2022 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia),
Journal Year:
2023,
Volume and Issue:
unknown, P. 1340 - 1345
Published: July 7, 2023
Extreme
weather
conditions
can
have
a
significant
impact
on
the
electricity
load
demand
and
energy
management
programs
thus
cause
unexpected
blackouts
in
systems.
To
predict
extreme
conditions,
it
is
important
to
consider
different
historical
data
analysis.
This
paper
proposes
an
intelligent
adversarial
model
for
prediction
of
consumers'
electric
condition.
By
analyzing
past
events
associated
demand,
new
predictive
deep
learning
developed
that
be
used
estimate
future
conditions.
The
proposed
constructed
based
generative
network
(GAN)
dragonfly
algorithm
(DA)
make
precise
prediction.
generator
trained
produce
are
similar
data,
while
discriminator
correctly
classify
real
from
generated
ones.
A
modified
DA
suggested
enhance
GAN
training
through
iterative
process.
dataset
California
over
years
2015–2020
examine
accuracy
model.
Energies,
Journal Year:
2023,
Volume and Issue:
16(3), P. 1404 - 1404
Published: Jan. 31, 2023
The
smart
grid
concept
is
introduced
to
accelerate
the
operational
efficiency
and
enhance
reliability
sustainability
of
power
supply
by
operating
in
self-control
mode
find
resolve
problems
developed
time.
In
grid,
use
digital
technology
facilitates
with
an
enhanced
data
transportation
facility
using
sensors
known
as
meters.
Using
these
meters,
various
functionalities
can
be
enhanced,
such
generation
scheduling,
real-time
pricing,
load
management,
quality
enhancement,
security
analysis
enhancement
system,
fault
prediction,
frequency
voltage
monitoring,
forecasting,
etc.
From
bulk
generated
a
architecture,
precise
predicted
before
time
support
energy
market.
This
supports
operation
maintain
balance
between
demand
generation,
thus
preventing
system
imbalance
outages.
study
presents
detailed
review
on
forecasting
category,
calculation
performance
indicators,
analyzing
process
for
conventional
meter
information,
used
conduct
task
its
challenges.
Next,
importance
meter-based
discussed
along
available
approaches.
Additionally,
merits
conducted
over
are
articulated
this
paper.
Electronics,
Journal Year:
2023,
Volume and Issue:
12(10), P. 2175 - 2175
Published: May 10, 2023
Accurate
power
load
forecasting
can
facilitate
effective
distribution
of
and
avoid
wasting
so
as
to
reduce
costs.
Power
is
affected
by
many
factors,
accurate
more
difficult,
the
current
methods
are
mostly
aimed
at
short-term
problems.
There
no
good
method
for
long-term
Aiming
this
problem,
paper
proposes
an
LSTM-Informer
model
based
on
ensemble
learning
solve
problem.
The
bottom
layer
uses
long
memory
network
(LSTM)
a
learner
capture
time
correlation
load,
top
Informer
dependence
problem
forecasting.
In
way,
not
only
but
also
accurately
predict
load.
paper,
one-year
dataset
in
city
Tetouan
northern
Morocco
was
used
experiments,
mean
square
error
(MSE)
absolute
(MAE)
were
evaluation
criteria.
prediction
0.58
0.38
higher
than
that
lstm
MSE
MAE.
experimental
results
show
has
advantages
advanced
baseline
method.
