"
"win
d
e
n
er
g
y
,
"
"p
h
o
to
v
o
ltaic")
,
a
n
d
s
p
ec
if
ic
ap
p
licatio
n
s
(
"o
p
tim
izatio
n
,
"
"f
o
r
ec
asti
n
g
,
"
"p
r
e
d
ictiv
e
m
ain
te
n
an
ce
,
"
"sm
ar
t
g
r
id
"
)
.
B
o
o
lean
o
p
e
r
ato
r
s
(
AND,
OR
)
wer
e
u
s
ed
.
T
h
e
s
ea
r
ch
was lim
ited
to
"a
r
ticle"
an
d
"r
ev
iew
ar
ticle"
d
o
cu
m
e
n
t ty
p
es a
n
d
E
n
g
lis
h
-
lan
g
u
ag
e
p
ap
e
r
s
.
2
.
2
.
I
nclus
io
n a
nd
ex
clus
io
n
cr
it
er
ia
a
nd
s
elec
t
io
n pro
ce
s
s
Stu
d
ies
wer
e
s
elec
ted
b
ased
o
n
s
p
ec
if
ic
in
cl
u
s
io
n
/ex
clu
s
io
n
cr
iter
ia.
I
n
clu
s
io
n
f
o
c
u
s
ed
o
n
p
ee
r
-
r
ev
iewe
d
jo
u
r
n
al
ar
ticles
an
d
r
ev
iew
ar
ticles
d
ir
ec
tly
ad
d
r
ess
in
g
AI
ap
p
licatio
n
s
in
o
p
tim
izin
g
,
f
o
r
ec
asti
n
g
,
o
r
m
ain
tain
in
g
s
o
lar
an
d
/o
r
win
d
en
er
g
y
s
y
s
tem
s
,
p
r
o
v
i
d
in
g
ex
p
er
im
en
tal
o
r
f
ield
-
b
ased
i
n
s
ig
h
ts
.
E
x
clu
s
io
n
f
ilter
ed
o
u
t
s
tu
d
ies
n
o
t
d
ir
ec
tly
ad
d
r
ess
in
g
AI
in
r
en
ewa
b
le
en
er
g
y
,
th
o
s
e
f
o
cu
s
ed
o
n
o
t
h
er
en
e
r
g
y
s
o
u
r
ce
s
(
u
n
less
p
ar
t
o
f
a
h
y
b
r
id
s
y
s
tem
ex
p
licitly
in
clu
d
i
n
g
s
o
lar
o
r
win
d
)
,
n
o
n
-
p
ee
r
-
r
ev
iewe
d
lit
er
atu
r
e,
an
d
p
a
p
er
s
s
o
lely
d
is
cu
s
s
in
g
h
ar
d
wa
r
e
d
esig
n
with
o
u
t
s
ig
n
if
ican
t
AI
in
teg
r
atio
n
.
T
h
e
two
-
s
ta
g
e
s
elec
tio
n
p
r
o
ce
s
s
in
v
o
lv
ed
in
itial
titl
e
an
d
ab
s
tr
ac
t
s
cr
ee
n
in
g
f
o
r
r
elev
a
n
ce
,
f
o
llo
wed
b
y
f
u
ll
-
te
x
t
r
ev
iew
o
f
s
elec
ted
ar
ticles
to
co
n
f
ir
m
c
o
m
p
lian
ce
.
2
.
3
.
Da
t
a
e
x
t
ra
ct
i
o
n a
nd
t
hem
a
t
ic
a
na
ly
s
is
Data
ex
tr
ac
tio
n
f
o
c
u
s
ed
o
n
g
ath
er
in
g
k
e
y
in
f
o
r
m
atio
n
:
s
p
e
cif
ic
AI
tech
n
iq
u
e
(
s
)
,
r
en
ewa
b
le
en
er
g
y
ty
p
e,
ap
p
licatio
n
ar
ea
,
k
e
y
f
i
n
d
in
g
s
,
c
h
allen
g
es,
an
d
p
r
o
p
o
s
ed
f
u
tu
r
e
d
ir
ec
tio
n
s
.
T
h
is
d
ata
th
en
u
n
d
er
wen
t
th
em
atic
an
aly
s
is
,
in
v
o
lv
in
g
f
am
iliar
izatio
n
th
r
o
u
g
h
r
ea
d
i
n
g
,
in
itial
co
d
in
g
f
o
r
r
ec
u
r
r
in
g
co
n
ce
p
ts
,
g
r
o
u
p
in
g
s
im
ilar
co
d
es
in
to
b
r
o
ad
er
t
h
e
m
es
(
e.
g
.
,
"
d
ee
p
lear
n
in
g
f
o
r
f
o
r
ec
asti
n
g
,
"
"c
h
allen
g
es
in
d
at
a
q
u
ality
,
"
"AI
-
I
o
T
i
n
teg
r
atio
n
")
,
an
d
r
ef
in
in
g
th
ese
th
em
es
f
o
r
ac
cu
r
ac
y
.
T
h
e
r
ev
iew's
f
in
d
in
g
s
ar
e
s
tr
u
ctu
r
ed
ar
o
u
n
d
th
ese
id
en
tifie
d
th
em
es,
p
r
o
v
id
in
g
a
co
h
er
en
t
n
ar
r
ativ
e
th
at
s
y
n
th
esizes
ad
v
an
ce
m
en
ts
,
ch
allen
g
es,
an
d
f
u
tu
r
e
d
ir
ec
tio
n
s
,
o
f
f
er
in
g
v
alu
ab
le
i
n
s
ig
h
ts
f
o
r
r
esear
ch
e
r
s
an
d
p
r
a
ctitio
n
er
s
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
3
.
1
.
O
v
er
v
iew
o
f
AI
t
ec
hn
iq
ues
in re
newa
b
le
ener
g
y
3
.
1
.
1
.
M
a
chine
l
ea
rning
Ma
ch
in
e
lear
n
in
g
(
ML
)
o
p
tim
izes
r
en
ewa
b
le
en
er
g
y
s
y
s
tem
s
b
y
en
h
a
n
cin
g
p
r
ed
ictiv
e
ac
c
u
r
ac
y
an
d
d
etec
tin
g
an
o
m
alies.
Su
p
er
v
is
ed
lear
n
in
g
im
p
r
o
v
es
s
o
lar
an
d
win
d
en
e
r
g
y
g
e
n
er
atio
n
p
r
ed
ictio
n
s
,
wh
ile
u
n
s
u
p
er
v
is
ed
lear
n
in
g
id
e
n
tifie
s
ir
r
eg
u
lar
co
n
s
u
m
p
tio
n
p
atter
n
s
,
co
n
tr
i
b
u
tin
g
to
g
r
id
s
tab
ilit
y
.
R
ein
f
o
r
ce
m
e
n
t
lear
n
in
g
(
R
L
)
o
p
tim
izes
co
n
tr
o
l
s
tr
ateg
ies
in
d
y
n
am
ic
g
r
i
d
en
v
ir
o
n
m
en
ts
,
im
p
r
o
v
in
g
e
f
f
i
cien
cy
an
d
r
ed
u
cin
g
s
to
r
ag
e
n
ee
d
s
[
1
6
]
.
T
h
ese
m
eth
o
d
s
ef
f
ec
tiv
ely
m
an
ag
e
e
n
er
g
y
v
ar
ia
b
ilit
y
,
en
ab
lin
g
in
f
o
r
m
ed
p
r
o
d
u
ctio
n
an
d
d
is
tr
ib
u
tio
n
d
ec
is
io
n
s
.
I
n
p
r
ac
tical
ap
p
licatio
n
s
,
ML
s
ig
n
if
ican
tly
im
p
r
o
v
es
r
en
e
wab
le
en
er
g
y
o
u
tp
u
t.
Gen
etic
a
lg
o
r
ith
m
s
(
GA)
in
cr
ea
s
e
s
o
lar
PV o
u
tp
u
t
b
y
u
p
to
1
5
%,
an
d
R
L
o
p
tim
izes
m
ax
im
u
m
p
o
wer
p
o
i
n
t tr
a
ck
in
g
(
MPPT)
ev
e
n
u
n
d
er
ch
allen
g
in
g
co
n
d
itio
n
s
[
1
7
]
,
[
1
8
]
.
Neu
r
al
n
etwo
r
k
s
(
N
N)
im
p
r
o
v
e
win
d
tu
r
b
in
e
ef
f
icien
cy
b
y
1
2
%
[
1
7
]
.
T
h
e
r
am
if
icatio
n
s
o
f
th
ese
a
d
v
an
ce
m
e
n
ts
ar
e
s
ig
n
if
ican
t:
th
ey
n
o
t
o
n
ly
en
h
a
n
ce
en
er
g
y
y
ield
an
d
en
a
b
le
o
p
er
atio
n
clo
s
er
to
th
e
o
r
etica
l
m
ax
im
u
m
ef
f
icien
cies,
b
u
t
a
ls
o
tr
an
s
late
in
to
tan
g
ib
le
ec
o
n
o
m
ic
s
av
in
g
s
a
n
d
r
ed
u
ce
d
r
elian
ce
o
n
n
o
n
-
r
e
n
e
wab
le
b
ac
k
u
p
s
y
s
tem
s
,
d
ir
ec
tly
co
n
tr
ib
u
tin
g
to
s
u
s
tain
ab
ilit
y
g
o
als.
Desp
ite
p
r
o
g
r
ess
,
ML
im
p
lem
en
tatio
n
in
r
en
ewa
b
le
en
er
g
y
f
ac
es
lim
itatio
n
s
.
Mo
d
els
m
ay
s
tr
u
g
g
le
with
u
n
p
r
ed
ictab
le
r
ea
l
-
wo
r
ld
co
n
d
itio
n
s
,
r
aisi
n
g
r
o
b
u
s
tn
ess
co
n
ce
r
n
s
.
C
h
allen
g
es
ar
is
e
f
r
o
m
ML
’
s
r
elian
ce
o
n
h
ig
h
-
q
u
ality
d
ata
an
d
co
m
p
lex
in
teg
r
atio
n
in
to
ex
is
tin
g
in
f
r
astru
ctu
r
e.
Mo
r
e
o
v
er
,
th
e
tech
n
ical
d
ep
th
o
f
th
ese
m
o
d
els
ca
n
also
h
in
d
er
in
ter
p
r
etab
ilit
y
,
m
a
k
in
g
it
d
i
f
f
icu
lt
f
o
r
o
p
er
ato
r
s
to
tr
u
s
t
AI
-
d
r
iv
en
d
ec
is
io
n
s
[
1
9
]
.
T
h
is
'
b
lack
b
o
x
'
n
atu
r
e
is
a
cr
itica
l
b
ar
r
ier
to
wid
esp
r
ea
d
ad
o
p
tio
n
in
h
ig
h
-
s
tak
es
en
er
g
y
ap
p
licatio
n
s
wh
er
e
tr
an
s
p
ar
e
n
t
d
ec
is
io
n
-
m
ak
in
g
is
p
a
r
am
o
u
n
t.
Fu
tu
r
e
a
d
v
an
ce
m
e
n
ts
lik
e
ex
p
lain
a
b
le
AI
(
XAI
)
a
n
d
e
d
g
e
co
m
p
u
tin
g
o
f
f
e
r
p
r
o
m
is
in
g
s
o
l
u
tio
n
s
to
ad
d
r
ess
th
ese
ch
allen
g
es in
r
en
ewa
b
le
en
e
r
g
y
a
p
p
li
ca
tio
n
s
[
2
0
]
.
T
h
ese
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
9
2
I
n
t J Ap
p
l Po
wer
E
n
g
,
Vo
l.
1
5
,
No
.
1
,
Ma
r
ch
20
2
6
:
275
-
2
8
8
278
in
n
o
v
atio
n
s
aim
to
b
u
ild
tr
u
s
t
an
d
ac
ce
s
s
ib
ilit
y
,
ac
ce
le
r
atin
g
AI
ad
o
p
tio
n
in
th
e
tr
an
s
itio
n
to
war
d
s
a
s
u
s
tain
ab
le
en
er
g
y
f
u
tu
r
e,
m
ak
in
g
th
em
h
ig
h
ly
v
alu
ab
le
f
o
r
f
u
tu
r
e
p
r
ac
tical
d
ep
lo
y
m
en
t.
3
.
1
.
2
.
Dee
p
l
ea
rning
Dee
p
lear
n
in
g
(
DL
)
is
cr
itical
f
o
r
f
o
r
ec
asti
n
g
an
d
co
n
tr
o
l
in
r
en
ewa
b
le
en
er
g
y
s
y
s
tem
s
,
d
u
e
to
its
ab
ilit
y
to
m
o
d
el
c
o
m
p
lex
,
n
o
n
lin
ea
r
r
elatio
n
s
h
ip
s
.
L
o
n
g
s
h
o
r
t
-
ter
m
m
e
m
o
r
y
(
L
STM
)
n
etwo
r
k
s
e
x
ce
l
at
tem
p
o
r
al
d
ep
en
d
en
cies
in
lo
a
d
f
o
r
ec
asti
n
g
[
2
1
]
,
wh
ile
co
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
s
(
C
NNs)
s
u
it
s
p
atial
d
ata
an
aly
s
is
[
2
2
]
.
A
k
ey
co
m
p
ar
i
s
o
n
r
ev
ea
ls
th
at
h
y
b
r
id
C
NN
-
L
STM
m
o
d
els
o
f
ten
lev
er
a
g
e
b
o
th
tem
p
o
r
al
an
d
s
p
atial
f
ea
tu
r
es,
o
f
f
er
in
g
im
p
r
o
v
ed
p
e
r
f
o
r
m
an
ce
o
v
er
s
in
g
le
-
ar
ch
itectu
r
e
m
o
d
els
b
y
ca
p
tu
r
in
g
r
ich
er
p
atter
n
s
[
2
1
]
.
T
h
ese
m
o
d
els
p
r
o
ce
s
s
v
ast
d
ataset
s
(
h
is
to
r
ical
co
n
s
u
m
p
tio
n
,
wea
th
er
p
atter
n
s
)
,
ef
f
ec
tiv
ely
f
o
r
ec
asti
n
g
en
er
g
y
lo
a
d
an
d
PV
p
o
wer
p
r
o
d
u
ctio
n
,
f
ac
ilit
atin
g
b
etter
r
e
n
ewa
b
le
en
er
g
y
in
teg
r
atio
n
a
n
d
en
h
a
n
ce
d
p
o
wer
s
y
s
tem
r
eliab
ilit
y
co
m
p
ar
ed
to
tr
ad
itio
n
al
s
tatis
tical
m
o
d
els
b
y
ca
p
tu
r
in
g
n
o
n
-
lin
ea
r
r
elatio
n
s
h
ip
s
m
o
r
e
ef
f
ec
tiv
ely
[
2
1
]
,
[2
2
]
.
B
ey
o
n
d
f
o
r
ec
asti
n
g
,
DL
o
p
tim
izes
co
n
tr
o
l
s
tr
ateg
ies
b
y
p
r
o
v
id
in
g
ac
cu
r
ate
s
h
o
r
t
-
ter
m
an
d
lo
n
g
-
ter
m
f
o
r
ec
asts
,
en
ab
lin
g
p
r
ec
is
e
b
al
an
cin
g
o
f
s
u
p
p
ly
a
n
d
d
em
an
d
with
in
s
m
ar
t
g
r
i
d
s
.
