Journal of Sensor and Actuator Networks,
Journal Year:
2024,
Volume and Issue:
14(1), P. 3 - 3
Published: Dec. 27, 2024
The
reliance
on
fossil
fuels
as
a
primary
global
energy
source
has
significantly
impacted
the
environment,
contributing
to
pollution
and
climate
change.
A
shift
towards
renewable
sources,
particularly
solar
power,
is
underway,
though
these
sources
face
challenges
due
their
inherent
intermittency.
Battery
storage
systems
(BESS)
play
crucial
role
in
mitigating
this
intermittency,
ensuring
reliable
power
supply
when
generation
insufficient.
objective
of
paper
accurately
predict
irradiance
for
battery
operation
optimization
microgrids.
Using
satellite
data
from
weather
sensors,
we
trained
machine
learning
models
enhance
predictions.
We
evaluated
five
popular
algorithms
applied
ensemble
methods,
achieving
substantial
improvement
predictive
accuracy.
Our
model
outperforms
previous
works
using
same
dataset
been
validated
generalize
across
diverse
geographical
locations
Florida.
This
work
demonstrates
potential
AI-assisted
data-driven
approaches
support
sustainable
management
solar-powered
IoT-based
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: Oct. 18, 2024
This
study
introduces
an
advanced
mathematical
methodology
for
predicting
energy
generation
and
consumption
based
on
temperature
variations
in
regions
with
diverse
climatic
conditions
increasing
demands.
Using
a
comprehensive
dataset
of
monthly
production,
consumption,
readings
spanning
ten
years
(2010-2020),
we
applied
polynomial,
sinusoidal,
hybrid
modeling
techniques
to
capture
the
non-linear
cyclical
relationships
between
metrics.
The
model,
which
combines
sinusoidal
polynomial
functions,
achieved
accuracy
79.15%
estimating
using
as
predictor
variable.
model
effectively
captures
seasonal
patterns,
demonstrating
significant
improvement
over
conventional
models.
In
contrast,
while
yielding
partial
(R²
=
0.65),
highlights
need
more
fully
temperature-dependent
nature
production.
results
indicate
that
significantly
affect
higher
temperatures
driving
increased
demand
cooling,
lower
production
efficiency,
particularly
systems
like
hydropower.
These
findings
underscore
necessity
integrating
sophisticated
models
into
planning
ensure
resilience
amidst
climate
variability.
offers
critical
insights
policymakers
optimize
distribution
response
changing
conditions.
Machines,
Journal Year:
2024,
Volume and Issue:
12(11), P. 804 - 804
Published: Nov. 13, 2024
Solar
energy
can
mitigate
the
power
supply
shortage
in
remote
regions
for
portable
irrigation
systems.
The
accurate
prediction
of
solar
irradiance
is
crucial
determining
capacity
photovoltaic
generation
(PVPG)
systems
mobile
sprinkler
machines.
In
this
study,
a
method
proposed
to
estimate
typical
areas.
relation
between
meteorological
parameters
and
studied,
four
different
parameter
combinations
are
formed
considered
as
inputs
model.
Based
on
data
provided
by
ten
radiation
stations
uniformly
distributed
nationwide,
an
Extreme
Gradient
Boosting
(XGBoost)
model
optimized
using
Whale
Optimization
Algorithm
(WOA)
developed
predict
radiation.
accuracy
stability
then
evaluated
input
through
training
testing.
differences
performances
models
trained
based
single-station
mixed
from
multiple
also
compared.
obtained
results
show
that
achieves
highest
when
maximum
temperature,
minimum
sunshine
hours
ratio,
relative
humidity,
wind
speed,
extraterrestrial
used
parameters.
testing,
RMSE
MAE
WOA-XGBoost
2.142
MJ·m−2·d−1
1.531
MJ·m−2·d−1,
respectively,
while
those
XGBoost
2.298
1.598
MJ·m−2·d−1.
effectiveness
verified
measured
data.
has
higher
than
study
be
applied
forecast
regions.
By
inputting
specific
given
area,
effectively
produce
predictions
region.
This
provides
foundation
optimization
configuration
PVPG
