Sustainability,
Journal Year:
2022,
Volume and Issue:
14(24), P. 16426 - 16426
Published: Dec. 8, 2022
The
scientific
prediction
of
energy
consumption
plays
an
essential
role
in
grasping
trends
and
optimizing
structures.
Energy
will
be
affected
by
many
factors.
In
this
paper,
order
to
improve
the
accuracy
model,
grey
correlation
analysis
method
is
used
analyze
relevant
First,
factor
with
largest
degree
selected,
then
a
new
multivariable
convolution
model
dual
orders
established.
Different
fractional
are
accumulate
target
data
sequence
influencing-factor
sequence,
optimized
particle
swarm
optimization
algorithm.
fit
test
Shanghai,
Guizhou
Shandong
provinces
China
from
2011
2020
compared
other
models.
Experimental
results
MAPE
RMSPE
measurements
show
that
our
improved
reasonable
effective
prediction.
At
same
time,
applied
forecast
three
regions
2021
2025,
providing
reliable
information
for
future
distribution.