T
h
is
is
ess
en
tial
f
o
r
s
y
s
tem
s
with
h
ig
h
i
n
ter
m
itten
t
r
en
ew
ab
le
p
en
etr
atio
n
[2
3
]
.
DL
-
b
a
s
ed
co
n
tr
o
l
m
eth
o
d
s
im
p
r
o
v
e
g
r
id
s
tab
ilit
y
a
n
d
o
p
er
atio
n
al
ef
f
icien
cy
b
y
m
an
ag
in
g
e
n
er
g
y
f
lo
ws
ef
f
ec
tiv
el
y
in
c
o
m
p
lex
s
y
s
tem
s
with
elec
tr
ic
v
eh
icles
an
d
d
is
tr
ib
u
ted
en
er
g
y
r
eso
u
r
ce
s
[2
4
]
.
Ho
wev
er
,
DL
d
ep
lo
y
m
en
t
f
ac
es
ch
allen
g
es.
I
n
ter
p
r
etab
ilit
y
is
a
s
ig
n
if
ican
t
is
s
u
e;
th
ese
"b
lack
b
o
x
"
m
o
d
els
lim
it
tr
u
s
t
am
o
n
g
en
g
in
ee
r
s
an
d
g
r
id
o
p
er
ato
r
s
wh
o
n
ee
d
tr
an
s
p
ar
en
t
o
u
tp
u
ts
f
o
r
r
ea
l
-
tim
e
d
ec
is
io
n
s
[2
5
]
.
T
h
is
lac
k
o
f
t
r
an
s
p
ar
en
c
y
is
a
m
ajo
r
im
p
e
d
im
en
t
t
o
th
eir
wid
esp
r
ea
d
ad
o
p
tio
n
in
cr
i
tical
in
f
r
astru
ctu
r
e
,
wh
er
e
ac
co
u
n
tab
ilit
y
an
d
u
n
d
er
s
tan
d
i
n
g
m
o
d
el
r
atio
n
ale
ar
e
im
p
o
r
tan
t,
r
ep
r
esen
tin
g
a
s
ig
n
if
ican
t
r
am
if
icatio
n
.
M
o
d
el
p
e
r
f
o
r
m
a
n
ce
h
ea
v
ily
d
e
p
en
d
s
o
n
d
ata
q
u
ality
a
n
d
a
v
ailab
ilit
y
;
in
a
d
eq
u
ate
d
ata
lim
its
ac
cu
r
ac
y
an
d
g
en
er
aliza
tio
n
[2
6
]
.
Hig
h
co
m
p
u
tatio
n
al
d
e
m
an
d
s
also
h
in
d
er
s
ca
lab
ilit
y
.
T
h
is
im
p
lies
th
at
f
u
tu
r
e
d
e
v
elo
p
m
e
n
ts
ar
e
lik
ely
to
f
o
cu
s
o
n
cr
ea
tin
g
m
o
r
e
i
n
ter
p
r
etab
le
m
o
d
els,
en
h
a
n
ce
d
d
ata
p
r
o
ce
s
s
in
g
,
an
d
ed
g
e
c
o
m
p
u
tin
g
to
r
e
d
u
ce
c
o
m
p
u
tatio
n
al
d
em
a
n
d
s
,
in
cl
u
d
in
g
lig
h
tweig
h
t,
s
ca
lab
le
n
e
u
r
al
n
etwo
r
k
s
(
e.
g
.
,
T
in
y
ML
)
tailo
r
ed
f
o
r
ed
g
e
d
ep
lo
y
m
en
t
[2
4
]
,
[
2
7
]
.
T
h
ese
ad
v
an
ce
m
e
n
ts
ar
e
ess
en
tial
f
o
r
DL
to
e
n
h
an
ce
ef
f
icien
cy
,
r
elia
b
ilit
y
,
an
d
s
u
s
t
ain
ab
ilit
y
,
d
ir
ec
tly
a
d
d
r
ess
in
g
wh
at
will c
o
m
e
in
h
an
d
y
in
th
e
f
u
tu
r
e.
3
.
1
.
3
.
Reinf
o
rc
em
ent
l
ea
rnin
g
R
ein
f
o
r
ce
m
en
t
lear
n
in
g
(
R
L
)
h
as
s
h
o
wn
s
ig
n
if
ican
t
p
r
o
m
is
e
in
th
e
ad
ap
tiv
e
m
an
a
g
em
en
t
o
f
r
en
ewa
b
le
en
e
r
g
y
s
y
s
tem
s
,
p
r
o
v
id
in
g
r
o
b
u
s
t
s
o
lu
tio
n
s
f
o
r
h
an
d
lin
g
u
n
ce
r
tain
t
y
an
d
d
y
n
a
m
ic
en
v
ir
o
n
m
en
ts
.
I
n
th
e
co
n
tex
t
o
f
en
e
r
g
y
m
an
ag
e
m
en
t
with
in
b
u
ild
in
g
s
,
R
L
m
o
d
els,
s
u
ch
as
th
o
s
e
u
tili
zin
g
th
e
u
p
p
er
co
n
f
id
en
ce
b
o
u
n
d
(
UC
B
1
)
alg
o
r
ith
m
,
h
a
v
e
b
ee
n
ef
f
ec
tiv
ely
ap
p
lied
t
o
m
an
ag
e
en
er
g
y
c
o
n
s
u
m
p
ti
o
n
,
g
e
n
er
atio
n
,
an
d
s
to
r
ag
e.
T
h
ese
m
o
d
els
d
em
o
n
s
tr
ate
s
u
p
er
io
r
ad
a
p
tab
ilit
y
to
o
n
g
o
in
g
e
n
v
ir
o
n
m
en
tal
c
h
an
g
es,
o
u
tp
er
f
o
r
m
i
n
g
tr
ad
itio
n
al
m
eth
o
d
s
lik
e
E
X
P3
an
d
s
im
p
ler
R
L
alg
o
r
it
h
m
s
in
s
elec
tin
g
th
e
b
est
ac
tio
n
s
f
o
r
en
er
g
y
o
p
tim
izatio
n
[2
8
]
.
T
h
is
ad
ap
ta
b
ilit
y
is
p
ar
ticu
lar
l
y
im
p
o
r
tan
t
in
t
h
e
f
ac
e
o
f
f
lu
ctu
atin
g
en
er
g
y
g
en
er
atio
n
f
r
o
m
r
en
ewa
b
les
lik
e
s
o
lar
an
d
win
d
,
wh
er
e
R
L
-
b
ased
m
o
d
els
ca
n
co
n
tin
u
o
u
s
ly
lear
n
a
n
d
ad
j
u
s
t
th
eir
s
tr
ateg
ies
to
o
p
tim
ize
en
er
g
y
u
s
e.
Similar
ly
,
in
th
e
r
ea
lm
o
f
m
icr
o
g
r
id
m
an
ag
em
e
n
t,
R
L
alg
o
r
ith
m
s
s
u
ch
as
d
ee
p
Q
-
n
etwo
r
k
s
(
DQN)
,
p
r
o
x
im
al
p
o
licy
o
p
tim
izatio
n
(
PP
O)
,
an
d
twin
d
elay
ed
d
ee
p
d
eter
m
in
i
s
tic
p
o
licy
g
r
ad
ien
t
(
T
D3
)
h
av
e
p
r
o
v
en
v
alu
a
b
le.
A
cr
itical
in
ter
p
r
etatio
n
o
f
th
ese
R
L
ap
p
r
o
ac
h
es
r
ev
ea
ls
th
eir
ad
v
an
tag
es
o
v
e
r
tr
ad
itio
n
al
m
eth
o
d
s
lik
e
r
u
le
-
b
ased
an
d
m
o
d
el
p
r
ed
ictiv
e
co
n
tr
o
l
(
MPC
)
,
p
ar
ticu
lar
l
y
in
m
an
a
g
in
g
th
e
co
m
p
lex
ities
o
f
en
e
r
g
y
f
lo
ws
with
in
m
icr
o
g
r
id
s
d
u
e
to
t
h
eir
ab
ilit
y
t
o
lear
n
o
p
tim
al
p
o
lici
es
with
o
u
t
e
x
p
licit
s
y
s
tem
m
o
d
els an
d
ad
a
p
t to
u
n
f
o
r
eseen
c
h
an
g
es
[
29
]
.
Ad
v
an
ce
d
ap
p
licatio
n
s
o
f
R
L
in
r
en
ewa
b
le
en
er
g
y
in
cl
u
d
e
m
u
lti
-
ag
en
t
r
ei
n
f
o
r
ce
m
e
n
t
lear
n
in
g
(
MA
R
L
)
f
r
am
ewo
r
k
s
,
wh
ich
allo
w
f
o
r
th
e
au
to
n
o
m
o
u
s
m
an
ag
em
en
t
o
f
r
en
ewa
b
le
e
n
er
g
y
m
icr
o
g
r
id
s
.
B
y
u
s
in
g
m
u
ltip
le
ag
e
n
ts
th
at
lear
n
an
d
ad
ap
t
to
d
if
f
e
r
en
t
p
a
r
ts
o
f
th
e
s
y
s
tem
,
MA
R
L
s
ig
n
if
ic
an
tly
en
h
a
n
ce
s
g
r
id
r
eliab
ilit
y
,
en
er
g
y
e
f
f
icien
cy
,
an
d
co
s
t
r
ed
u
ctio
n
co
m
p
ar
e
d
to
tr
ad
itio
n
al
r
u
le
-
b
ased
ap
p
r
o
ac
h
es
[3
0
]
.
T
h
e
r
am
if
icatio
n
s
o
f
th
is
d
ec
en
tr
alize
d
ap
p
r
o
ac
h
ar
e
p
r
o
f
o
u
n
d
,
as
it
i
s
esp
ec
ial
ly
ad
v
an
tag
eo
u
s
in
co
m
p
lex
,
d
is
tr
ib
u
ted
en
e
r
g
y
s
y
s
tem
s
wh
er
e
r
ea
l
-
tim
e
co
o
r
d
in
atio
n
a
m
o
n
g
d
if
f
e
r
en
t
e
n
er
g
y
s
o
u
r
ce
s
an
d
co
n
s
u
m
er
s
is
r
eq
u
ir
ed
to
m
ai
n
tain
s
tab
ilit
y
an
d
o
p
tim
ize
r
eso
u
r
ce
allo
ca
ti
o
n
,
lead
in
g
to
m
o
r
e
r
esil
ien
t
an
d
ef
f
icien
t
g
r
id
s
.
Ad
d
itio
n
ally
,
R
L
h
as
p
r
o
v
en
ef
f
ec
tiv
e
in
e
n
er
g
y
s
to
r
ag
e
o
p
tim
izatio
n
,
with
s
tr
ateg
ies
lik
e
r
ein
f
o
r
ce
m
en
t
lear
n
in
g
-
o
p
tim
al
p
o
wer
f
lo
w
(
RL
-
OPF
)
b
ein
g
u
s
ed
to
m
ax
im
ize
s
to
r
ag
e
ca
p
ac
ity
in
p
o
wer
s
y
s
tem
s
.
T
h
ese
m
eth
o
d
s
e
x
ce
l
in
s
itu
atio
n
s
w
h
er
e
ac
c
u
r
ate
s
to
ch
asti
c
m
o
d
el
s
ar
e
u
n
av
ailab
le,
u
s
in
g
R
L
’
s
f
lex
ib
ilit
y
to
h
an
d
le
r
ea
l
-
tim
e
v
ar
iatio
n
s
in
g
r
id
c
o
n
d
itio
n
s
[3
1
]
.
Fo
r
b
u
ild
in
g
en
er
g
y
m
an
ag
e
m
en
t,
m
eth
o
d
s
lik
e
th
e
s
o
f
t
ac
to
r
-
cr
itic
co
m
b
in
ed
with
tr
an
s
f
o
r
m
er
d
ee
p
n
eu
r
al
n
etwo
r
k
s
h
a
v
e
s
h
o
wn
s
u
cc
ess
in
co
n
tr
o
llin
g
h
ea
t
p
u
m
p
s
a
n
d
th
er
m
al
s
to
r
ag
e,
lead
in
g
to
b
e
tter
en
er
g
y
ef
f
icien
cy
th
a
n
r
u
l
e
-
b
ased
ap
p
r
o
ac
h
es
[3
2
]
.
I
n
z
er
o
-
en
er
g
y
h
o
u
s
es,
m
o
d
el
-
b
ased
R
L
ap
p
r
o
ac
h
es
s
u
ch
as
DQN
an
d
d
ee
p
d
eter
m
in
is
tic
p
o
licy
g
r
ad
ien
t
(
DDPG
)
h
av
e
b
ee
n
in
s
tr
u
m
en
tal
in
o
p
tim
izin
g
P
V
g
en
er
atio
n
u
s
ag
e,
ac
h
iev
i
n
g
f
ast
co
n
v
er
g
en
ce
an
d
co
s
t
-
ef
f
ec
tiv
e
en
er
g
y
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
p
l Po
wer
E
n
g
I
SS
N:
2252
-
8
7
9
2
A
r
tifi
cia
l in
tellig
en
ce
fo
r
o
p
timiz
in
g
r
en
ewa
b
le
en
erg
y
s
yste
ms:
tech
n
iq
u
es
,
a
p
p
lica
tio
n
s
,
… (
I
a
n
B
.
B
en
itez
)
279
m
an
ag
em
en
t,
with
DDPG
s
tan
d
in
g
o
u
t
f
o
r
its
ab
ilit
y
to
m
ax
im
ize
s
elf
-
co
n
s
u
m
p
tio
n
a
n
d
s
elf
-
s
u
f
f
icien
c
y
r
atio
s
[3
3
]
.
Desp
ite
R
L
’
s
p
o
ten
tial,
its
im
p
lem
en
tatio
n
in
r
e
n
ewa
b
le
en
er
g
y
s
y
s
tem
s
f
ac
es
ce
r
tain
ch
allen
g
es,
s
u
ch
as
th
e
r
eq
u
ir
em
en
t
f
o
r
ex
ten
s
iv
e
tr
ain
in
g
d
ata,
h
ig
h
co
m
p
u
tatio
n
al
d
e
m
an
d
s
,
an
d
p
r
o
lo
n
g
ed
tr
ain
in
g
tim
es
n
ee
d
ed
to
lear
n
o
p
tim
al
s
tr
ateg
ies
in
co
m
p
lex
,
n
o
n
-
lin
ea
r
en
v
ir
o
n
m
e
n
ts
.
Su
ch
f
ac
to
r
s
m
ay
lim
it
d
ep
lo
y
m
e
n
t
in
r
eso
u
r
ce
-
c
o
n
s
tr
ain
ed
s
ettin
g
s
.
T
h
ese
lim
itatio
n
s
p
r
esen
t
s
ig
n
if
ican
t
h
u
r
d
les
to
b
r
o
a
d
er
ad
o
p
tio
n
,
an
d
th
eir
r
eso
lu
tio
n
is
cr
itical
f
o
r
R
L
to
s
ca
le
ef
f
ec
tiv
ely
.
