Sustainability,
Journal Year:
2022,
Volume and Issue:
14(8), P. 4832 - 4832
Published: April 18, 2022
With
population
increases
and
a
vital
need
for
energy,
energy
systems
play
an
important
decisive
role
in
all
of
the
sectors
society.
To
accelerate
process
improve
methods
responding
to
this
increase
demand,
use
models
algorithms
based
on
artificial
intelligence
has
become
common
mandatory.
In
present
study,
comprehensive
detailed
study
been
conducted
applications
Machine
Learning
(ML)
Deep
(DL),
which
are
newest
most
practical
Artificial
Intelligence
(AI)
systems.
It
should
be
noted
that
due
development
DL
algorithms,
usually
more
accurate
less
error,
these
ability
model
solve
complex
problems
field.
article,
we
have
tried
examine
very
powerful
problem
solving
but
received
attention
other
studies,
such
as
RNN,
ANFIS,
RBN,
DBN,
WNN,
so
on.
This
research
uses
knowledge
discovery
databases
understand
ML
systems’
current
status
future.
Subsequently,
critical
areas
gaps
identified.
addition,
covers
efficient
used
field;
optimization,
forecasting,
fault
detection,
investigated.
Attempts
also
made
cover
their
evaluation
metrics,
including
not
only
important,
newer
ones
attention.
Sustainability,
Journal Year:
2023,
Volume and Issue:
15(9), P. 7087 - 7087
Published: April 23, 2023
This
article
presents
a
review
of
current
advances
and
prospects
in
the
field
forecasting
renewable
energy
generation
using
machine
learning
(ML)
deep
(DL)
techniques.
With
increasing
penetration
sources
(RES)
into
electricity
grid,
accurate
their
becomes
crucial
for
efficient
grid
operation
management.
Traditional
methods
have
limitations,
thus
ML
DL
algorithms
gained
popularity
due
to
ability
learn
complex
relationships
from
data
provide
predictions.
paper
reviews
different
approaches
models
that
been
used
discusses
strengths
limitations.
It
also
highlights
challenges
future
research
directions
field,
such
as
dealing
with
uncertainty
variability
generation,
availability,
model
interpretability.
Finally,
this
emphasizes
importance
developing
robust
enable
integration
RES
facilitate
transition
towards
sustainable
future.
Energy Reports,
Journal Year:
2022,
Volume and Issue:
8, P. 8965 - 8980
Published: July 14, 2022
As
a
clean
and
renewable
energy
source,
wind
power
is
of
great
significance
for
addressing
global
shortages
environmental
pollution.
However,
the
uncertainty
speed
hinders
direct
use
power,
resulting
in
high
proportion
abandoned
wind.
Therefore,
accurate
prediction
improving
utilization
rate
energy.
In
this
study,
hybrid
model
proposed
based
on
seasonal
autoregressive
integrated
moving
average
(SARIMA),
ensemble
empirical
mode
decomposition
(EEMD),
long
short-term
memory
(LSTM)
methods.
First,
original
data
were
resampled
to
obtain
within
time
scales
15,
30,
60
min.
The
SARIMA
was
used
extract
linear
features
nonlinear
residual
sequences
series
at
different
scales,
EEMD
decompose
sequence
intrinsic
functions
(IMFs)
sub-residual
sequences.
For
IMFs
obtained
after
decomposition,
LSTM
method
training,
predicted
IMFs,
sequence,
series,
final
speed.
To
verify
superiority
large
farm
as
case
study.
Finally,
compared
with
other
models,
verifying
that
experimental
has
higher
accuracy.
PLoS ONE,
Journal Year:
2023,
Volume and Issue:
18(1), P. e0280006 - e0280006
Published: Jan. 3, 2023
Monkey
king
evolution
(MKE)
is
a
population-based
differential
evolutionary
algorithm
in
which
the
single
strategy
and
control
parameter
affect
convergence
balance
between
exploration
exploitation.
Since
strategies
have
considerable
impact
on
performance
of
algorithms,
collaborating
multiple
can
significantly
enhance
abilities
algorithms.
This
our
motivation
to
propose
multi-trial
vector-based
monkey
named
MMKE.
It
introduces
novel
best-history
trial
vector
producer
(BTVP)
random
(RTVP)
that
effectively
collaborate
with
canonical
MKE
(MKE-TVP)
using
approach
tackle
various
real-world
optimization
problems
diverse
challenges.
expected
proposed
MMKE
improve
global
search
capability,
strike
exploitation,
prevent
original
from
converging
prematurely
during
process.
The
was
assessed
CEC
2018
test
functions,
results
were
compared
eight
metaheuristic
As
result
experiments,
it
demonstrated
capable
producing
competitive
superior
terms
accuracy
rate
comparison
comparative
Additionally,
Friedman
used
examine
gained
experimental
statistically,
proving
Furthermore,
four
engineering
design
optimal
power
flow
(OPF)
problem
for
IEEE
30-bus
system
are
optimized
demonstrate
MMKE's
real
applicability.
showed
handle
difficulties
associated
able
solve
multi-objective
OPF
better
solutions
than
