Power Prediction in Photovoltaic Systems with Neural Networks: A Multi-Parameter Approach
Applied Sciences,
Journal Year:
2025,
Volume and Issue:
15(7), P. 3615 - 3615
Published: March 26, 2025
In
this
study,
a
neural
network-based
power
prediction
for
photovoltaic
system
was
conducted
using
multi-parameter
approach,
considering
radiation,
temperature,
wind
speed,
humidity,
and
cloud
cover.
Photovoltaic
systems
are
highly
popular
renewable
energy
sources
due
to
their
robust,
modular,
environmentally
friendly
characteristics.
Although
offer
many
advantages,
dependency
on
irradiation
generation
sensitivity
meteorological
parameters
pose
significant
disadvantage,
leading
intermittent
production.
Since
these
affect
the
quality
of
generated
at
plant,
they
introduce
uncertainty
in
systems.
Therefore,
it
is
crucial
consider
factors
planning
management.
mitigate
contribute
by
predicting
production,
data
obtained
from
along
with
data,
were
used
Single
Layer
Perceptron
Neural
Network.
The
predicted
values
proposed
model
compared
actual
values,
results
comparison
presented.
Furthermore,
demonstrate
model’s
performance,
R
MSE
provided
as
0.98
0.03,
respectively,
indicating
strong
correlation
between
low
error.
Language: Английский
Applications of machine learning algorithms on the compressive strength of laterite blocks made with metakaolin-based geopolymer and sugarcane molasses
Waste Management Bulletin,
Journal Year:
2025,
Volume and Issue:
3(3), P. 100212 - 100212
Published: May 4, 2025
Language: Английский