Electronics,
Journal Year:
2024,
Volume and Issue:
13(23), P. 4829 - 4829
Published: Dec. 6, 2024
Wind
speed,
wind
direction,
humidity,
temperature,
altitude,
and
other
factors
affect
power
generation,
the
uncertainty
instability
of
above
bring
challenges
to
regulation
control
which
requires
flexible
management
scheduling
strategies.
Therefore,
it
is
crucial
improve
accuracy
ultra-short-term
prediction.
To
solve
this
problem,
paper
proposes
an
prediction
method
with
MIVNDN.
Firstly,
Spearman’s
Kendall’s
correlation
coefficients
are
integrated
select
appropriate
features.
Secondly,
multi-strategy
dung
beetle
optimization
algorithm
(MSDBO)
used
optimize
parameter
combinations
in
improved
complete
ensemble
empirical
mode
decomposition
adaptive
noise
(ICEEMDAN)
method,
optimized
decompose
historical
sequence
obtain
a
series
intrinsic
modal
function
(IMF)
components
different
frequency
ranges.
Then,
high-frequency
band
IMF
low-frequency
reconstructed
using
t-mean
test
sample
entropy,
component
decomposed
quadratically
variational
(VMD)
new
set
components.
Finally,
Nons-Transformer
model
by
adding
dilated
causal
convolution
its
encoder,
components,
as
well
unreconstructed
mid-frequency
IMF,
inputs
results
perform
error
analysis.
The
experimental
show
that
our
proposed
outperforms
single
combined
models.