Fu
tu
r
e
r
esear
ch
co
u
ld
f
o
cu
s
o
n
im
p
r
o
v
in
g
in
ter
p
r
etab
ilit
y
to
f
o
s
ter
g
r
ea
t
er
tr
u
s
t
am
o
n
g
en
e
r
g
y
o
p
e
r
at
o
r
s
,
ex
p
lo
r
in
g
d
ata
-
e
f
f
icien
t
R
L
alg
o
r
ith
m
s
an
d
ed
g
e
co
m
p
u
tin
g
to
r
e
d
u
ce
c
o
m
p
u
tatio
n
al
lo
a
d
,
in
clu
d
in
g
o
f
f
lin
e
an
d
f
ed
e
r
ated
R
L
f
o
r
d
e
ce
n
tr
alize
d
,
p
r
iv
ac
y
p
r
eser
v
in
g
p
o
licy
lear
n
in
g
s
u
itab
le
f
o
r
r
ea
l
-
wo
r
ld
en
er
g
y
s
y
s
tem
s
[3
0
]
,
[
3
1
]
,
[
3
3
]
.
B
y
ad
d
r
ess
in
g
th
ese
ch
allen
g
es,
R
L
h
as
th
e
p
o
ten
tial
to
f
u
r
th
er
en
h
an
ce
th
e
e
f
f
icien
cy
an
d
s
u
s
tain
ab
ilit
y
o
f
r
en
ewa
b
le
en
er
g
y
s
y
s
tem
s
,
m
ak
in
g
it
ad
ap
tab
le
to
th
e
ev
o
lv
in
g
d
e
m
an
d
s
o
f
th
e
en
er
g
y
m
ar
k
et
an
d
en
s
u
r
in
g
its
u
tili
ty
in
f
u
tu
r
e
en
er
g
y
i
n
f
r
astru
ctu
r
e.
3
.
1
.
4
.
H
y
brid
m
o
dels
Hy
b
r
id
m
o
d
els,
w
h
ich
in
te
g
r
a
te
AI
with
p
h
y
s
ical
m
o
d
els,
o
f
f
er
a
p
o
we
r
f
u
l
ap
p
r
o
ac
h
f
o
r
i
m
p
r
o
v
i
n
g
th
e
ac
cu
r
ac
y
an
d
ef
f
icien
c
y
o
f
r
en
ewa
b
le
en
e
r
g
y
p
r
e
d
ictio
n
s
an
d
o
p
tim
izatio
n
.
B
y
le
v
er
ag
i
n
g
th
e
s
tr
en
g
th
s
o
f
b
o
th
AI
tech
n
iq
u
es
—
s
u
ch
as
m
ac
h
in
e
lear
n
in
g
a
n
d
d
ee
p
l
ea
r
n
in
g
—
an
d
tr
ad
itio
n
al
p
h
y
s
ical
m
o
d
els,
th
ese
s
y
s
tem
s
ca
n
d
eliv
er
m
o
r
e
p
r
ec
is
e
en
er
g
y
f
o
r
ec
asts
an
d
b
etter
r
eso
u
r
ce
m
an
ag
e
m
en
t.
Fo
r
ex
am
p
le,
th
e
Ku
wait
R
en
ewa
b
le
E
n
er
g
y
Pre
d
ictio
n
Sy
s
tem
(
KR
E
PS
)
co
m
b
in
es
n
u
m
er
ical
wea
th
er
p
r
ed
icti
o
n
with
s
tatis
tica
l
lear
n
in
g
m
o
d
els
an
d
an
a
n
alo
g
en
s
em
b
le
a
p
p
r
o
ac
h
,
p
r
o
v
id
i
n
g
ac
cu
r
ate
s
h
o
r
t
-
ter
m
an
d
lo
n
g
-
ter
m
f
o
r
ec
asts
f
o
r
en
er
g
y
p
r
o
d
u
ctio
n
[3
4
]
.
T
h
is
b
len
d
o
f
d
ata
-
d
r
iv
en
in
s
ig
h
ts
an
d
p
h
y
s
ically
b
ased
m
o
d
els
allo
ws
f
o
r
a
m
o
r
e
n
u
an
ce
d
u
n
d
er
s
tan
d
in
g
o
f
co
m
p
lex
en
e
r
g
y
s
y
s
tem
s
,
r
e
p
r
esen
tin
g
a
k
ey
a
d
v
an
tag
e
an
d
m
ak
in
g
h
y
b
r
i
d
m
o
d
els
p
ar
ticu
lar
ly
v
alu
a
b
le
in
s
ce
n
ar
io
s
wh
er
e
v
ar
iab
ilit
y
in
r
en
ewa
b
le
en
er
g
y
g
en
er
atio
n
p
o
s
es
s
ig
n
if
ican
t
ch
allen
g
es
d
u
e
to
t
h
eir
ab
ilit
y
to
in
co
r
p
o
r
ate
p
h
y
s
ical
co
n
s
tr
ain
ts
an
d
k
n
o
wn
s
y
s
tem
b
eh
av
io
r
,
lea
d
in
g
t
o
m
o
r
e
r
o
b
u
s
t a
n
d
r
eliab
le
p
r
e
d
i
ctio
n
s
.
On
e
o
f
th
e
k
ey
ad
v
a
n
tag
es
o
f
h
y
b
r
id
m
o
d
els
is
th
eir
ab
ilit
y
to
o
p
tim
ize
en
er
g
y
s
y
s
tem
s
th
r
o
u
g
h
th
e
in
teg
r
atio
n
o
f
a
d
v
an
ce
d
AI
alg
o
r
ith
m
s
with
tr
ad
itio
n
al
m
ath
em
atica
l
f
r
am
ewo
r
k
s
.
T
ec
h
n
iq
u
es
s
u
ch
as
ar
tific
ial
n
eu
r
al
n
etwo
r
k
s
(
A
NN)
an
d
L
STM
n
etwo
r
k
s
a
r
e
o
f
te
n
c
o
m
b
in
e
d
with
cla
s
s
ical
m
eth
o
d
s
to
s
tr
ea
m
lin
e
th
e
o
p
tim
izatio
n
p
r
o
ce
s
s
,
r
ed
u
cin
g
c
o
m
p
u
tatio
n
al
b
u
r
d
en
s
w
h
ile
m
ain
tain
in
g
a
cc
u
r
ac
y
[3
5
]
,
[
3
6
]
.
T
h
is
ap
p
r
o
ac
h
is
an
im
p
o
r
tan
t
in
ter
p
r
etatio
n
o
f
t
h
eir
u
tili
ty
:
it
en
ab
les
f
ast
an
d
p
r
ec
is
e
p
r
ed
ictio
n
s
o
f
en
er
g
y
p
r
o
d
u
ctio
n
,
w
h
ich
is
cr
itical
f
o
r
r
ea
l
-
tim
e
e
n
er
g
y
m
an
a
g
em
en
t
in
r
en
ewa
b
le
e
n
er
g
y
s
y
s
tem
s
,
o
f
f
er
in
g
a
s
u
p
er
io
r
b
alan
ce
o
f
ac
c
u
r
ac
y
a
n
d
co
m
p
u
tatio
n
al
ef
f
icien
c
y
c
o
m
p
ar
ed
to
p
u
r
el
y
d
ata
-
d
r
iv
e
n
o
r
p
u
r
el
y
p
h
y
s
ics
-
b
ased
m
o
d
els.
Ad
d
itio
n
ally
,
AI
-
d
r
iv
en
m
o
d
els
with
in
th
es
e
h
y
b
r
i
d
f
r
am
ew
o
r
k
s
ca
n
p
r
o
ce
s
s
lar
g
e
d
atasets
,
in
clu
d
in
g
wea
th
er
f
o
r
ec
asts
an
d
h
is
to
r
ical
p
r
o
d
u
ctio
n
r
e
co
r
d
s
,
to
f
in
e
-
t
u
n
e
en
er
g
y
s
y
s
tem
p
er
f
o
r
m
a
n
ce
lead
in
g
to
r
ed
u
ce
d
en
e
r
g
y
was
te
an
d
im
p
r
o
v
ed
o
v
er
all
ef
f
ici
en
cy
.
B
y
u
tili
zin
g
s
to
ch
asti
c
u
n
ce
r
tain
ty
an
aly
s
is
an
d
s
tatis
tical
m
eth
o
d
s
,
th
i
s
also
en
h
an
ce
s
th
e
r
o
b
u
s
tn
ess
o
f
en
er
g
y
p
r
e
d
ictio
n
s
,
en
s
u
r
in
g
r
eliab
le
p
er
f
o
r
m
an
ce
ev
e
n
u
n
d
er
f
lu
ct
u
atin
g
en
v
ir
o
n
m
e
n
tal
co
n
d
itio
n
s
[3
7
]
.
T
h
e
ad
ap
tab
ilit
y
o
f
h
y
b
r
id
m
o
d
els
ex
ten
d
s
to
r
ea
l
-
tim
e
o
p
er
atio
n
s
,
wh
er
e
th
ey
en
a
b
l
e
d
y
n
am
ic
ad
ju
s
tm
en
ts
to
e
n
er
g
y
g
en
er
a
tio
n
an
d
d
is
tr
ib
u
tio
n
.
T
h
is
ca
p
ab
ilit
y
is
p
a
r
ticu
lar
ly
b
en
e
f
icial
f
o
r
m
an
a
g
in
g
d
ec
en
tr
alize
d
en
er
g
y
s
y
s
tem
s
lik
e
m
icr
o
g
r
id
s
,
wh
er
e
v
ar
i
ab
ilit
y
in
en
er
g
y
s
u
p
p
ly
an
d
d
em
an
d
r
eq
u
i
r
es
co
n
s
tan
t
m
o
n
it
o
r
in
g
an
d
ad
j
u
s
tm
en
t
[3
8
]
.
Fo
r
lar
g
e
-
s
ca
le
ap
p
licatio
n
s
,
s
u
ch
as
r
en
ew
ab
le
en
er
g
y
p
ar
k
s
,
s
y
s
tem
s
lik
e
KR
E
PS
s
h
o
wc
ase
th
e
p
o
ten
tial
o
f
h
y
b
r
id
m
o
d
els
to
in
teg
r
ate
m
u
ltip
le
r
en
ewa
b
le
en
e
r
g
y
s
o
u
r
ce
s
—
in
clu
d
in
g
win
d
,
s
o
la
r
,
an
d
s
to
r
a
g
e
tech
n
o
lo
g
ies
—
in
to
a
co
h
esiv
e
m
an
ag
e
m
en
t
f
r
am
ewo
r
k
[
5
]
,
[
3
4
]
.
T
h
e
v
er
s
atility
an
d
p
r
ec
is
io
n
o
f
h
y
b
r
id
m
o
d
els
m
ak
e
th
em
a
r
o
b
u
s
t
s
o
lu
tio
n
f
o
r
o
p
tim
izi
n
g
r
en
ewa
b
le
en
er
g
y
s
y
s
tem
s
,
o
f
f
er
in
g
s
ig
n
if
ican
t
im
p
r
o
v
e
m
en
ts
in
f
o
r
ec
asti
n
g
ac
cu
r
ac
y
,
s
y
s
tem
ef
f
icien
cy
,
a
n
d
o
v
er
all
s
tab
ilit
y
[
39
]
.
T
h
eir
a
b
ilit
y
to
c
o
m
b
in
e
th
eo
r
etica
l
u
n
d
er
s
tan
d
in
g
with
d
ata
-
d
r
i
v
en
lear
n
in
g
s
u
g
g
ests
th
ey
will
co
m
e
in
h
an
d
y
as
in
c
r
ea
s
in
g
ly
im
p
o
r
ta
n
t
to
o
ls
f
o
r
e
n
s
u
r
in
g
r
eliab
le
a
n
d
s
u
s
tain
ab
le
en
er
g
y
p
r
o
d
u
ct
io
n
in
in
cr
ea
s
in
g
l
y
co
m
p
lex
f
u
tu
r
e
e
n
er
g
y
lan
d
s
c
ap
es.
3
.
2
.
AI in
s
o
la
r
ener
g
y
s
y
s
t
e
m
s
AI
ad
v
an
ce
s
s
o
lar
en
er
g
y
b
y
en
h
an
cin
g
ef
f
icien
c
y
,
r
elia
b
ilit
y
,
an
d
g
r
id
i
n
teg
r
atio
n
.
I
n
p
o
wer
f
o
r
ec
asti
n
g
,
AI
im
p
r
o
v
es
p
r
ed
ictio
n
s
f
o
r
g
r
i
d
s
tab
ilit
y
an
d
e
n
er
g
y
p
lan
n
in
g
.
Fo
r
o
p
tim
izat
io
n
,
AI
m
a
x
im
izes
en
er
g
y
ca
p
t
u
r
e,
ad
ap
ts
to
co
n
d
itio
n
s
,
an
d
s
tr
ea
m
lin
es
m
o
n
ito
r
in
g
.
AI
-
d
r
iv
en
p
r
ed
ictiv
e
m
ain
ten
an
ce
en
ab
les
ea
r
ly
f
au
lt
d
etec
tio
n
,
an
o
m
al
y
id
en
tific
atio
n
,
an
d
r
ea
l
-
tim
e
d
iag
n
o
s
tics
,
en
s
u
r
in
g
c
o
n
s
is
ten
t
p
er
f
o
r
m
an
ce
a
n
d
r
ed
u
cin
g
co
s
ts
.
3
.
2
.
1
.
So
la
r
po
wer
f
o
re
ca
s
t
ing
AI
-
b
ased
tech
n
iq
u
es
h
av
e
s
ee
n
its
s
ig
n
if
ican
t
ef
f
ec
ts
f
o
r
im
p
r
o
v
in
g
s
o
lar
p
o
wer
f
o
r
ec
asti
n
g
an
d
g
r
i
d
s
tab
ilit
y
.
Ad
v
an
ce
d
m
o
d
els
lik
e
L
STM
s
h
o
w
s
u
p
er
io
r
p
er
f
o
r
m
an
ce
in
p
r
e
d
ictin
g
s
o
lar
elec
tr
icity
g
en
er
atio
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
9
2
I
n
t J Ap
p
l Po
wer
E
n
g
,
Vo
l.
1
5
,
No
.
1
,
Ma
r
ch
20
2
6
:
275
-
2
8
8
280
b
ey
o
n
d
two
h
o
u
r
s
,
en
a
b
lin
g
b
etter
p
la
n
n
in
g
[4
0
]
.
E
n
h
a
n
ce
d
ANNs
in
co
r
p
o
r
atin
g
we
ath
er
d
ata
im
p
r
o
v
e
ac
cu
r
ac
y
,
s
u
itab
le
f
o
r
v
ar
iab
le
clim
ates
[4
1
]
.
E
n
s
em
b
le
m
et
h
o
d
s
(
L
STM
,
XGBo
o
s
t,
r
id
g
e
r
eg
r
ess
io
n
)
p
r
o
v
id
e
r
o
b
u
s
t
f
o
r
ec
asti
n
g
[4
2
]
.
