Applied Energy,
Journal Year:
2021,
Volume and Issue:
301, P. 117461 - 117461
Published: Aug. 6, 2021
Due
to
the
strong
randomness
of
wind
speed,
power
generation
is
difficult
integrate
into
grid.
It
very
important
predict
speed
reliably
and
accurately
so
that
energy
can
be
utilized
effectively.
In
this
study,
obtain
accurate
prediction
results,
a
combined
VMD-D-ESN
model
based
on
variational
mode
decomposition
(VMD),
double-layer
staged
training
echo
state
network
(D-ESN)
genetic
algorithm
(GA)
optimization
proposed.
First,
preprocesses
original
data
with
VMD
then
uses
D-ESN
each
decomposed
subsequence.
Lastly,
final
value
obtained
by
combining
all
predicted
subsequences.
model's
structure,
first
layer
selects
length
set,
second
has
ability
correct
error
in
layer.
practical
application
case
using
six
different
collection
sites,
ten
models
are
established
compare
performance
proposed
model.
Compared
other
traditional
models,
results
show
combines
structure
achieves
high
accuracy
stability
available
datasets.
Additionally,
also
shows
use
strongly
improves
Data Science and Management,
Journal Year:
2022,
Volume and Issue:
5(2), P. 84 - 95
Published: June 1, 2022
Accurate
forecasting
results
are
crucial
for
increasing
energy
efficiency
and
lowering
consumption
in
wind
energy.
Big
data
artificial
intelligence
(AI)
have
great
potential
forecasting.
Although
the
literature
on
this
subject
is
extensive,
it
lacks
a
comprehensive
research
status
survey.
In
identifying
evolution
rules
of
big
AI
methods
forecasting,
paper
summarizes
studies
over
last
two
decades.
The
existing
types,
analysis
techniques,
classified
sorted
by
combining
reviews
scientometrics
methods.
Furthermore,
trend
determined
based
combing
hotspots
frontier
progress.
Finally,
research's
opportunities,
challenges,
implications
from
various
perspectives.
serve
as
foundation
future
promote
further
development
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.
