Aiming
at
the
huge
interference
of
abnormal
information
speech
to
backtracking
power
events
and
evaluation
dispatching
process,
a
recognition
model
is
designed.
The
collected
initial
recording
files
are
preprocessed
by
means
discretization,
filtering
framing.
cepstrum
feature
used
describe
voiceprint
staff,
spectrum
mapped
Mel
energy
filter,
static
dynamic
features
extracted
analysis.
According
short-time
steady-state
characteristics
signal,
energy,
average
amplitude
zero
crossing
rate
analyzed
in
time
domain
taken
as
segment
threshold.
Based
on
information,
time-frequency
sequence
matrix
same
frequency
point
established,
obtained
combining
trend
regression
parameters.
experimental
data
show
that
has
good
immune
performance
for
common
noise
characteristic
dimensions,
significant
advantages
accuracy
real-time,
ability
ensure
quality
system.
Energy & Fuels,
Journal Year:
2024,
Volume and Issue:
38(3), P. 1593 - 1617
Published: Jan. 16, 2024
This
review
illuminates
the
pivotal
synergy
between
machine
learning
(ML)
and
biopolymers,
spotlighting
their
combined
potential
to
reshape
sustainable
energy,
fuels,
biochemicals.
Biobased
polymers,
derived
from
renewable
sources,
have
garnered
attention
for
roles
in
energy
fuel
sectors.
These
when
integrated
with
ML
techniques,
exhibit
enhanced
functionalities,
optimizing
systems,
storage,
conversion.
Detailed
case
studies
reveal
of
biobased
polymers
applications
industry,
further
showcasing
how
bolsters
efficiency
innovation.
The
intersection
also
marks
advancements
biochemical
production,
emphasizing
innovations
drug
delivery
medical
device
development.
underscores
imperative
harnessing
convergence
future
global
sustainability
endeavors
collective
evidence
presented
asserts
immense
promise
this
union
holds
steering
a
innovative
trajectory.
Journal of Forecasting,
Journal Year:
2024,
Volume and Issue:
43(6), P. 2064 - 2087
Published: March 11, 2024
Abstract
Wind
power
has
emerged
as
a
successful
component
within
systems.
The
ability
to
reliably
and
accurately
forecast
wind
speed
is
of
great
importance
in
maintaining
the
security
stability
grid.
However,
significance
explaining
prediction
models
often
been
overlooked
by
researchers.
To
address
this
gap,
study
introduces
novel
approach
forecasting
that
incorporates
significant
decomposition
method,
attention‐based
machine
learning,
local
explanation
techniques.
proposed
model
utilizes
grid
search
variational
mode
decompose
sequence
into
different
modes
while
employing
gate
recurrent
unit
with
an
attention
mechanism
achieve
superior
performance.
Experimental
evaluations
conducted
on
eight
real‐world
datasets
demonstrate
outperforms
other
popular
across
multiple
performance
criteria.
In
two
specific
experiments,
achieved
minimal
mean
absolute
percentage
error
2.74%
1.70%,
respectively.
Furthermore,
interpretable
model‐agnostic
explanations
(LIME)
were
employed
assess
influence
factors,
highlighting
whether
they
positively
or
negatively
affected
predicted
values.
Energies,
Journal Year:
2023,
Volume and Issue:
16(4), P. 1841 - 1841
Published: Feb. 13, 2023
This
study
proposes
an
effective
wind
speed
forecasting
model
combining
a
data
processing
strategy,
neural
network
predictor,
and
parameter
optimization
method.
(a)
Variational
mode
decomposition
(VMD)
is
adopted
to
decompose
the
into
multiple
subseries
where
each
contains
unique
local
characteristics,
all
are
converted
two-dimensional
samples.
(b)
A
gated
recurrent
unit
(GRU)
sequentially
modeled
based
on
obtained
samples
makes
predictions
for
future
speed.
(c)
The
grid
search
with
rolling
cross-validation
(GSRCV)
designed
simultaneously
optimize
key
parameters
of
VMD
GRU.
To
evaluate
effectiveness
proposed
VMD-GRU-GSRCV
model,
comparative
experiments
hourly
collected
from
National
Renewable
Energy
Laboratory
implemented.
Numerical
results
show
that
root
mean
square
error,
absolute
percentage
symmetric
error
this
reach
0.2047,
0.1435,
3.77%,
3.74%,
respectively,
which
outperform
benchmark
using
popular
methods,
techniques,
hybrid
models.
