Heliyon,
Journal Year:
2025,
Volume and Issue:
11(2), P. e41765 - e41765
Published: Jan. 1, 2025
Effectively
managing
and
optimizing
energy
resources
to
accommodate
population
growth
while
minimizing
carbon
emissions
has
become
increasingly
intricate.
A
proficient
approach
this
dilemma
is
accurately
predicting
usage
across
diverse
sectors.
This
paper
unveils
a
genetic
algorithm
(GA)-optimized
support
vector
regression
(SVR)
model
designed
(i)
predict
electricity
generation,
(ii)
consumption
in
four
primary
sectors—residential,
industrial,
commercial,
agricultural,
(iii)
estimate
sector-specific
emissions.
The
proposed
model's
efficacy
assessed
by
calculating
the
R2
value,
mean
absolute
error
(MAE),
root
squared
(RMSE),
residual
plot.
achieved
high
accuracy
with
an
MAE
of
1.18
%,
yielded
reliable
sectoral
predictions,
reflected
values
1.22
%
(residential),
4.98
(industrial),
4.40
(commercial),
4.04
(agricultural).
residuals
exhibited
homoscedasticity,
value
approached
one.
predicts
that
2027,
residential
sector
will
consume
55748.66
GWh
energy,
commercial
14892.49
GWh,
industrial
32642.35
agricultural
2288.37
GWh.
It
been
predicted
these
sectors
release
75437.96-billion-gram
equivalents.
Sensors,
Journal Year:
2023,
Volume and Issue:
23(6), P. 3202 - 3202
Published: March 17, 2023
Insulators
installed
outdoors
are
vulnerable
to
the
accumulation
of
contaminants
on
their
surface,
which
raise
conductivity
and
increase
leakage
current
until
a
flashover
occurs.
To
improve
reliability
electrical
power
system,
it
is
possible
evaluate
development
fault
in
relation
thus
predict
whether
shutdown
may
occur.
This
paper
proposes
use
empirical
wavelet
transform
(EWT)
reduce
influence
non-representative
variations
combines
attention
mechanism
with
long
short-term
memory
(LSTM)
recurrent
network
for
prediction.
The
Optuna
framework
has
been
applied
hyperparameter
optimization,
resulting
method
called
optimized
EWT-Seq2Seq-LSTM
attention.
proposed
model
had
10.17%
lower
mean
square
error
(MSE)
than
standard
LSTM
5.36%
MSE
without
showing
that
optimization
promising
strategy.
Energies,
Journal Year:
2024,
Volume and Issue:
17(7), P. 1662 - 1662
Published: March 30, 2024
Distribution
System
Operators
(DSOs)
and
Aggregators
benefit
from
novel
energy
forecasting
(EF)
approaches.
Improved
accuracy
may
make
it
easier
to
deal
with
imbalances
between
generation
consumption.
It
also
helps
operations
such
as
Demand
Response
Management
(DRM)
in
Smart
Grid
(SG)
architectures.
For
utilities,
companies,
consumers
manage
resources
effectively
educated
decisions
about
consumption,
EF
is
essential.
many
applications,
Energy
Load
Forecasting
(ELF),
Generation
(EGF),
grid
stability,
accurate
crucial.
The
state
of
the
art
examined
this
literature
review,
emphasising
cutting-edge
techniques
technologies
their
significance
for
industry.
gives
an
overview
statistical,
Machine
Learning
(ML)-based,
Deep
(DL)-based
methods
ensembles
that
form
basis
EF.
Various
time-series
are
explored,
including
sequence-to-sequence,
recursive,
direct
forecasting.
Furthermore,
evaluation
criteria
reported,
namely,
relative
absolute
metrics
Mean
Absolute
Error
(MAE),
Root
Square
(RMSE),
Percentage
(MAPE),
Coefficient
Determination
(R2),
Variation
(CVRMSE),
well
Execution
Time
(ET),
which
used
gauge
prediction
accuracy.
Finally,
overall
step-by-step
standard
methodology
often
utilised
problems
presented.
Journal of Pipeline Science and Engineering,
Journal Year:
2024,
Volume and Issue:
5(1), P. 100220 - 100220
Published: Aug. 23, 2024
The
foundation
of
natural
gas
intelligent
scheduling
is
the
accurate
prediction
consumption
(NGC).
However,
because
its
volatility,
this
brings
difficulties
and
challenges
in
accurately
predicting
NGC.
To
address
problem,
an
improved
model
developed
combining
sparrow
search
algorithm
(ISSA),
long
short-term
memory
(LSTM),
wavelet
transform
(WT).
First,
performance
ISSA
tested.
Second,
NGC
divided
into
several
high-
low-frequency
components
applying
different
layers
Coilfets',
Fejer-Korovkins',
Symletss',
Haars',
Discretes'
orders.
In
addition,
LSTM
applied
to
forecast
decomposed
view
one-
multi-step,
hyper-parameters
are
optimized
by
ISSA.
At
last,
final
results
reconstructed.
research
indicate
that:
(1)
Comparing
other
machine
algorithms
(e.g.
fuzzy
neural
network),
convergence
speed
stability
stronger
standard
deviation
mean;
(2)
better
than
that
forecasting
models;
(3)
single-step
superior
two-,
three-,
four-
step;
(4)
computational
load
proposed
highest
compared
models,
accuracy
still
excellent
on
extended
time
series.