Hy
b
r
id
m
o
d
els
(
1
D
C
NN
with
g
ated
r
ec
u
r
r
en
t
u
n
it
(
GR
U
)
)
ex
ce
l
in
s
h
o
r
t
-
ter
m
f
o
r
ec
asts
,
ca
p
tu
r
in
g
co
m
p
lex
p
atter
n
s
with
h
ig
h
er
p
r
ec
is
io
n
[4
3
]
.
A
cr
itical
co
m
p
ar
is
o
n
o
f
t
h
ese
m
eth
o
d
s
in
d
icate
s
th
at
wh
ile
d
ee
p
lear
n
in
g
m
o
d
els (
L
STM
,
C
NN
-
GR
U)
o
f
ten
o
f
f
er
h
ig
h
er
p
r
ec
is
io
n
f
o
r
co
m
p
lex
,
n
o
n
-
lin
ea
r
p
atter
n
s
,
tr
ad
itio
n
al
m
e
th
o
d
s
an
d
e
n
s
em
b
les
ca
n
p
r
o
v
id
e
a
b
alan
ce
o
f
in
ter
p
r
etab
i
lity
an
d
r
o
b
u
s
tn
ess
,
m
ak
in
g
t
h
eir
s
u
itab
ilit
y
d
e
p
e
n
d
en
t
o
n
th
e
s
p
ec
if
ic
f
o
r
ec
a
s
tin
g
h
o
r
izo
n
,
d
ata
av
ailab
ili
ty
,
an
d
o
p
e
r
atio
n
al
r
eq
u
ir
em
e
n
ts
.
AI
also
en
s
u
r
es
g
r
id
s
tab
ilit
y
th
r
o
u
g
h
in
ter
p
r
eta
b
le
in
s
ig
h
ts
.
E
x
p
lain
ab
le
AI
(
XAI
)
m
eth
o
d
s
(
L
I
ME
,
SHAP,
E
L
I
5
)
h
elp
o
p
e
r
ato
r
s
u
n
d
er
s
tan
d
AI
f
o
r
ec
asts
,
im
p
o
r
tan
t
f
o
r
d
e
cisi
o
n
-
m
ak
in
g
[4
4
]
.
T
h
e
r
am
if
icatio
n
o
f
tr
an
s
p
ar
e
n
t
XAI
is
in
cr
ea
s
e
d
o
p
er
at
o
r
tr
u
s
t
an
d
q
u
ick
er
a
d
o
p
tio
n
o
f
AI
to
o
ls
,
wh
ich
is
ess
en
tial
f
o
r
s
tab
le
g
r
id
o
p
er
atio
n
s
,
p
ar
ticu
lar
ly
wh
en
au
to
n
o
m
o
u
s
d
ec
is
io
n
s
ar
e
in
v
o
l
v
ed
.
T
r
ad
itio
n
al
m
eth
o
d
s
(
d
ec
is
io
n
tr
ee
s
,
r
an
d
o
m
f
o
r
est
)
r
em
ain
u
s
ef
u
l
f
o
r
s
o
lar
ir
r
ad
ia
n
ce
f
o
r
ec
asti
n
g
d
u
e
to
r
eliab
ilit
y
an
d
in
ter
p
r
etab
ilit
y
[4
5
]
.
T
in
y
m
ac
h
in
e
lear
n
i
n
g
(
T
i
n
y
ML
)
o
f
f
er
s
o
n
-
d
ev
ice
p
r
ed
ictio
n
s
f
o
r
r
eso
u
r
ce
-
co
n
s
tr
ain
ed
s
ettin
g
s
[4
6
]
.
T
h
ese
A
I
m
o
d
els
co
n
tr
ib
u
te
to
g
r
id
s
tab
ilit
y
b
y
ad
ap
tin
g
to
co
n
d
i
tio
n
s
,
p
r
o
v
id
i
n
g
p
r
ec
is
e
f
o
r
e
ca
s
ts
,
an
d
o
f
f
er
in
g
s
ca
lab
le
s
o
lu
tio
n
s
.
Ho
wev
er
,
th
eir
ef
f
ec
tiv
e
im
p
lem
en
t
atio
n
r
eq
u
ir
es
ad
d
r
ess
in
g
i
n
ter
p
r
etab
ilit
y
a
n
d
in
teg
r
atio
n
ch
allen
g
es.
Fu
tu
r
e
ef
f
o
r
ts
will
f
o
cu
s
o
n
m
a
k
in
g
th
ese
ad
v
an
ce
d
m
o
d
els
m
o
r
e
ac
ce
s
s
ib
le
an
d
in
ter
p
r
etab
le,
d
i
r
ec
tly
co
n
tr
i
b
u
tin
g
to
th
eir
p
r
a
ctica
l
u
tili
ty
an
d
en
ab
lin
g
wid
e
r
ad
o
p
tio
n
in
d
iv
er
s
e
o
p
er
atio
n
al
en
v
ir
o
n
m
en
ts
.
3
.
2
.
2
.
O
ptim
iza
t
io
n o
f
P
V
s
y
s
t
em
s
AI
p
lay
s
an
im
p
o
r
tan
t
r
o
le
i
n
en
h
an
cin
g
th
e
ef
f
icien
cy
a
n
d
p
e
r
f
o
r
m
an
ce
o
f
PV
s
y
s
tem
s
th
r
o
u
g
h
v
ar
io
u
s
o
p
tim
izatio
n
tech
n
iq
u
es
th
at
ad
d
r
ess
ch
allen
g
es
s
u
ch
as
f
lu
ctu
atin
g
wea
th
er
co
n
d
itio
n
s
an
d
th
e
n
ee
d
f
o
r
p
r
ec
is
e
en
er
g
y
m
an
a
g
em
en
t.
AI
-
b
ased
MPPT
(
n
eu
r
al
n
etwo
r
k
s
,
f
u
zz
y
l
o
g
ic)
o
u
tp
er
f
o
r
m
s
tr
ad
itio
n
al
m
eth
o
d
s
lik
e
p
er
tu
r
b
an
d
o
b
s
er
v
e
(
P&
O)
b
y
o
f
f
er
in
g
g
r
ea
ter
s
tab
ilit
y
an
d
ad
ap
ta
b
ilit
y
to
ch
a
n
g
in
g
wea
th
er
[4
7
]
,
[
4
8
]
.
T
h
is
s
u
p
er
i
o
r
ity
is
in
ter
p
r
eted
as AI
'
s
ca
p
ac
ity
to
d
y
n
am
ically
ad
j
u
s
t th
e
o
p
er
atin
g
p
o
in
t o
f
PV
s
y
s
tem
s
in
r
esp
o
n
s
e
to
r
ea
l
-
tim
e
ch
an
g
es
in
s
u
n
lig
h
t
in
ten
s
ity
an
d
tem
p
er
at
u
r
e,
en
s
u
r
i
n
g
th
at
PV
s
y
s
tem
s
alwa
y
s
o
p
er
ate
at
th
eir
o
p
tim
al
p
o
wer
p
o
in
t,
ev
en
u
n
d
er
r
ap
id
l
y
ch
an
g
i
n
g
co
n
d
itio
n
s
lik
e
p
ar
tial
s
h
ad
in
g
[4
8
]
,
[
49
]
.
T
h
e
r
am
if
icatio
n
o
f
t
h
is
ad
ap
tab
ilit
y
is
p
ar
ticu
lar
ly
v
alu
ab
le
in
r
eg
io
n
s
with
h
ig
h
ly
v
ar
iab
le
wea
th
er
,
wh
er
e
ev
e
n
s
u
b
tle
ch
an
g
es
in
ir
r
ad
ian
ce
ca
n
s
ig
n
if
ican
tly
af
f
ec
t
p
o
wer
o
u
tp
u
t,
lead
in
g
to
s
u
b
-
o
p
tim
al
e
n
er
g
y
ca
p
tu
r
e.
B
y
co
n
tin
u
o
u
s
ly
o
p
tim
izin
g
e
n
e
r
g
y
ca
p
tu
r
e,
th
ese
AI
tech
n
i
q
u
es
in
cr
ea
s
e
o
v
e
r
all
ef
f
icien
cy
,
p
r
o
v
i
d
in
g
a
s
tab
le
s
u
p
p
ly
an
d
r
ed
u
cin
g
r
elian
ce
o
n
s
u
p
p
lem
en
ta
r
y
s
o
u
r
ce
s
.
B
ey
o
n
d
MPPT,
AI
en
h
a
n
ce
s
h
y
b
r
id
PV
s
y
s
tem
s
b
y
i
n
teg
r
a
tin
g
with
o
t
h
er
e
n
er
g
y
s
o
u
r
ce
s
(
e.
g
.
,
f
u
el
ce
lls
)
,
im
p
r
o
v
in
g
d
ec
is
io
n
-
m
a
k
in
g
,
p
er
f
o
r
m
an
ce
,
ef
f
icien
cy
,
an
d
c
o
s
t
s
av
in
g
s
[
1
2
]
.
AI
-
b
ased
f
o
r
ec
asti
n
g
to
o
ls
(
e.
g
.
,
ev
o
l
v
in
g
g
en
e
r
ativ
e
a
d
v
er
s
ar
ial
f
u
zz
y
n
etwo
r
k
s
-
E
G
AFN)
p
r
o
v
i
d
e
ac
cu
r
ate
s
o
lar
en
er
g
y
p
r
e
d
ictio
n
s
,
cr
itical
f
o
r
p
lan
n
in
g
a
n
d
m
an
a
g
in
g
r
eso
u
r
ce
s
[5
0
]
.
T
h
is
ca
p
a
b
ilit
y
h
elp
s
g
r
id
o
p
er
ato
r
s
m
a
n
ag
e
en
e
r
g
y
f
lo
ws,
r
ed
u
cin
g
o
v
er
p
r
o
d
u
ctio
n
o
r
s
h
o
r
tag
es.
AI
alg
o
r
ith
m
s
en
ab
le
au
to
m
ate
d
m
o
n
ito
r
i
n
g
an
d
f
au
lt
d
etec
tio
n
,
id
en
tify
in
g
in
e
f
f
icien
cies
an
d
f
ac
ilit
atin
g
tim
ely
m
ain
ten
an
ce
,
th
u
s
r
ed
u
cin
g
d
o
wn
ti
m
e
an
d
o
p
er
atio
n
al
co
s
ts
[5
1
]
.
Desp
ite
a
d
v
an
ce
s
,
ch
allen
g
es
r
em
ain
:
lar
g
e
tr
ain
in
g
d
atasets
,
co
m
p
u
tatio
n
al
d
em
an
d
s
,
an
d
s
elec
tin
g
th
e
m
o
s
t
s
u
itab
le
AI
ap
p
r
o
ac
h
.
Ad
d
r
ess
in
g
th
ese
th
r
o
u
g
h
r
esear
ch
co
u
ld
en
h
an
ce
AI
'
s
p
r
ac
ticali
ty
in
PV
s
y
s
tem
s
,
m
ak
in
g
th
em
m
o
r
e
ef
f
icien
t
an
d
a
d
ap
tab
l
e.
T
h
ese
o
n
g
o
in
g
im
p
r
o
v
em
en
ts
in
AI
-
d
r
iv
en
o
p
tim
izatio
n
h
o
ld
p
r
o
m
is
e
f
o
r
m
ak
in
g
PV
s
y
s
tem
s
a
m
o
r
e
r
eliab
le
an
d
co
s
t
-
ef
f
ec
tiv
e
s
o
lu
tio
n
f
o
r
s
u
s
tain
ab
le
en
er
g
y
p
r
o
d
u
ctio
n
,
clea
r
ly
i
n
d
icatin
g
wh
at
will
co
m
e
in
h
an
d
y
in
t
h
e
f
u
tu
r
e
f
o
r
b
r
o
a
d
d
ep
lo
y
m
en
t
an
d
im
p
r
o
v
e
d
g
r
i
d
s
tab
ilit
y
.
3
.
2
.
3
.
P
re
dict
iv
e
ma
inte
na
nce
f
o
r
s
o
la
r
pla
nts
AI
-
d
r
iv
en
p
r
ed
ictiv
e
m
ain
ten
an
ce
tr
an
s
f
o
r
m
s
s
o
lar
e
n
er
g
y
b
y
en
h
a
n
cin
g
r
eliab
ilit
y
,
ef
f
ic
ien
cy
,
an
d
r
ed
u
cin
g
o
p
er
atio
n
al
co
s
ts
.
Su
p
er
v
is
ed
lear
n
in
g
alg
o
r
ith
m
s
p
r
ed
ict
an
d
class
if
y
f
au
lts
,
en
ab
lin
g
ea
r
ly
d
etec
tio
n
b
ef
o
r
e
s
ig
n
if
ican
t
p
o
wer
lo
s
s
es
o
cc
u
r
[5
2
]
.
Un
s
u
p
e
r
v
is
ed
lear
n
in
g
d
etec
ts
an
o
m
al
ies
in
s
en
s
o
r
d
ata,
id
en
tify
in
g
p
o
ten
tial
f
ailu
r
es
ea
r
ly
.
R
L
o
p
tim
izes
m
ain
te
n
an
ce
s
ch
ed
u
lin
g
an
d
r
eso
u
r
ce
allo
ca
tio
n
wh
ile
d
ig
ital
twin
s
p
r
o
v
id
e
v
ir
tu
al
r
ep
licas
f
o
r
r
ea
l
-
tim
e
d
iag
n
o
s
tics
an
d
p
la
n
n
in
g
,
r
e
d
u
cin
g
r
elian
ce
o
n
m
an
u
al
in
s
p
ec
tio
n
s
[5
2
]
.
Dee
p
lea
r
n
in
g
m
o
n
ito
r
s
s
o
ilin
g
o
n
PV p
an
e
ls
,
o
p
tim
izin
g
clea
n
in
g
s
ch
ed
u
les
,
an
d
in
ter
n
et
o
f
th
in
g
s
(
I
o
T
)
d
ev
ices c
o
llect
r
e
al
-
tim
e
d
ata
f
o
r
p
r
ed
ictiv
e
i
n
s
ig
h
ts
[5
3
]
,
[
5
4
]
.
A
ch
allen
g
e
in
a
d
o
p
tin
g
AI
-
d
r
iv
en
p
r
ed
ictiv
e
m
ai
n
ten
an
ce
i
s
m
o
d
el
in
te
r
p
r
etab
ilit
y
.
XAI
t
ec
h
n
iq
u
es
(
L
I
ME
,
SHAP)
m
ak
e
AI
d
e
cisi
o
n
s
tr
an
s
p
ar
en
t,
b
u
ild
in
g
tr
u
s
t
in
au
to
m
ated
m
ain
ten
an
ce
[4
4
]
,
[
5
5
]
.
T
h
e
cr
itical
r
am
if
icatio
n
o
f
im
p
r
o
v
ed
XAI
is
n
o
t
ju
s
t
tech
n
ical
u
n
d
er
s
tan
d
in
g
b
u
t
also
in
cr
ea
s
ed
h
u
m
a
n
co
n
f
id
en
ce
in
au
to
m
ated
s
y
s
t
em
s
,
wh
ich
is
im
p
o
r
ta
n
t
in
h
i
g
h
-
s
tak
es
en
v
ir
o
n
m
e
n
ts
wh
er
e
u
n
d
er
s
tan
d
in
g
th
e
r
atio
n
ale
b
eh
in
d
ac
tio
n
s
ca
n
im
p
ac
t
s
y
s
tem
s
af
ety
an
d
ef
f
icien
cy
,
an
d
wh
e
r
e
r
eg
u
lato
r
y
c
o
m
p
lian
ce
d
em
a
n
d
s
tr
an
s
p
ar
en
t
o
p
er
atio
n
s
.
