In
this
study,
a
novel
heuristic
computing
technique
is
presented
to
solve
bioinformatics
problem
for
the
corneal
shape
model
of
eye
surgery
using
Morlet
wavelet
artificial
neural
network
optimized
by
global
search
schemes,
i.e.
genetic
algorithm
(GA),
local
technique,
sequential
quadratic
programming
(SQP)
and
hybrid
GA-SQP.
To
measure
performance
design
configuration,
different
cases
based
on
nonlinear
second-order
differential
equations
governing
have
been
solved
effectively.
The
numerical
procedure
Adams
method
implemented
comparison
purpose
outcomes
stochastic
solver,
which
shows
worth
present
scheme
accuracy
convergence
with
negligible
values
absolute
error
in
range
10[Formula:
see
text]
text].
Furthermore,
statistical
measures
are
“mean
error”,
“root
mean
square
error”
“coefficient
Theil’s
inequality”
additionally
endorsed
consistently
accurate
integrated
intelligent
framework
solving
model.
Energies,
Год журнала:
2023,
Номер
16(4), С. 1786 - 1786
Опубликована: Фев. 10, 2023
The
use
of
fossil-fueled
power
stations
to
generate
electricity
has
had
a
damaging
effect
over
the
years,
necessitating
need
for
alternative
energy
sources.
Microgrids
consisting
renewable
source
concepts
have
gained
lot
consideration
in
recent
years
as
an
because
they
advances
information
and
communication
technology
(ICT)
increase
quality
efficiency
services
distributed
resources
(DERs),
which
are
environmentally
friendly.
Nevertheless,
microgrids
constrained
by
outbreaks
faults,
impact
on
their
performance
necessitate
dynamic
management
optimization
strategies.
application
artificial
intelligence
(AI)
is
gaining
momentum
vital
key
at
this
point.
This
study
focuses
comprehensive
review
applications
strategies
hybrid
optimization,
enhancement,
analyses
fault
microgrids.
techniques
such
machine
learning
(ML),
genetic
algorithms
(GA),
neural
networks
(ANN),
fuzzy
logic
(FL),
particle
swarm
(PSO),
heuristic
bee
colony
(ABC),
others
reviewed
various
microgrid
regression
classification
study.
Applications
AI
together
with
benefits,
drawbacks,
prospects
future.
coordination
maximum
penetration
energy,
solar
PV,
wind
under
furthermore
reviewed.
Energies,
Год журнала:
2023,
Номер
16(24), С. 8057 - 8057
Опубликована: Дек. 14, 2023
This
review
paper
provides
a
summary
of
methods
in
which
artificial
intelligence
(AI)
techniques
have
been
applied
the
management
variable
renewable
energy
(VRE)
systems,
and
an
outlook
to
future
directions
research
field.
The
VRE
types
included
are
namely
solar,
wind
marine
varieties.
AI
techniques,
particularly
machine
learning
(ML),
gained
traction
as
result
data
explosion,
offer
method
for
integration
multimodal
more
accurate
forecasting
applications.
aspects
include
optimized
power
generation
into
grids,
including
demand
forecasting,
storage,
system
optimization,
performance
monitoring,
cost
management.
Future
applications
proposed
discussed,
issue
availability,
quality,
addition
explainable
(XAI),
quantum
(QAI),
coupling
with
emerging
digital
twins
technology,
natural
language
processing.
Heliyon,
Год журнала:
2023,
Номер
9(11), С. e21484 - e21484
Опубликована: Ноя. 1, 2023
As
one
of
the
future's
most
promising
clean
energy
sources,
solar
is
key
to
developing
renewable
energy.
The
randomness
irradiance
can
affect
efficiency
photovoltaic
power
generation,
which
makes
generation
planning
extremely
difficult.
main
goal
this
study
accurately
predict
and
establish
a
prediction
model
with
meteorological
characteristics
improve
accuracy.
This
paper
proposes
convolutional
neural
network
(CNN)
attention
mechanism-based
long
short-term
memory
(A-LSTM)
next
day.
In
addition,
accuracy
further
improved
by
combining
similar
day
analyses.
A
constructed
selecting
data
from
Andhra
Pradesh,
India.
experimental
results
show
that
method
proposed
in
more
accurately,
providing
new
idea
for
planning.
IEEE Access,
Год журнала:
2024,
Номер
12, С. 103299 - 103312
Опубликована: Янв. 1, 2024
The
rapid
increase
in
energy
demand
and
the
disadvantages
of
using
fossil
fuels
electricity
production
have
led
to
a
greater
emphasis
on
renewable
sources.
Consequently,
research
use
resources
has
gained
importance.
Numerous
factors
influence
power
plants
that
generate
from
these
Power
utilizing
solar
energy,
one
sources,
are
significantly
affected
by
environmental
meteorological
variables,
impacting
continuity
electrical
(SPPs).
For
reasons,
this
study
developed
prediction
models
two
different
methods
based
machine
learning
artificial
intelligence
analyze
predict
changes
SPPs
due
changes.
data
used
real
collected
180
kWe
plant
currently
operation.
Data
collection
started
day
was
commissioned.
Using
data,
effects
pollution
impacts
PV
panels'
demonstrated.
To
mitigate
examine
impact
adverse
conditions
efficiency,
analysis
were
used:
Random
Forest
Regression
(RFR)
Model
Artificial
Neural
Networks
(ANN).
This
allowed
for
comparison
results
between
models.
Long
Short-Term
Memory
(LSTM)
networks,
type
neural
network,
utilized.
A
model
created
decrease
(RF)
regression
analysis,
which
analyzes
non-linear
independent
input
variables
creates
model.
estimated
SPP's
measurements
pollution.
graph
comparing
amounts
with
actual
values
is
shown.
In
another
phase,
networks
trained
SPP
measurement
station
networks.
LSTM
shown
graphically.
very
large
set
training
includes
hourly
sunshine
duration,
accumulated
irradiation
(Wh/m2),
maximum
temperature,
minimum
humidity
(%),
total
precipitation
(kg/m2),
daily
since
began
It
consists
wind
speed
(m/s),
pollution,
plant.
means
119,808
points
processed
model,
highlighting
detail
analysis.
evaluated
four
performance
measures:
correlation
coefficient
(R),
fractional
gross
error
(FGE),
mean
standard
(MBE),
root
square
(RMSE).
RF
showed
0.8111
predictions.
contrast,
network
predictions
had
an
R
value
0.9759.
Comparing
RFR
LSTM,
it
evident
provides
much
better
entire
set.
Photonics,
Год журнала:
2025,
Номер
12(3), С. 286 - 286
Опубликована: Март 20, 2025
Solar
photovoltaic
(PV)
technology
is
developing
quickly
due
to
the
continual
rise
in
demand
for
energy
and
environmental
protection.
thermal
(STPV)
systems
can
break
Shockley–Queisser
limit
of
conventional
PV
by
reshaping
solar
spectrum
using
selective
absorbers
emitters.
However,
traditional
design
method
relies
on
designer’s
experience,
which
fails
achieve
rapid
designing
STPV
devices
greatly
improve
performance.
In
this
paper,
an
thin-film
emitter
inversely
designed
based
a
genetic
algorithm.
The
optimized
structure
consists
SiO2
SiC
layers
alternately
stacked
Cr
substrate,
whose
emissivity
reach
0.99
at
1.86
μm.
When
combined
with
InGaAsSb
cell,
power
conversion
efficiency
be
up
43.3%
1673
K.
This
straightforward
easily
scalable
film
gain
excellent
efficiency,
promotes
practical
application
systems.
