International Journal of Ambient Energy,
Journal Year:
2024,
Volume and Issue:
45(1)
Published: Oct. 17, 2024
Accurate
solar
irradiation
forecasting
is
essential
for
optimising
energy
use.
This
paper
presents
a
novel
approach:
the
'Clustering-based
CNN-BiLSTM-Attention
Hybrid
Architecture
with
PSO'.
It
combines
clustering,
attention
mechanisms,
Convolutional
Neural
Networks
(CNN),
Bidirectional
Long-Short
Term
Memory
(BiLSTM)
networks,
and
Particle
Swarm
Optimisation
(PSO)
into
unified
framework.
Clustering
categorises
days
groups,
improving
predictive
capabilities.
The
CNN-BiLSTM
model
captures
spatial
temporal
features,
identifying
complex
patterns.
PSO
optimises
hybrid
model's
hyperparameters,
while
an
mechanism
assigns
probability
weights
to
relevant
information,
enhancing
performance.
By
leveraging
patterns
in
data,
proposed
improves
accuracy
univariate
multivariate
analyses
multi-step
predictions.
Extensive
tests
on
real-world
datasets
from
various
locations
show
effectiveness.
For
example,
NASA
power
achieves
Mean
Absolute
Error
(MAE)
of
24.028
W/m2,
Root
Square
(RMSE)
43.025
R2
score
0.984
1-hour
ahead
forecasting.
results
significant
improvements
over
conventional
methods.
Sustainability,
Journal Year:
2025,
Volume and Issue:
17(7), P. 3239 - 3239
Published: April 5, 2025
Accurate
interval
forecasting
of
wind
power
is
crucial
for
ensuring
the
safe,
stable,
and
cost-effective
operation
grids.
In
this
paper,
we
propose
a
hybrid
deep
learning
model
day-ahead
forecasting.
The
begins
by
utilizing
Gaussian
mixture
(GMM)
to
cluster
daily
data
with
similar
distribution
patterns.
To
optimize
input
features,
feature
selection
(FS)
method
applied
remove
irrelevant
data.
empirical
wavelet
transform
(EWT)
then
employed
decompose
both
numerical
weather
prediction
(NWP)
into
frequency
components,
effectively
isolating
high-frequency
components
that
capture
inherent
randomness
volatility
A
convolutional
neural
network
(CNN)
used
extract
spatial
correlations
meteorological
while
bidirectional
gated
recurrent
unit
(BiGRU)
captures
temporal
dependencies
within
sequence.
further
enhance
accuracy,
multi-head
self-attention
mechanism
(MHSAM)
incorporated
assign
greater
weight
most
influential
elements.
This
leads
development
based
on
GMM-FS-EWT-CNN-BiGRU-MHSAM.
proposed
validated
through
comparison
benchmark
demonstrates
superior
performance.
Furthermore,
forecasts
generated
using
NPKDE
shows
new
achieves
higher
accuracy.
Sustainability,
Journal Year:
2024,
Volume and Issue:
16(19), P. 8497 - 8497
Published: Sept. 29, 2024
The
integration
of
photovoltaic
and
electric
vehicles
in
distribution
networks
is
rapidly
increasing
due
to
the
shortage
fossil
fuels
need
for
environmental
protection.
However,
randomness
disordered
charging
loads
cause
imbalances
power
flow
within
system.
These
complicate
voltage
management
economic
inefficiencies
dispatching.
This
study
proposes
an
innovative
strategy
utilizing
battery
energy
storage
system
cooperation
achieve
regulation
photovoltaic-connected
Firstly,
a
novel
pelican
optimization
algorithm-XGBoost
introduced
enhance
accuracy
prediction.
To
address
challenge
loads,
wide-local
area
scheduling
method
implemented
using
Monte
Carlo
simulations.
Additionally,
scheme
allocation
slack
are
proposed
optimize
both
available
capacity
efficiency
Finally,
we
recommend
day-ahead
real-time
control
regulate
voltage.
utilizes
multi-particle
swarm
algorithm
dispatching
between
on
side
user
during
stage.
At
stage,
superior
capabilities
prediction
errors
vehicle
reservation
defaults.
models
IEEE
33
that
incorporates
high-penetration
photovoltaics,
vehicles,
systems.
A
comparative
analysis
four
scenarios
revealed
significant
financial
benefits.
approach
ensures
devices
sides
effective
management.
it
encourages
trading
activities
these
market
establishes
foundation
sides.