Desp
ite
ch
allen
g
es,
AI
s
tr
ateg
ies
im
p
r
o
v
e
ec
o
n
o
m
ic
v
iab
ilit
y
b
y
m
in
im
izin
g
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ap
p
l Po
wer
E
n
g
I
SS
N:
2252
-
8
7
9
2
A
r
tifi
cia
l in
tellig
en
ce
fo
r
o
p
timiz
in
g
r
en
ewa
b
le
en
erg
y
s
yste
ms:
tech
n
iq
u
es
,
a
p
p
lica
tio
n
s
,
… (
I
a
n
B
.
B
en
itez
)
281
d
o
wn
tim
e,
r
ed
u
cin
g
m
an
u
al
m
ain
ten
an
ce
c
o
s
ts
,
an
d
m
ax
i
m
izin
g
en
e
r
g
y
p
r
o
d
u
ctio
n
[3
8
]
.
As
AI
ad
v
a
n
ce
s
,
th
ese
p
r
ed
ictiv
e
m
ain
ten
an
ce
ap
p
r
o
ac
h
es
will
b
ec
o
m
e
m
o
r
e
r
ef
in
ed
,
m
ak
in
g
s
o
lar
en
er
g
y
s
y
s
tem
s
m
o
r
e
r
esil
ien
t,
co
s
t
-
ef
f
ec
tiv
e,
an
d
ca
p
ab
le
o
f
m
ee
tin
g
d
em
a
n
d
,
r
ep
r
esen
tin
g
wh
at
will
co
m
e
i
n
h
an
d
y
f
o
r
f
u
tu
r
e
lar
g
e
-
s
ca
le,
r
eliab
le,
an
d
s
u
s
tain
ab
le
s
o
lar
en
er
g
y
d
e
p
lo
y
m
e
n
t.
3
.
3
.
AI in
wind
ener
g
y
s
y
s
t
em
s
AI
is
im
p
o
r
tan
t
in
ad
v
an
cin
g
win
d
en
er
g
y
s
y
s
tem
s
,
im
p
r
o
v
in
g
f
o
r
ec
asti
n
g
,
o
p
tim
iz
atio
n
,
an
d
p
r
ed
ictiv
e
m
ai
n
ten
an
ce
.
AI
m
o
d
els
en
h
a
n
ce
win
d
p
o
wer
p
r
ed
ictio
n
s
f
o
r
b
etter
g
r
id
in
teg
r
atio
n
an
d
en
e
r
g
y
m
an
ag
em
en
t.
Fo
r
tu
r
b
in
e
o
p
tim
izatio
n
,
AI
-
d
r
iv
en
co
n
tr
o
l
s
y
s
tem
s
ad
ju
s
t
s
et
tin
g
s
u
s
i
n
g
r
ea
l
-
tim
e
d
ata,
m
ax
im
izin
g
ef
f
icien
c
y
an
d
r
e
liab
ilit
y
.
I
n
p
r
ed
ictiv
e
m
ain
te
n
an
ce
,
AI
d
etec
ts
p
o
ten
tial
is
s
u
es
ea
r
ly
v
ia
d
ata
an
aly
s
is
,
r
ed
u
cin
g
d
o
wn
tim
e
an
d
ex
ten
d
in
g
co
m
p
o
n
en
t
life
s
p
an
.
T
h
is
in
teg
r
atio
n
m
ak
es
win
d
en
er
g
y
m
o
r
e
ef
f
icien
t,
r
eliab
le,
a
n
d
co
s
t
-
ef
f
ec
tiv
e.
3
.
3
.
1
.
Wind
po
wer
f
o
re
ca
s
t
ing
AI
tech
n
iq
u
es
en
h
an
ce
win
d
s
p
ee
d
an
d
p
o
wer
p
r
ed
ictio
n
s
,
im
p
r
o
v
in
g
en
er
g
y
m
an
a
g
em
en
t
b
y
b
o
o
s
tin
g
ac
c
u
r
ac
y
an
d
ef
f
icie
n
cy
.
ML
an
d
d
ee
p
lear
n
in
g
m
o
d
els
u
s
e
h
is
to
r
ical
d
ata,
f
o
r
ec
asts
,
an
d
s
en
s
o
r
in
f
o
r
m
atio
n
f
o
r
p
r
ec
is
e
win
d
en
er
g
y
f
o
r
ec
asts
,
en
ab
lin
g
ef
f
ec
tiv
e
p
lan
n
in
g
[5
6
]
,
[
5
7
]
.
T
h
ese
m
eth
o
d
s
,
in
clu
d
in
g
h
y
b
r
i
d
C
NN
-
L
STM
,
o
f
f
er
u
p
to
a
1
5
%
im
p
r
o
v
e
m
en
t
o
v
er
tr
a
d
itio
n
al
m
o
d
els,
in
cr
ea
s
in
g
en
er
g
y
ef
f
icien
cy
b
y
1
0
%
an
d
im
p
r
o
v
in
g
g
r
id
in
teg
r
atio
n
[5
7
]
,
[
5
8
]
.
A
k
ey
in
ter
p
r
etatio
n
o
f
th
ese
ad
v
an
ce
s
is
t
h
at
th
ey
h
elp
u
tili
ties
s
ig
n
if
ican
tly
r
ed
u
ce
o
p
er
atio
n
al
co
s
ts
an
d
im
p
r
o
v
e
o
v
er
all
g
r
id
r
esp
o
n
s
iv
en
ess
b
y
m
in
im
izin
g
th
e
n
ee
d
f
o
r
ex
p
e
n
s
iv
e
r
eser
v
e
p
o
wer
,
o
p
tim
izi
n
g
en
er
g
y
d
is
p
atch
,
an
d
f
ac
ilit
atin
g
m
o
r
e
ef
f
icien
t
s
ch
ed
u
lin
g
with
in
th
e
g
r
id
n
et
wo
r
k
.
C
h
allen
g
es
in
clu
d
e
p
r
ed
ictio
n
u
n
ce
r
tain
ties
an
d
d
ata
q
u
a
lity
/q
u
an
tity
is
s
u
es
af
f
ec
tin
g
r
eliab
ilit
y
.
No
n
-
s
tatio
n
ar
ity
a
n
d
co
m
p
lex
v
a
r
iab
le
in
ter
ac
tio
n
s
m
a
k
e
co
n
s
is
ten
t
ac
cu
r
ac
y
h
ar
d
t
o
a
ttain
.
T
h
ese
is
s
u
es
p
r
esen
t
s
ig
n
if
ican
t
r
am
if
icatio
n
s
,
as
u
n
r
eliab
le
f
o
r
ec
asts
ca
n
lead
to
g
r
id
in
s
tab
ilit
y
,
h
ig
h
e
r
o
p
er
atio
n
al
co
s
ts
,
in
cr
ea
s
ed
cu
r
tailm
en
t
o
f
r
en
e
wab
le
en
er
g
y
,
an
d
r
ed
u
ce
d
c
o
n
f
id
en
ce
in
win
d
p
o
wer
as
a
p
r
im
ar
y
en
er
g
y
s
o
u
r
ce
,
th
er
e
b
y
im
p
e
d
in
g
t
h
e
en
er
g
y
t
r
an
s
itio
n
.
Ad
d
r
ess
in
g
th
ese
r
eq
u
ir
es
im
p
r
o
v
ed
d
ata
p
r
ep
r
o
ce
s
s
in
g
an
d
m
o
d
el
in
ter
p
r
etab
ilit
y
.
Hig
h
c
o
m
p
u
tatio
n
al
r
eq
u
ir
e
m
en
ts
also
lim
it r
ea
l
-
tim
e
ap
p
licatio
n
,
i
n
d
icatin
g
a
n
ee
d
f
o
r
m
o
r
e
ef
f
icien
t a
p
p
r
o
ac
h
es
[5
6
]
.
Fu
tu
r
e
ad
v
a
n
ce
m
en
ts
in
AI
f
o
r
win
d
s
p
ee
d
an
d
p
o
wer
p
r
ed
ictio
n
aim
to
tack
le
t
h
ese
ch
allen
g
es
th
r
o
u
g
h
th
e
in
teg
r
atio
n
o
f
m
eta
-
h
eu
r
is
tic
alg
o
r
ith
m
s
,
wh
ic
h
en
h
an
ce
th
e
ad
ap
tab
ilit
y
a
n
d
o
p
tim
izatio
n
o
f
p
r
ed
ictio
n
s
.
I
m
p
r
o
v
in
g
f
ea
tu
r
e
s
elec
tio
n
an
d
u
s
in
g
ad
v
a
n
ce
d
d
ata
-
d
r
iv
en
tech
n
iq
u
es
ca
n
f
u
r
th
er
r
ef
in
e
f
o
r
ec
asti
n
g
ac
cu
r
ac
y
,
m
ak
in
g
AI
m
o
d
els
m
o
r
e
r
esp
o
n
s
iv
e
to
ch
an
g
in
g
wea
th
e
r
co
n
d
itio
n
s
.
T
h
ese
ad
v
an
ce
m
e
n
ts
will
en
ab
le
b
etter
in
teg
r
atio
n
o
f
win
d
p
o
wer
in
to
en
er
g
y
s
y
s
tem
s
,
r
ed
u
cin
g
en
er
g
y
waste
an
d
m
ak
in
g
r
en
ewa
b
le
e
n
er
g
y
s
o
u
r
ce
s
m
o
r
e
r
eliab
le
an
d
co
s
t
-
ef
f
ec
tiv
e,
d
ir
e
ctly
illu
s
tr
atin
g
wh
at
will
co
m
e
in
h
an
d
y
f
o
r
f
u
tu
r
e
en
er
g
y
m
a
n
a
g
em
en
t a
n
d
g
r
id
m
o
d
e
r
n
izatio
n
.
3
.
3
.
2
.
T
urbin
e
o
ptim
iza
t
io
n a
nd
co
ntr
o
l
AI
-
en
ab
led
co
n
tr
o
l sch
em
es o
p
tim
ize
win
d
tu
r
b
in
e
co
o
r
d
in
a
tio
n
with
in
win
d
f
ar
m
s
.
T
h
ey
allo
w
r
ea
l
-
tim
e
co
n
tr
o
l,
b
y
p
ass
in
g
tim
e
-
co
n
s
u
m
in
g
o
p
tim
izatio
n
p
r
o
ce
s
s
es
wi
th
h
ig
h
co
m
p
u
tatio
n
al
ef
f
icien
cy
f
o
r
MPPT
an
d
s
et
p
o
in
t
tr
ac
k
in
g
(
SP
T
)
m
o
d
es
[
59
]
.
ML
-
b
ased
win
d
tu
r
b
in
e
c
o
n
tr
o
l
s
y
s
tem
s
(
ML
B
W
T
C
S)
u
s
e
s
en
s
o
r
d
ata
(
win
d
s
p
ee
d
,
b
lad
e
p
itch
an
g
le,
g
e
n
er
ato
r
to
r
q
u
e)
to
p
r
ed
ict
o
p
tim
al
s
ettin
g
s
,
en
h
an
cin
g
ef
f
icien
c
y
an
d
r
eliab
ilit
y
[6
0
]
.
ANN
an
d
f
u
zz
y
l
o
g
ic
(
FL)
ar
e
a
p
p
lied
in
win
d
e
n
er
g
y
f
o
r
MPPT,
m
ain
tain
in
g
o
p
tim
al
o
u
tp
u
t
u
n
d
er
f
l
u
ctu
atin
g
wi
n
d
[6
1
]
.
R
L
,
ANNs,
an
d
m
etah
eu
r
is
tic
o
p
tim
izatio
n
a
lg
o
r
ith
m
s
o
p
tim
ize
tu
r
b
in
e
p
lace
m
en
t
with
in
win
d
f
ar
m
s
,
co
n
s
id
er
i
n
g
e
n
er
g
y
o
u
tp
u
t,
co
s
t
-
ef
f
icien
cy
,
an
d
en
v
ir
o
n
m
en
tal
im
p
ac
t
[6
2
]
.
I
m
p
lem
en
tin
g
AI
in
win
d
tu
r
b
in
e
s
y
s
tem
s
f
ac
es
ch
allen
g
es,
s
u
ch
as
r
eliab
ilit
y
in
o
f
f
s
h
o
r
e
win
d
tech
n
o
lo
g
y
,
wh
er
e
AI
is
i
m
p
o
r
tan
t
f
o
r
im
p
r
o
v
in
g
p
er
f
o
r
m
an
ce
,
p
ar
ticu
lar
ly
i
n
p
r
ed
ictin
g
f
ailu
r
es
in
u
n
s
u
p
er
v
is
ed
co
m
p
o
n
e
n
ts
lik
e
y
aw
b
r
ak
es
[6
3
]
.
C
y
b
er
s
e
cu
r
ity
is
an
o
th
er
co
n
ce
r
n
;
AI
-
b
ased
co
n
v
er
te
r
co
n
tr
o
ller
s
ar
e
p
r
o
p
o
s
ed
to
m
itig
ate
cy
b
er
-
attac
k
s
(
e.
g
.
,
d
ata
s
p
o
o
f
in
g
,
Do
S)
u
s
in
g
an
o
m
aly
d
etec
tio
n
an
d
en
cr
y
p
tio
n
[6
4
]
.
T
h
e
r
am
i
f
icatio
n
s
o
f
th
ese
cy
b
er
s
ec
u
r
ity
v
u
ln
er
ab
ilit
ies
ar
e
s
ig
n
if
ican
t,
p
o
ten
tially
lead
in
g
to
g
r
id
in
s
tab
ilit
y
,
o
p
e
r
atio
n
al
d
i
s
r
u
p
tio
n
,
f
in
an
cial
lo
s
s
es,
an
d
ev
en
n
atio
n
al
s
ec
u
r
ity
th
r
ea
t
s
if
n
o
t
ef
f
ec
tiv
el
y
m
an
ag
ed
t
h
r
o
u
g
h
r
o
b
u
s
t
AI
-
en
ab
led
d
e
f
en
s
es.
Desp
ite
th
ese
ch
allen
g
es,
AI
b
o
o
s
ts
en
er
g
y
p
r
o
d
u
ctio
n
b
y
o
p
tim
izin
g
ef
f
icien
cy
a
n
d
r
ed
u
cin
g
co
s
ts
[
1
5
]
.
I
t
also
im
p
r
o
v
es
win
d
p
o
wer
f
o
r
ec
asti
n
g
an
d
en
ab
les
ea
r
ly
d
etec
tio
n
o
f
tu
r
b
i
n
e
f
ailu
r
es
v
ia
SC
A
DA
d
ata
[6
5
]
.
