2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC),
Год журнала:
2023,
Номер
unknown, С. 165 - 169
Опубликована: Дек. 8, 2023
To
improve
the
utilization
efficiency
of
wind
energy,
this
research
proposes
a
hybrid
model
based
on
Temporal
Convolutional
Network
(TCN)
and
two-level
speed
decomposition.
Firstly,
original
data
is
decomposed
into
main
residual
signals
through
Singular
Spectrum
Analysis
(SSA).
Then,
usage
Variational
mode
decomposition
(VMD)
decomposes
several
sub-sequences.
The
next
step
involves
predicting
signal
all
sub-sequences
using
TCN.
Eventually,
Grey
Wolf
Optimizer
(GWO)
employed
to
perform
optimization
stack
prediction
results,
resulting
in
outcomes.
results
demonstrate
that
proposed
SSA-VMD-TCN-GWO
outperforms
reference
models.
Thus,
provides
new
solution
International Transactions on Electrical Energy Systems,
Год журнала:
2025,
Номер
2025(1)
Опубликована: Янв. 1, 2025
Due
to
the
intermittent
and
fluctuating
nature
of
wind
power
generation,
it
is
difficult
achieve
desired
prediction
accuracy
for
prediction.
For
this
reason,
paper
proposes
a
combined
model
based
on
Pearson
correlation
coefficient
method,
multivariate
variational
mode
decomposition
(MVMD),
African
vultures
optimization
algorithm
(AVOA)
leader–follower
patterns,
convolutional
neural
network
(CNN),
long
short‐term
memory
(LSTM),
attention
mechanism
(AM).
Firstly,
method
used
filter
out
meteorological
data
with
strong
relationship
establish
dataset;
subsequently,
MVMD
decompose
original
into
multiple
subsequences
in
order
handle
better.
Thereafter,
optimize
hyperparameters
CNN‐LSTM
algorithm,
AM
added
increase
effect,
decomposed
are
predicted
separately,
values
each
subsequence
superimposed
obtain
final
value.
Finally,
effectiveness
verified
using
from
farm
Shenyang.
The
results
show
that
MAE
established
MVMD‐AVA‐CNN‐LSTM‐AM
2.0467,
MSE
2.8329.
Compared
other
models,
significantly
improved,
had
better
generalization
ability
robustness,
robustness.
Axioms,
Год журнала:
2023,
Номер
12(3), С. 250 - 250
Опубликована: Март 1, 2023
This
work
proposed
a
new
hybridised
network
of
3-Satisfiability
structures
that
widens
the
search
space
and
improves
effectiveness
Hopfield
by
utilising
fuzzy
logic
metaheuristic
algorithm.
The
method
effectively
overcomes
downside
current
structure,
which
uses
Boolean
creating
diversity
in
space.
First,
we
included
into
system
to
make
bipolar
structure
change
continuous
while
keeping
its
structure.
Then,
Genetic
Algorithm
is
employed
optimise
solution.
Finally,
return
answer
initial
form
casting
it
framework
hybrid
function
between
two
procedures.
suggested
network’s
performance
was
trained
validated
using
Matlab
2020b.
techniques
significantly
obtain
better
results
terms
error
analysis,
efficiency
evaluation,
energy
similarity
index,
computational
time.
outcomes
validate
significance
results,
this
comes
from
fact
model
has
positive
impact.
information
concepts
will
be
used
develop
an
efficient
gathering
for
subsequent
investigation.
development
with
presents
viable
strategy
mining
applications
future.
Sensors,
Год журнала:
2023,
Номер
23(2), С. 755 - 755
Опубликована: Янв. 9, 2023
The
Aquila
Optimizer
(AO)
is
a
new
bio-inspired
meta-heuristic
algorithm
inspired
by
Aquila’s
hunting
behavior.
Adaptive
Combining
Niche
Thought
with
Dispersed
Chaotic
Swarm
(NCAAO)
proposed
to
address
the
problem
that
although
has
strong
global
exploration
capability,
it
an
insufficient
local
exploitation
capability
and
slow
convergence
rate.
First,
improve
diversity
of
populations
in
uniformity
distribution
search
space,
DLCS
chaotic
mapping
used
generate
initial
so
better
state.
Then,
accuracy
algorithm,
adaptive
adjustment
strategy
de-searching
preferences
proposed.
development
phases
NCAAO
are
effectively
balanced
changing
threshold
introducing
position
weight
parameter
adaptively
adjust
process.
Finally,
idea
small
habitats
promote
exchange
information
between
groups
accelerate
rapid
optimal
solution.
To
verify
optimization
performance
improved
was
tested
on
15
standard
benchmark
functions,
Wilcoxon
rank
sum
test,
engineering
problems
test
optimization-seeking
ability
algorithm.
experimental
results
show
faster
speed
compared
other
intelligent
algorithms.
