Agronomy,
Год журнала:
2024,
Номер
14(11), С. 2696 - 2696
Опубликована: Ноя. 15, 2024
Currently,
photovoltaic
(PV)
resources
have
been
widely
applied
in
the
agricultural
sector.
However,
due
to
unreasonable
configuration
of
multi-energy
collaboration,
issues
such
as
unstable
power
supply
and
high
investment
costs
still
persist.
Therefore,
this
study
proposes
a
solution
reasonably
determine
area
capacity
PV
panels
for
irrigation
machines,
addressing
fluctuations
generation
solar
sprinkler
systems
under
different
regional
meteorological
conditions.
The
aim
is
more
accurately
predict
(PVPG)
optimize
system,
ensuring
reliability
while
reducing
costs.
This
paper
first
establishes
PVPG
prediction
model
based
on
four
forecasting
models
conducts
comparative
analysis
identify
optimal
model.
Next,
annual,
seasonal,
term
scale
are
developed
further
studied
conjunction
with
model,
using
evaluation
metrics
assess
compare
models.
Finally,
mathematical
established
combination
solved
system
machines.
results
indicate
that
among
models,
SARIMAX
performs
best,
R2
index
reached
0.948,
which
was
19.4%
higher
than
others,
MAE
10%
lower
others.
exhibited
highest
accuracy
three
time
RMSE
4.8%
1.1%
After
optimizing
machine
scale,
it
verified
can
ensure
both
manage
energy
overflow
effectively.
Energies,
Год журнала:
2025,
Номер
18(2), С. 399 - 399
Опубликована: Янв. 17, 2025
To
address
the
challenges
of
issue
inaccurate
prediction
results
due
to
missing
data
in
PV
power
records,
a
photovoltaic
imputation
method
based
on
Wasserstein
Generative
Adversarial
Network
(WGAN)
and
Long
Short-Term
Memory
(LSTM)
network
is
proposed.
This
introduces
data-driven
GAN
framework
with
quasi-convex
characteristics
ensure
smoothness
imputed
existing
employs
gradient
penalty
mechanism
single-batch
multi-iteration
strategy
for
stable
training.
Finally,
through
frequency
domain
analysis,
t-Distributed
Stochastic
Neighbor
Embedding
(t-SNE)
metrics,
performance
validation
generated
data,
proposed
can
improve
continuity
reliability
tasks.
Energies,
Год журнала:
2025,
Номер
18(5), С. 1042 - 1042
Опубликована: Фев. 21, 2025
The
increasing
adoption
of
photovoltaic
(PV)
systems
has
introduced
challenges
for
grid
stability
due
to
the
intermittent
nature
PV
power
generation.
Accurate
forecasting
and
data
quality
are
critical
effective
integration
into
grids.
However,
records
often
contain
missing
system
downtime,
posing
difficulties
pattern
recognition
model
accuracy.
To
address
this,
we
propose
a
GAN-based
imputation
method
tailored
Unlike
traditional
GANs
used
in
image
generation,
our
ensures
smooth
transitions
with
existing
by
utilizing
data-guided
GAN
framework
quasi-convex
properties.
stabilize
training,
introduce
gradient
penalty
mechanism
single-batch
multi-iteration
strategy.
Our
contributions
include
analyzing
necessity
imputation,
designing
novel
conditional
network
validating
generated
using
frequency
domain
analysis,
t-NSE,
prediction
performance.
This
approach
significantly
enhances
continuity
reliability
tasks.