I
n
teg
r
at
in
g
AI
in
to
win
d
tu
r
b
in
e
o
p
er
atio
n
s
h
o
ld
s
g
r
ea
t
p
o
ten
tial
f
o
r
o
p
tim
izin
g
e
n
er
g
y
p
r
o
d
u
ctio
n
,
en
h
an
cin
g
f
o
r
ec
asti
n
g
ca
p
ab
ilit
ies,
an
d
p
r
o
ac
tiv
ely
ad
d
r
ess
in
g
k
ey
ch
allen
g
es
lik
e
r
eliab
ilit
y
an
d
cy
b
er
s
ec
u
r
it
y
,
s
h
o
win
g
wh
at
will
co
m
e
in
h
an
d
y
f
o
r
th
e
f
u
tu
r
e
o
f
lar
g
e
-
s
ca
le,
s
ec
u
r
e,
an
d
ef
f
icien
t w
in
d
en
er
g
y
d
ev
elo
p
m
en
t.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
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8
7
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2
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p
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n
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Vo
l.
1
5
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No
.
1
,
Ma
r
ch
20
2
6
:
275
-
2
8
8
282
3
.
3
.
3
.
P
re
dict
iv
e
ma
inte
na
nce
f
o
r
wind
pla
nts
Pre
d
ictiv
e
m
ain
ten
an
ce
f
o
r
win
d
tu
r
b
i
n
es
lev
er
ag
es
AI
to
en
h
an
ce
r
eliab
ilit
y
,
r
ed
u
ce
d
o
wn
tim
e,
an
d
lo
wer
o
p
er
atio
n
al
co
s
ts
.
ML
alg
o
r
ith
m
s
(
r
a
n
d
o
m
f
o
r
est,
L
STM
,
ANFI
S)
p
r
ed
ict
f
ailu
r
e
s
an
d
class
if
y
f
au
lts
.
An
aly
zin
g
h
is
to
r
ical
an
d
r
e
al
-
tim
e
d
ata,
th
ese
m
o
d
els
d
etec
t
an
o
m
alies
an
d
f
o
r
ec
a
s
t
is
s
u
es,
allo
win
g
p
r
o
ac
tiv
e
i
n
ter
v
en
tio
n
b
ef
o
r
e
escalatio
n
[3
0
]
,
[
6
3
]
.
T
h
is
p
r
o
ac
tiv
e
ap
p
r
o
ac
h
is
a
cr
itical
i
n
ter
p
r
etatio
n
o
f
AI
'
s
v
alu
e:
it
n
o
t
o
n
ly
m
i
n
im
izes
u
n
p
lan
n
ed
d
o
wn
tim
e
b
u
t
also
ex
ten
d
s
th
e
life
s
p
an
o
f
tu
r
b
i
n
e
co
m
p
o
n
en
ts
b
y
en
s
u
r
in
g
tim
el
y
an
d
tar
g
eted
m
ain
ten
an
ce
,
lead
in
g
to
s
u
b
s
tan
tial
ec
o
n
o
m
ic
an
d
o
p
e
r
atio
n
al
b
en
e
f
its
b
y
r
ed
u
cin
g
co
s
tly
r
ea
ctiv
e
r
ep
a
ir
s
an
d
lo
s
t
r
ev
en
u
e
f
r
o
m
o
u
tag
es.
AI
en
h
a
n
ce
s
m
ain
ten
an
ce
s
ch
ed
u
les
b
y
r
ec
o
g
n
izin
g
p
atter
n
s
p
r
ec
ed
in
g
f
ailu
r
es.
AI
-
d
r
iv
en
p
r
ed
ictiv
e
m
ain
ten
an
ce
o
f
ten
in
teg
r
ates
clo
u
d
p
latf
o
r
m
s
an
d
I
o
T
s
y
s
tem
s
f
o
r
r
ea
l
-
tim
e
d
ata
p
r
o
ce
s
s
in
g
,
en
a
b
lin
g
s
ca
lab
le,
ce
n
tr
alize
d
m
o
n
ito
r
in
g
o
f
tu
r
b
in
es
ac
r
o
s
s
g
eo
g
r
a
p
h
ie
s
[6
6
]
,
[
6
7
]
.
R
ap
id
an
aly
s
is
o
f
s
en
s
o
r
d
ata
s
u
p
p
o
r
ts
q
u
ick
er
d
ec
is
io
n
-
m
a
k
in
g
.
C
lo
u
d
co
m
p
u
tin
g
m
ak
es
AI
s
o
lu
tio
n
s
co
s
t
-
ef
f
ec
tiv
e
b
y
r
ed
u
cin
g
o
n
-
s
ite
in
f
r
astru
ctu
r
e
n
ee
d
s
.
T
h
is
co
m
b
in
atio
n
h
ig
h
lig
h
ts
a
s
h
if
t
to
war
d
s
d
ata
-
d
r
iv
e
n
,
ef
f
icien
t m
ain
ten
an
ce
.
Dig
ital
twin
s
f
u
r
th
er
e
n
h
an
ce
ca
p
ab
ilit
ies,
p
r
o
v
id
in
g
v
ir
tu
al
r
ep
licas
f
o
r
r
ea
l
-
tim
e
co
n
d
itio
n
m
o
n
ito
r
in
g
,
d
ata
an
aly
s
is
,
an
d
f
ee
d
b
ac
k
[6
7
]
.
T
h
is
in
s
ig
h
t
allo
ws
o
p
er
ato
r
s
to
s
im
u
late
s
ce
n
ar
io
s
an
d
p
r
ed
ict
m
ain
ten
an
ce
n
ee
d
s
.
Sen
s
o
r
d
a
ta
is
cr
itica
l
f
o
r
p
r
ed
ictin
g
th
e
r
em
ain
in
g
u
s
ef
u
l
life
(
R
UL
)
o
f
co
m
p
o
n
e
n
ts
[6
6
]
.
Au
to
en
co
d
e
r
s
an
d
is
o
latio
n
f
o
r
ests
ass
is
t
in
an
o
m
al
y
d
ete
ctio
n
[3
0
]
.
C
o
m
b
in
in
g
AI
in
s
ig
h
ts
with
h
u
m
an
ex
p
er
tis
e
im
p
r
o
v
es
f
au
lt
d
ete
ctio
n
ac
cu
r
ac
y
,
s
u
p
p
o
r
tin
g
le
s
s
ex
p
er
ien
ce
d
tech
n
ician
s
[6
8
]
.
T
h
is
h
u
m
an
-
AI
co
llab
o
r
atio
n
r
ep
r
esen
ts
an
im
p
o
r
tan
t
f
u
tu
r
e
d
ir
ec
tio
n
f
o
r
p
r
ac
tical
im
p
lem
en
tatio
n
,
en
s
u
r
in
g
th
at
t
h
e
tech
n
o
lo
g
y
c
o
m
p
lem
en
ts
h
u
m
an
s
k
ills
r
ath
er
th
an
r
ep
lacin
g
th
em
e
n
tire
ly
.
As
a
r
esu
lt,
AI
-
d
r
iv
e
n
p
r
ed
ictiv
e
m
ain
ten
an
ce
o
f
f
er
s
s
ig
n
if
ica
n
t
b
en
ef
its
,
in
clu
d
in
g
g
r
ea
ter
o
p
er
atio
n
al
ef
f
icien
c
y
,
r
ed
u
ce
d
c
o
s
ts
,
an
d
en
h
an
ce
d
r
eliab
ilit
y
,
m
ak
in
g
r
en
ewa
b
l
e
en
er
g
y
p
r
o
d
u
ctio
n
m
o
r
e
s
u
s
tain
ab
le
an
d
c
o
s
t
-
ef
f
ec
tiv
e
[6
5
]
,
[
6
6
]
.
T
h
ese
ad
v
an
ce
m
e
n
ts
d
ir
ec
tly
co
n
tr
i
b
u
te
to
wh
at
will
co
m
e
in
h
an
d
y
in
t
h
e
f
u
t
u
r
e
f
o
r
lar
g
e
-
s
ca
le,
r
esil
ien
t,
an
d
ec
o
n
o
m
ically
v
iab
le
win
d
p
o
wer
d
ep
lo
y
m
en
t a
cr
o
s
s
d
iv
er
s
e
en
v
ir
o
n
m
en
ts
.
3
.
4
.
Cha
lleng
es a
nd
l
im
it
a
t
i
o
ns
3
.
4
.
1
.
Da
t
a
a
v
a
ila
bil
it
y
a
nd
qu
a
lity
AI
ap
p
licatio
n
s
in
r
en
ewa
b
le
en
er
g
y
s
y
s
tem
s
(
R
E
S)
f
ac
e
s
ig
n
if
ican
t
ch
allen
g
es
r
elate
d
to
d
ata
q
u
ality
an
d
in
teg
r
atio
n
,
h
in
d
er
in
g
ef
f
ec
tiv
en
ess
in
o
p
tim
i
za
tio
n
,
f
o
r
ec
asti
n
g
,
a
n
d
m
ain
ten
an
ce
.
R
eso
lv
in
g
th
ese
is
es
s
en
tial
f
o
r
im
p
r
o
v
e
d
s
y
s
tem
p
er
f
o
r
m
an
ce
.
Data
q
u
ality
co
n
ce
r
n
s
in
clu
d
e
tim
elin
ess
,
co
m
p
leten
ess
,
co
n
s
is
ten
cy
,
an
d
ac
c
u
r
ac
y
.
AI
m
o
d
els
r
eq
u
i
r
e
r
ea
l
-
tim
e
d
ata
f
r
o
m
I
o
T
d
ev
ices
f
o
r
q
u
ick
r
esp
o
n
s
es
to
f
lu
ctu
atin
g
co
n
d
itio
n
s
[
6
]
.
D
elay
s
o
r
in
co
m
p
lete
d
atasets
r
ed
u
ce
p
r
ed
ictio
n
ac
c
u
r
ac
y
an
d
h
in
d
er
m
o
d
el
tr
ain
in
g
[
1
5
]
.
T
h
e
in
h
er
e
n
t
v
ar
iab
ilit
y
o
f
R
E
S
d
ata
lead
s
to
in
co
n
s
is
ten
cies,
co
m
p
licatin
g
lo
n
g
-
ter
m
f
o
r
ec
asti
n
g
[
6
]
,
[
69
]
.
I
n
ac
c
u
r
ate
s
en
s
o
r
o
r
wea
th
er
d
ata
d
is
to
r
ts
AI
-
d
r
iv
en
d
ec
is
io
n
s
,
with
s
ig
n
if
ican
t
r
am
if
icatio
n
s
f
o
r
s
y
s
tem
r
eliab
ilit
y
an
d
ef
f
icien
cy
,
p
o
ten
t
ially
lead
in
g
to
s
u
b
o
p
tim
al
e
n
er
g
y
d
is
p
atch
a
n
d
in
cr
ea
s
ed
o
p
er
atio
n
al
co
s
ts
.
Data
in
teg
r
atio
n
in
v
o
lv
es
co
m
b
in
in
g
d
iv
e
r
s
e
d
ata
s
o
u
r
ce
s
(
wea
th
er
,
s
en
s
o
r
o
u
tp
u
ts
,
h
is
to
r
ical
r
ec
o
r
d
s
)
,
o
f
ten
in
v
ar
y
in
g
f
o
r
m
ats
an
d
r
eso
lu
tio
n
s
[
5
]
,
[
6
]
.
T
h
ese
s
o
u
r
ce
s
d
if
f
er
in
f
r
eq
u
en
cy
an
d
p
r
ec
is
io
n
,
co
m
p
licatin
g
s
tan
d
ar
d
izatio
n
an
d
alig
n
m
en
t
[7
0
]
.
E
f
f
ec
tiv
e
alig
n
m
en
t
is
im
p
o
r
tan
t
f
o
r
ac
cu
r
ate,
r
ea
l
-
tim
e
in
s
ig
h
ts
th
at
en
h
a
n
ce
e
n
er
g
y
y
ield
,
r
ed
u
ce
o
p
er
atio
n
al
c
o
s
ts
,
an
d
im
p
r
o
v
e
g
r
id
r
eliab
ilit
y
[3
7
]
.
T
h
ese
ch
allen
g
es lim
it AI
'
s
ab
ilit
y
to
f
u
lly
ca
p
tu
r
e
co
m
p
lex
R
E
S d
y
n
am
ics an
d
p
r
o
v
id
e
o
p
tim
al
s
o
lu
tio
n
s
.
3
.
4
.
2
.
M
o
del
inte
rpre
t
a
bil
it
y
a
nd
t
rus
t
wo
rt
hin
es
s
Desp
ite
AI
’
s
im
p
o
r
ta
n
t
r
o
le,
its
ef
f
ec
tiv
en
ess
an
d
a
d
o
p
t
io
n
d
e
p
en
d
o
n
m
o
d
el
i
n
ter
p
r
etab
ilit
y
,
tr
u
s
two
r
th
in
ess
,
an
d
u
s
er
en
g
ag
em
en
t.
B
u
ild
in
g
tr
u
s
t
an
d
en
s
u
r
in
g
r
esp
o
n
s
ib
le
d
e
p
lo
y
m
en
t
r
eq
u
ir
es
AI
s
y
s
tem
s
d
esig
n
ed
with
tr
a
n
s
p
ar
en
cy
,
ac
co
u
n
tab
ilit
y
,
a
n
d
u
s
ab
ilit
y
.
An
o
m
aly
d
etec
tio
n
i
s
co
r
e
to
AI
-
d
r
iv
en
en
er
g
y
s
y
s
tem
s
,
id
en
tify
in
g
e
q
u
ip
m
en
t
f
au
lts
an
d
d
is
r
u
p
tio
n
s
.
T
ec
h
n
iq
u
es
lik
e
a
u
to
en
co
d
er
s
an
d
is
o
latio
n
f
o
r
ests
d
etec
t
ab
n
o
r
m
al
p
atter
n
s
in
win
d
an
d
s
o
lar
d
ata,
s
u
p
p
o
r
tin
g
p
r
ed
ictiv
e
m
ain
te
n
a
n
ce
th
at
m
in
im
izes
d
o
wn
tim
e
[4
5
]
.
T
h
is
p
r
o
ac
tiv
e
ca
p
ab
ilit
y
s
ig
n
if
ican
tly
im
p
r
o
v
es sy
s
tem
p
er
f
o
r
m
a
n
ce
.
T
o
en
s
u
r
e
tr
an
s
p
ar
en
c
y
,
b
y
-
d
esig
n
f
r
am
ewo
r
k
s
lik
e
u
s
er
-
ce
n
tr
ic
ex
p
lain
ab
le
AI
(
XA
I
)
em
b
ed
in
ter
p
r
etab
ilit
y
f
r
o
m
ea
r
l
y
d
ev
elo
p
m
en
t
s
tag
es.
T
h
is
in
teg
r
at
es
tr
an
s
p
ar
en
cy
in
t
o
task
s
lik
e
p
o
wer
f
o
r
ec
asti
n
g
an
d
f
au
lt
p
r
e
d
ictio
n
,
m
a
k
in
g
AI
d
ec
is
io
n
s
u
n
d
er
s
tan
d
ab
le
an
d
r
eg
u
lat
o
r
y
-
c
o
m
p
lian
t
[7
1
]
.
