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.
Energy Reports,
Journal Year:
2024,
Volume and Issue:
11, P. 1774 - 1786
Published: Jan. 25, 2024
Wind-solar
hybrid
hydrogen
production
is
an
effective
technique
route,
by
converting
the
fluctuate
renewable
electricity
into
high-quality
hydrogen.
However,
intermittency
of
wind
and
solar
resources
also
exert
challenges
to
efficient
production.
In
order
address
this
issue,
paper
developed
a
day-ahead
scheduling
strategy
based
on
multi-state
transitions
alkaline
electrolyzer(AEL),
which
improves
system
flexibility
coordinating
operation
electrolyzer
with
battery.
Meanwhile,
K-means+
+
algorithm
applied
scenario
clustering,
then
proposed
capacity
configuration
method.
Based
adopted
case
study,
wind-solar
installed
designed
it
first
optimized,
power
fluctuation
mitigated
complementarity
index
considering
volatility
12.49%.
Moreover,
effectively
reduces
idle
standby
states
electrolyzer,
daily
average
energy
utilization
rate
12
typical
scenarios
reaching
92.83%.
addition,
exhibits
favorable
economic
potential,
internal
return
investment
payback
period
reach
6.81%
12.87
years,
respectively.
This
research
provides
valuable
insights
for
efficiently
producing
using
sources
promoting
their
synergistic
operation.
Results in Engineering,
Journal Year:
2024,
Volume and Issue:
21, P. 101747 - 101747
Published: Jan. 5, 2024
Integrating
uncertainties
associated
with
photovoltaic
(PV)
generation
is
an
important
aspect
used
to
ensure
the
planning
and
operation
of
power
distribution
systems.
Therefore,
this
research
proposed
uncertainty
model
for
PV
by
combining
methods
change
point
detection,
cyclic
k-means
clustering
(KMC),
Monte
Carlo
simulation
(MCS)
freedman
diaconis
estimator
(FDE),
KMC
soft-dynamic
time
warping
(DTW).
Firstly,
a
seasonal
split
was
performed
using
detection
techniques
identify
shifts
in
global
horizontal
irradiance
(GHI)
points.
Secondly,
GHI
generated
MCS
each
season
FDE
method
optimize
number
bins
data
distribution.
Finally,
curve
from
simplified
through
soft-DTW
metric,
which
facilitated
more
straightforward
representation
profile.
The
impact
profile
integration
on
quasi-dynamic
flow
tested
IEEE
33
Bus
system.
voltage
feeder
significantly
impacted
integration,
specifically
during
hours
when
high
produced.
For
instance,
at
11:00
a.m.,
values
buses
18,
17,
increased
0.933,
0.934,
0.935,
respectively,
0.982,
0.980,
0.972.
Similarly,
value
losses,
greater
produced
certain
hour,
smaller
losses
generated.
experimental
results
indicated
that
changes
electrical
parameters
over
according
input
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: Aug. 10, 2024
Solar
photovoltaic
(PV)
systems,
integral
for
sustainable
energy,
face
challenges
in
forecasting
due
to
the
unpredictable
nature
of
environmental
factors
influencing
energy
output.
This
study
explores
five
distinct
machine
learning
(ML)
models
which
are
built
and
compared
predict
production
based
on
four
independent
weather
variables:
wind
speed,
relative
humidity,
ambient
temperature,
solar
irradiation.
The
evaluated
include
multiple
linear
regression
(MLR),
decision
tree
(DTR),
random
forest
(RFR),
support
vector
(SVR),
multi-layer
perceptron
(MLP).
These
were
hyperparameter
tuned
using
chimp
optimization
algorithm
(ChOA)
a
performance
appraisal.
subsequently
validated
data
from
264
kWp
PV
system,
installed
at
Applied
Science
University
(ASU)
Amman,
Jordan.
Of
all
5
models,
MLP
shows
best
root
mean
square
error
(RMSE),
with
corresponding
value
0.503,
followed
by
absolute
(MAE)
0.397
coefficient
determination
(R
