Energy Reports,
Journal Year:
2021,
Volume and Issue:
7, P. 1217 - 1233
Published: Feb. 21, 2021
Due
to
the
intermittent,
fluctuating
and
random
characteristics
of
wind
system,
output
power
will
become
unstable
with
change
wind,
which
brings
severe
challenges
safe
stable
operation
system.
An
effective
way
solve
this
problem
is
accurately
forecast
speed.
This
paper
presents
a
novel
speed
combination
forecasting
model
based
on
decomposition.
The
innovation
as
follows.
(a)
In
view
speed,
variational
mode
decomposition
algorithm
introduced
decompose
historical
data
obtain
series
components
different
frequencies.
(b)
Echo
state
network
good
ability
selected
each
component.
(c)
To
that
performance
echo
greatly
affected
by
parameters
reservoir,
an
improved
whale
optimization
proposed
optimize
these
parameters.
optimized
improves
effect.
(d)
final
results
are
obtained
adding
values
(e)
developed
verified
using
two
actual
collected
sets
ultra-short-term
short-term
Compared
some
state-of-the-art
models,
comparison
result
curve
between
value
error
distribution,
histogram
indicators,
related
statistical
Taylor
diagram
show
has
higher
prediction
accuracy
able
reflect
laws
correctly.
IEEE Transactions on Intelligent Transportation Systems,
Journal Year:
2020,
Volume and Issue:
22(9), P. 5566 - 5576
Published: May 8, 2020
Accurate
prediction
of
the
traffic
state
can
help
to
address
issue
congestion,
providing
guiding
advices
for
people's
travel
and
regulation.
In
this
paper,
we
propose
a
novel
short-term
flow
approach
based
on
empirical
mode
decomposition
combination
model
fusion.
First,
explore
amplitude-frequency
characteristics
series,
use
decompose
several
components
with
different
frequency.
Second,
results
self-similarity
analysis
each
component,
improved
extreme
learning
machine,
seasonal
auto
regressive
integrated
moving
average
are
selected
predict
components.
Meanwhile,
an
fruit
fly
optimization
algorithm
is
proposed
optimize
weight
coefficient
model.
Third,
multiplied
by
their
respective
get
final
results.
We
evaluate
our
doing
thorough
experiment
real
data
set.
Moreover,
experimental
show
that
has
superior
performance
than
state-of-the-art
methods
or
models
in
prediction.
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