A
u
s
er
-
ce
n
tr
ic
ap
p
r
o
ac
h
p
r
i
o
r
itizes
en
d
-
u
s
er
n
ee
d
s
,
allo
win
g
o
p
e
r
ato
r
s
an
d
en
g
in
ee
r
s
to
i
n
ter
ac
t
wi
th
u
n
d
e
r
s
tan
d
ab
le,
tailo
r
ed
AI
m
o
d
els.
B
u
ild
in
g
tr
u
s
two
r
th
y
AI
also
r
eq
u
ir
es
ad
d
r
ess
in
g
r
eliab
ilit
y
,
tr
an
s
p
ar
en
cy
,
eth
ics,
an
d
ac
c
o
u
n
tab
ilit
y
.
User
s
m
u
s
t
tr
u
s
t
co
n
s
is
ten
t
m
o
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el
p
er
f
o
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m
a
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ce
ac
r
o
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s
d
iv
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s
e
co
n
d
itio
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s
.
Fair
n
ess
a
n
d
b
ias
m
u
s
t
b
e
m
o
n
ito
r
ed
,
esp
ec
ially
wh
en
AI
in
f
lu
en
ce
s
d
ec
is
io
n
s
with
o
p
er
atio
n
al
an
d
s
af
et
y
im
p
licatio
n
s
[
9
]
.
I
n
ter
p
r
etab
ilit
y
tec
h
n
iq
u
es
li
k
e
SHAP
an
d
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tify
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m
p
lex
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I
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f
ea
tu
r
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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-
8
7
9
2
A
r
tifi
cia
l in
tellig
en
ce
fo
r
o
p
timiz
in
g
r
en
ewa
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ms:
tech
n
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,
a
p
p
lica
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s
,
… (
I
a
n
B
.
B
en
itez
)
283
co
n
tr
ib
u
tio
n
s
to
p
r
e
d
ictio
n
s
,
en
ab
lin
g
i
n
f
o
r
m
ed
d
ec
is
io
n
s
[7
1
]
,
[
7
2
]
.
I
n
h
ig
h
-
s
tak
es
r
en
ewa
b
le
en
e
r
g
y
ap
p
licatio
n
s
,
in
ter
p
r
eta
b
ilit
y
is
n
o
t
o
p
tio
n
al
b
u
t
ess
en
tial
f
o
r
tr
u
s
t,
ac
co
u
n
tab
ilit
y
,
a
n
d
l
o
n
g
-
ter
m
ad
o
p
tio
n
.
W
ith
o
u
t
it,
th
e
r
am
if
icatio
n
s
in
clu
d
e
r
ed
u
ce
d
ad
o
p
tio
n
,
o
p
er
atio
n
al
r
is
k
s
,
a
lack
o
f
ac
co
u
n
ta
b
ilit
y
in
au
to
m
ated
d
ec
is
io
n
-
m
ak
in
g
,
a
n
d
a
s
ig
n
if
ican
t
h
in
d
r
a
n
ce
to
s
ca
lin
g
in
tellig
en
t
en
er
g
y
s
o
lu
tio
n
s
in
r
ea
l
-
wo
r
ld
s
ce
n
ar
io
s
.
B
y
in
co
r
p
o
r
atin
g
ex
p
lain
ab
ilit
y
,
tr
u
s
two
r
th
in
ess
,
a
n
d
u
s
er
-
ce
n
ter
ed
d
esig
n
,
AI
m
o
d
els
ca
n
n
o
t
o
n
l
y
en
h
an
ce
o
p
er
atio
n
al
e
f
f
icien
c
y
b
u
t
also
b
ec
o
m
e
tr
u
s
ted
to
o
ls
in
th
e
r
en
ewa
b
le
en
e
r
g
y
s
ec
to
r
.
As
en
er
g
y
s
y
s
tem
s
g
r
o
w
m
o
r
e
in
tellig
e
n
t
an
d
in
ter
co
n
n
ec
ted
,
th
ese
ele
m
en
ts
will
b
e
im
p
o
r
tan
t
f
o
r
e
n
s
u
r
in
g
AI
s
er
v
es
as
a
s
u
s
tain
ab
le
an
d
r
eliab
le
f
o
r
c
e
in
clea
n
en
er
g
y
tr
a
n
s
f
o
r
m
ati
o
n
.
3
.
4
.
3
.
E
t
hica
l a
nd
s
ec
urit
y
c
o
ncer
ns
I
n
teg
r
atin
g
AI
in
t
o
r
en
ewa
b
l
e
en
er
g
y
s
y
s
tem
s
p
r
esen
ts
s
i
g
n
if
ican
t
eth
ical
an
d
s
ec
u
r
ity
co
n
ce
r
n
s
.
A
p
r
im
ar
y
eth
ical
ch
allen
g
e
is
d
ata
p
r
iv
ac
y
,
as
lar
g
e
v
o
lu
m
es
o
f
o
p
er
atio
n
al
an
d
u
s
er
d
ata
ar
e
co
llected
.
W
ith
o
u
t
s
af
eg
u
ar
d
s
,
s
en
s
itiv
e
in
f
o
r
m
atio
n
is
v
u
ln
er
ab
le
to
m
is
u
s
e,
u
n
d
er
m
in
in
g
tr
u
s
t
an
d
co
m
p
lian
ce
[
1
1
]
,
[7
3
]
.
T
r
an
s
p
ar
en
c
y
is
also
im
p
o
r
tan
t,
en
a
b
lin
g
s
tak
eh
o
ld
er
s
to
u
n
d
e
r
s
tan
d
AI
-
d
r
i
v
e
n
d
ec
is
io
n
s
.
XAI
f
r
am
ewo
r
k
s
m
a
k
e
AI
m
o
d
els
in
ter
p
r
etab
le
a
n
d
tr
u
s
two
r
th
y
.
E
q
u
itab
le
ac
ce
s
s
to
AI
-
d
r
i
v
en
tech
n
o
lo
g
ies
is
an
o
th
er
m
ajo
r
eth
ical
co
n
ce
r
n
;
th
e
d
ig
ital
d
iv
id
e
m
u
s
t
b
e
ad
d
r
ess
ed
to
en
s
u
r
e
all
co
m
m
u
n
ities
b
en
ef
it,
p
r
ev
en
tin
g
f
u
r
th
er
in
e
q
u
alities
.
On
th
e
s
ec
u
r
ity
f
r
o
n
t,
th
e
in
cr
ea
s
in
g
d
ig
italizatio
n
o
f
en
er
g
y
in
f
r
astru
ctu
r
e
e
x
p
o
s
es
s
y
s
tem
s
to
cy
b
er
th
r
ea
ts
.
Stro
n
g
cy
b
e
r
s
ec
u
r
ity
m
ea
s
u
r
es
(
en
cr
y
p
tio
n
,
ac
ce
s
s
co
n
tr
o
l,
r
e
g
u
lar
au
d
its
)
ar
e
ess
en
tial
f
o
r
p
r
o
tectin
g
cr
itical
ass
et
s
[7
0
]
.
AI
-
en
ab
le
d
th
r
ea
t
d
etec
tio
n
u
s
in
g
m
ac
h
i
n
e
lear
n
in
g
e
n
h
an
ce
s
s
ec
u
r
ity
b
y
id
en
tify
i
n
g
r
ea
l
-
tim
e
an
o
m
alies.
Ho
wev
er
,
c
h
allen
g
es
r
em
ain
,
in
clu
d
in
g
th
e
n
ee
d
f
o
r
q
u
ality
d
atasets
an
d
in
ter
p
r
etab
le
s
ec
u
r
ity
m
o
d
els
[7
4
]
.
T
h
e
r
a
m
if
icatio
n
s
o
f
f
ailin
g
to
ad
d
r
ess
th
ese
s
ec
u
r
ity
co
n
ce
r
n
s
in
clu
d
e
p
o
ten
tial
g
r
id
in
s
tab
ilit
y
,
o
p
er
atio
n
al
d
is
r
u
p
t
io
n
,
f
in
a
n
cial
lo
s
s
es,
an
d
e
v
e
n
n
atio
n
al
s
ec
u
r
ity
th
r
ea
ts
,
je
o
p
ar
d
izin
g
th
e
v
er
y
in
f
r
astru
ctu
r
e
AI
aim
s
to
o
p
ti
m
ize.
Ad
d
r
ess
in
g
th
ese
co
n
ce
r
n
s
r
eq
u
ir
es
co
m
p
r
e
h
en
s
iv
e
r
eg
u
lato
r
y
f
r
am
ewo
r
k
s
th
at
en
f
o
r
ce
s
tan
d
ar
d
s
o
n
d
ata
p
r
iv
ac
y
,
tr
a
n
s
p
ar
en
cy
,
an
d
cy
b
er
s
ec
u
r
ity
.
Su
ch
p
o
lic
ies
m
u
s
t
en
s
u
r
e
AI
tech
n
o
lo
g
ies
ar
e
d
ep
lo
y
e
d
eth
ically
an
d
s
ec
u
r
ely
,
alig
n
i
n
g
with
s
o
cieta
l
v
alu
es
wh
ile
m
ain
tain
in
g
s
y
s
tem
in
teg
r
ity
[7
3
]
.
B
y
cr
ea
tin
g
clea
r
r
eg
u
latio
n
s
,
t
h
e
r
en
ewa
b
le
en
er
g
y
s
ec
to
r
ca
n
co
n
f
i
d
en
tly
ad
o
p
t
AI
tech
n
o
lo
g
ies,
en
s
u
r
in
g
s
y
s
tem
s
r
em
ain
s
ec
u
r
e,
eth
ical,
an
d
r
esil
ien
t.
3
.
5
.
F
uture
t
re
nd
s
a
nd
direct
io
ns
3
.
5
.
1
.
E
m
er
g
ing
AI
t
ec
hn
iqu
es f
o
r
re
newa
ble e
nerg
y
E
m
er
g
in
g
AI
tec
h
n
iq
u
es
li
k
e
d
ee
p
r
ei
n
f
o
r
ce
m
en
t
lear
n
in
g
(
DR
L
)
,
g
en
er
ativ
e
ad
v
e
r
s
ar
ial
n
etwo
r
k
s
(
GANs)
,
an
d
h
y
b
r
id
AI
-
p
h
y
s
i
ca
l
m
o
d
els
ar
e
ex
p
ec
te
d
to
s
i
g
n
if
ican
tly
en
h
an
ce
r
en
ewa
b
l
e
en
er
g
y
s
y
s
tem
s
.
DR
L
is
ef
f
ec
tiv
e
in
r
ea
l
-
tim
e
d
ec
is
io
n
-
m
ak
i
n
g
in
d
y
n
am
i
c
en
er
g
y
en
v
ir
o
n
m
en
ts
,
o
p
ti
m
izin
g
s
to
r
ag
e
an
d
d
is
tr
ib
u
tio
n
.
I
ts
ab
ilit
y
to
co
n
tin
u
o
u
s
ly
lear
n
an
d
a
d
ap
t
is
v
alu
ab
le
in
s
m
ar
t
g
r
id
s
an
d
d
ec
en
tr
alize
d
s
y
s
tem
s
,
wh
er
e
r
ea
l
-
tim
e
ad
ju
s
tm
en
ts
ar
e
cr
itical
f
o
r
s
u
p
p
ly
-
d
em
an
d
b
alan
ce
[
1
7
]
,
[
29
]
.
GANs
ca
n
im
p
r
o
v
e
en
er
g
y
f
o
r
ec
asti
n
g
b
y
g
e
n
er
atin
g
s
y
n
th
etic
d
ata
f
o
r
ar
ea
s
with
in
s
u
f
f
icien
t
h
is
to
r
ical
r
ec
o
r
d
s
,
e
n
h
an
cin
g
AI
m
o
d
el
tr
ain
in
g
.
T
h
eir
a
b
ilit
y
to
s
im
u
late
d
iv
er
s
e
s
ce
n
ar
i
o
s
im
p
r
o
v
e
s
ac
cu
r
ac
y
an
d
r
o
b
u
s
tn
ess
o
f
f
o
r
ec
asts
,
esp
ec
ially
in
r
eg
io
n
s
with
h
i
g
h
wea
th
er
v
ar
iab
ilit
y
.
Hy
b
r
id
AI
-
p
h
y
s
ical
m
o
d
els
co
m
b
in
e
AI
with
tr
ad
itio
n
al
p
h
y
s
ics
-
b
ased
ap
p
r
o
ac
h
es f
o
r
m
o
r
e
ac
cu
r
ate
p
r
ed
ictio
n
s
an
d
o
p
tim
izatio
n
.
B
len
d
in
g
d
ata
-
d
r
i
v
en
in
s
ig
h
ts
with
p
h
y
s
ical
m
o
d
elin
g
a
d
d
r
ess
es
n
o
n
lin
ea
r
d
y
n
am
ics
o
f
r
en
ewa
b
le
e
n
er
g
y
s
y
s
tem
s
,
as
s
h
o
wn
b
y
KR
E
PS
,
wh
ich
en
h
an
ce
s
f
o
r
ec
asts
b
y
in
teg
r
atin
g
AI
with
wea
th
er
p
r
ed
ictio
n
s
[3
4
]
.
T
h
ese
ad
v
an
ce
d
AI
tech
n
i
q
u
es
will
p
lay
a
p
iv
o
tal
r
o
le
in
o
v
er
co
m
i
n
g
cu
r
r
en
t
ch
allen
g
es
r
elate
d
to
d
ata
v
a
r
iab
ilit
y
,
p
r
e
d
ictio
n
ac
cu
r
ac
y
,
an
d
s
y
s
tem
r
eliab
ilit
y
,
r
ep
r
e
s
en
tin
g
wh
at
will
ce
r
tain
ly
co
m
e
i
n
h
an
d
y
f
o
r
f
u
tu
r
e
R
E
S d
ev
elo
p
m
e
n
t a
n
d
i
n
c
r
ea
s
ed
g
r
id
r
esil
ien
ce
.
3
.
5
.
2
.
I
nte
g
ra
t
io
n o
f
AI wit
h Io
T
in
re
newa
b
le
ener
g
y
T
h
e
in
teg
r
atio
n
o
f
AI
with
I
o
T
will
r
ev
o
lu
tio
n
ize
r
en
ewa
b
l
e
en
er
g
y
m
an
a
g
em
en
t
th
r
o
u
g
h
r
ea
l
-
tim
e
m
o
n
ito
r
in
g
,
a
n
aly
s
is
,
an
d
co
n
tr
o
l.
I
o
T
d
e
v
ices
(
s
en
s
o
r
s
,
s
m
ar
t
m
eter
s
)
g
en
er
ate
v
ast
a
m
o
u
n
ts
o
f
r
ea
l
-
tim
e
d
ata,
wh
ich
AI
m
o
d
els
a
n
aly
z
e
to
o
p
tim
ize
p
r
o
d
u
ctio
n
,
d
ete
ct
an
o
m
alies,
a
n
d
p
r
e
d
ict
m
ai
n
ten
an
ce
[5
3
]
,
[
5
4
]
.
T
h
is
is
p
ar
ticu
lar
ly
b
en
e
f
icial
f
o
r
s
o
lar
an
d
win
d
,
wh
er
e
e
n
v
i
r
o
n
m
en
tal
f
ac
to
r
s
f
lu
ctu
ate
c
o
n
s
tan
tly
.
AI
-
d
r
iv
en
I
o
T
s
y
s
tem
s
en
a
b
le
co
n
tin
u
o
u
s
m
o
n
ito
r
in
g
an
d
r
e
al
-
tim
e
ad
ju
s
tm
en
ts
f
o
r
p
ea
k
e
f
f
icien
cy
.
AI
’
s
r
o
le
in
p
r
ed
ictiv
e
m
ai
n
ten
an
ce
is
en
h
an
ce
d
b
y
I
o
T
s
en
s
o
r
s
co
llectin
g
p
er
f
o
r
m
an
ce
d
ata,
allo
win
g
ea
r
ly
d
etec
tio
n
o
f
e
q
u
ip
m
e
n
t
f
ail
u
r
e,
m
in
im
izin
g
d
o
wn
tim
e,
an
d
ex
ten
d
in
g
ass
et
life
s
p
an
[5
3
]
,
[
5
4
]
.
I
n
d
ec
en
tr
alize
d
s
y
s
tem
s
,
AI
-
I
o
T
in
teg
r
atio
n
p
r
o
v
i
d
es
r
ea
l
-
tim
e
co
o
r
d
i
n
atio
n
f
o
r
m
an
a
g
in
g
d
is
p
er
s
ed
en
er
g
y
s
o
u
r
ce
s
an
d
o
p
tim
izin
g
en
er
g
y
f
lo
ws
[3
8
]
.
As
I
o
T
s
p
r
ea
d
s
,
f
u
tu
r
e
r
esear
ch
will
f
o
cu
s
o
n
im
p
r
o
v
in
g
d
ata
in
teg
r
atio
n
,
en
h
a
n
cin
g
s
y
s
tem
s
ca
lab
ilit
y
,
an
d
en
s
u
r
in
g
th
e
s
ec
u
r
ity
o
f
AI
-
I
o
T
n
etwo
r
k
s
,
wh
ich
will
b
e
im
p
o
r
tan
t f
o
r
its
wid
esp
r
ea
d
a
d
o
p
tio
n
an
d
u
tili
ty
in
f
o
s
ter
in
g
s
m
ar
ter
,
m
o
r
e
au
to
n
o
m
o
u
s
en
er
g
y
s
y
s
tem
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
2
5
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5
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Ma
r
ch
20
2
6
:
275
-
2
8
8
284
3
.
5
.
3
.
AI f
o
r
dece
ntr
a
lized
ener
g
y
s
y
s
t
em
s
AI
is
ess
en
tial
f
o
r
ad
v
an
cin
g
d
ec
en
tr
alize
d
en
e
r
g
y
s
y
s
tem
s
,
in
clu
d
in
g
m
icr
o
g
r
i
d
s
an
d
d
is
tr
ib
u
ted
n
etwo
r
k
s
,
cr
itical
f
o
r
en
er
g
y
ac
ce
s
s
an
d
r
esi
lien
ce
.
MA
R
L
is
a
k
ey
AI
tech
n
iq
u
e
ap
p
lied
h
er
e,
en
ab
lin
g
au
to
n
o
m
o
u
s
co
o
r
d
in
atio
n
am
o
n
g
d
iv
er
s
e
en
er
g
y
s
o
u
r
ce
s
(
s
o
lar
,
win
d
,
s
to
r
ag
e)
.
MA
R
L
en
s
u
r
es
d
ec
en
tr
alize
d
s
y
s
tem
s
r
esp
o
n
d
to
r
ea
l
-
tim
e
s
u
p
p
ly
a
n
d
d
em
a
n
d
ch
a
n
g
es,
o
p
tim
izin
g
en
er
g
y
d
is
tr
ib
u
tio
n
with
o
u
t
r
elian
ce
o
n
a
ce
n
tr
al
g
r
id
[3
0
]
,
[
3
3
]
.
AI
also
im
p
r
o
v
es e
n
er
g
y
s
to
r
a
g
e
m
an
ag
em
en
t w
ith
in
d
ec
en
t
r
alize
d
s
y
s
tem
s
,
u
s
in
g
m
o
d
els
lik
e
R
L
to
o
p
tim
ize
ch
a
r
g
in
g
/
d
is
ch
ar
g
in
g
cy
cles,
e
n
s
u
r
in
g
ef
f
icie
n
t
en
er
g
y
u
s
e
a
n
d
co
s
t
r
ed
u
ctio
n
[3
1
]
.
AI
-
d
r
iv
e
n
p
r
ed
ictiv
e
m
ain
ten
an
ce
e
n
h
an
ce
s
r
eliab
ilit
y
b
y
an
aly
zin
g
s
en
s
o
r
d
ata
to
p
r
ed
ict
eq
u
ip
m
en
t
f
ailu
r
es
[5
3
]
,
[
5
4
]
.
AI
’
s
ab
ilit
y
to
ad
ap
t
to
lo
ca
l
c
o
n
d
itio
n
s
a
n
d
o
p
tim
ize
e
n
er
g
y
u
s
ag
e
will b
e
im
p
o
r
tan
t
as
d
e
ce
n
tr
alize
d
s
y
s
tem
s
b
ec
o
m
e
m
o
r
e
wid
esp
r
e
ad
,
p
a
r
ticu
lar
ly
f
o
r
o
f
f
-
g
r
id
s
o
lu
tio
n
s
,
h
ig
h
lig
h
tin
g
th
eir
in
d
is
p
en
s
ab
le
f
u
tu
r
e
r
o
le
in
ac
h
iev
in
g
e
n
er
g
y
eq
u
ity
a
n
d
r
esil
ien
ce
g
lo
b
ally
.
4.
CO
NCLU
SI
O
N
AI
h
as
em
er
g
ed
as
a
k
e
y
e
n
ab
ler
o
f
ad
v
a
n
ce
m
en
ts
an
d
in
n
o
v
atio
n
in
s
o
lar
a
n
d
win
d
e
n
er
g
y
s
y
s
tem
s
,
d
eliv
er
in
g
m
ea
s
u
r
ab
le
im
p
r
o
v
em
en
ts
in
p
o
wer
f
o
r
ec
asti
n
g
,
s
y
s
tem
o
p
tim
izatio
n
,
a
n
d
p
r
e
d
ictiv
e
m
ain
ten
an
ce
.
T
h
ese
ca
p
ab
ilit
ies
co
n
tr
ib
u
te
n
o
t
o
n
ly
to
en
h
a
n
ce
d
o
p
er
a
tio
n
al
ef
f
icien
cy
an
d
r
elia
b
ilit
y
b
u
t
also
r
ed
u
ce
ca
r
b
o
n
e
m
is
s
io
n
s
an
d
lo
wer
en
er
g
y
co
s
ts
,
th
er
eb
y
s
u
p
p
o
r
tin
g
th
e
g
lo
b
al
s
h
if
t
t
o
war
d
s
u
s
tain
ab
le
an
d
ec
o
n
o
m
ically
v
iab
le
en
e
r
g
y
s
y
s
tem
s
.
T
h
e
s
cien
tific
ju
s
tific
atio
n
o
f
th
is
r
esear
ch
lies
in
it
s
f
o
cu
s
ed
s
y
n
th
esis
o
f
AI
a
p
p
licatio
n
s
s
p
ec
if
icall
y
in
s
o
lar
an
d
win
d
en
er
g
y
c
o
n
tex
ts
,
wh
ich
a
r
e
two
o
f
th
e
m
o
s
t
p
r
o
m
i
n
en
t
a
n
d
r
ap
id
ly
g
r
o
win
g
r
en
ewa
b
le
tec
h
n
o
lo
g
ies.
T
h
is
s
tu
d
y
b
r
id
g
es
a
cr
itical
g
ap
in
u
n
d
er
s
tan
d
i
n
g
h
o
w
d
iv
e
r
s
e
AI
tech
n
iq
u
es
ar
e
cu
r
r
en
tly
d
ep
lo
y
ed
an
d
wh
er
e
f
u
tu
r
e
r
esear
ch
is
m
o
s
t
n
ee
d
ed
.
Un
lik
e
b
r
o
ad
,
g
en
er
al
-
p
u
r
p
o
s
e
r
ev
iews,
th
is
wo
r
k
h
ig
h
lig
h
ts
d
o
m
ain
-
s
p
ec
if
ic
ad
v
a
n
ce
m
en
t
s
,
ch
allen
g
es,
a
n
d
o
p
p
o
r
tu
n
ities
,
p
r
o
v
id
in
g
a
tar
g
eted
k
n
o
w
led
g
e
b
ase
f
o
r
b
o
th
r
esear
ch
er
s
an
d
p
r
ac
titi
o
n
er
s
.
Desp
ite
th
e
ev
id
en
t
p
r
o
g
r
ess
,
s
ig
n
if
ican
t
ch
allen
g
es
p
er
s
is
t,
p
ar
ticu
lar
ly
ar
o
u
n
d
d
ata
q
u
ality
,
m
o
d
el
tr
an
s
p
a
r
en
cy
,
an
d
cy
b
e
r
s
ec
u
r
ity
,
wh
i
ch
co
n
tin
u
e
to
co
n
s
tr
ain
AI
'
s
f
u
ll
p
o
ten
tial
in
r
en
ewa
b
le
en
e
r
g
y
.
Ad
d
r
ess
in
g
th
ese
ch
allen
g
es
will
b
e
e
s
s
en
tial
f
o
r
s
ca
lin
g
in
tellig
en
t
en
er
g
y
s
o
lu
tio
n
s
ac
r
o
s
s
r
eg
io
n
s
an
d
u
s
e
ca
s
es.
B
y
ad
d
r
ess
in
g
th
e
id
en
tifie
d
g
ap
s
an
d
em
e
r
g
in
g
AI
m
eth
o
d
s
tailo
r
ed
f
o
r
r
en
ewa
b
le
e
n
e
r
g
y
,
f
u
tu
r
e
wo
r
k
ca
n
u
n
lo
c
k
th
e
f
u
ll
p
o
t
en
tial
o
f
in
tellig
en
t,
r
esil
ien
t,
an
d
d
ec
en
tr
alize
d
en
er
g
y
s
y
s
tem
s
.
T
h
is
s
co
p
in
g
r
ev
iew
p
r
o
v
id
es
a
f
o
u
n
d
atio
n
f
o
r
s
u
ch
en
d
ea
v
o
r
s
,
o
f
f
er
in
g
a
r
o
a
d
m
ap
f
o
r
r
esear
ch
er
s
a
n
d
p
r
ac
titi
o
n
er
s
t
o
b
u
ild
u
p
o
n
ex
is
tin
g
k
n
o
wled
g
e
an
d
co
n
tr
i
b
u
te
to
th
e
g
lo
b
al
en
er
g
y
tr
an
s
itio
n
.
T
h
e
co
n
tin
u
atio
n
o
f
th
is
r
esear
ch
co
u
ld
f
o
cu
s
o
n
th
e
f
o
llo
win
g
k
e
y
ar
ea
s
:
i)
I
m
p
r
o
v
e
d
ata
q
u
ality
an
d
in
teg
r
at
io
n
:
Fu
tu
r
e
s
tu
d
ies
s
h
o
u
ld
e
x
p
lo
r
e
s
tan
d
ar
d
ize
d
d
ata
s
ch
em
as,
r
ea
l
-
tim
e
d
ata
co
llectio
n
m
eth
o
d
s
,
an
d
ef
f
ec
tiv
e
in
teg
r
atio
n
o
f
I
o
T
d
ev
ices
an
d
wea
th
er
d
ata
to
s
tr
en
g
th
en
AI
s
y
s
tem
ac
cu
r
ac
y
an
d
r
esp
o
n
s
iv
en
ess
.
Stu
d
ies
c
o
u
ld
also
ass
ess
th
e
im
p
ac
t
o
f
m
is
s
in
g
o
r
n
o
is
y
d
ata
o
n
m
o
d
el
p
er
f
o
r
m
a
n
c
e
;
ii)
E
n
h
a
n
ce
m
o
d
el
in
ter
p
r
etab
ilit
y
an
d
t
r
u
s
t
:
C
o
n
tin
u
ed
d
ev
elo
p
m
en
t
o
f
X
AI
to
o
ls
tailo
r
e
d
to
en
e
r
g
y
s
y
s
tem
s
is
n
ee
d
ed
.
Fu
tu
r
e
wo
r
k
ca
n
f
o
cu
s
o
n
h
o
w
in
ter
p
r
etab
ilit
y
af
f
ec
ts
o
p
er
ato
r
d
ec
is
io
n
-
m
ak
in
g
an
d
s
y
s
tem
s
af
ety
,
esp
ec
ially
in
h
ig
h
-
s
tak
es a
p
p
licatio
n
s
lik
e
g
r
id
c
o
n
tr
o
l
an
d
m
ain
ten
a
n
ce
p
lan
n
in
g
;
iii)
Stre
n
g
th
e
n
c
y
b
e
r
s
ec
u
r
ity
m
ea
s
u
r
es
:
R
esear
ch
in
to
AI
m
o
d
els
th
at
ca
n
d
etec
t,
p
r
ev
en
t,
an
d
ad
ap
t
to
cy
b
er
s
ec
u
r
ity
th
r
ea
ts
in
r
ea
l
tim
e
is
n
ee
d
ed
,
in
clu
d
in
g
t
h
e
d
ev
elo
p
m
e
n
t
o
f
r
esil
ien
t
ar
ch
itectu
r
es
an
d
lea
r
n
in
g
al
g
o
r
ith
m
s
th
at
m
ain
tai
n
f
u
n
ctio
n
ality
u
n
d
er
attac
k
;
iv
)
E
x
p
a
n
d
AI
-
I
o
T
s
y
n
er
g
ies:
Fu
r
th
er
s
tu
d
ies
s
h
o
u
ld
in
v
esti
g
ate
s
ca
lab
le
a
r
ch
itectu
r
es
f
o
r
r
ea
l
-
tim
e
A
I
-
I
o
T
in
teg
r
atio
n
,
p
ar
ticu
lar
ly
f
o
r
o
f
f
-
g
r
id
s
y
s
tem
s
.
E
m
er
g
in
g
ar
ea
s
lik
e
ed
g
e
AI
an
d
f
ed
er
ated
lear
n
in
g
ca
n
b
e
ex
p
lo
r
e
d
to
r
ed
u
ce
laten
cy
a
n
d
im
p
r
o
v
e
a
d
ap
tab
ilit
y
;
an
d
v
)
Su
p
p
o
r
t
d
ec
en
tr
alize
d
en
er
g
y
s
y
s
tem
s
with
AI
:
T
h
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ies
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ies wo
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ain
.
F
UNDING
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wh
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ee
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o
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th
is
p
ap
er
.
AUTHO
R
CO
NT
RI
B
UT
I
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NS ST
A
T
E
M
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N
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h
is
jo
u
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al
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ib
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to
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R
o
les
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ax
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y
(
C
R
ed
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to
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n
ize
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al
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ed
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ce
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th
o
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s
h
ip
d
is
p
u
tes,
an
d
f
ac
ilit
ate
co
llab
o
r
atio
n
.
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