Solar
radiation
is
an
essential
meteorological
parameter
for
building
energy
efficiency
analysis,
and
the
quality
of
data
directly
affects
analysis
results.
This
paper
investigates
estimation
hourly
solar
based
on
generation
typical
year(TMY)
using
various
real
parameters
limited
data.
The
focus
this
to
use
two
types
neural
network
algorithms
improve
accuracy
applicability,
solve
problem
acquisition
in
non-radiation
areas.
First,
select
city
station
three
methods
generate
TMY.
Then,
models,
BP
Neural
Network
(BP)，Convolutional
(CNN)
are
used
estimate
verify
Finally,
by
constructing
a
photovoltaic-integrated
office
model,
model
verified
consumption
simulation
photovoltaic
(PV)
power
simulation.
results
show
that
can
well
data,
which
provides
new
idea
study
areas
where
missing.
Journal of Cloud Computing Advances Systems and Applications,
Journal Year:
2024,
Volume and Issue:
13(1)
Published: March 22, 2024
Abstract
Solar
energy
has
emerged
as
a
key
industry
in
the
field
of
renewable
due
to
its
universality,
harmlessness,
and
sustainability.
Accurate
prediction
solar
radiation
is
crucial
for
optimizing
economic
benefits
photovoltaic
power
plants.
In
this
paper,
we
propose
novel
spatiotemporal
attention
mechanism
model
based
on
an
encoder-translator-decoder
architecture.
Our
built
upon
temporal
AttUNet
network
incorporates
auxiliary
branch
enhance
extraction
correlation
information
from
input
images.
And
utilize
powerful
ability
edge
intelligence
process
meteorological
data
parameters
real-time,
adjust
thereby
improving
real-time
performance
prediction.
The
dataset
utilized
study
sourced
total
surface
incident
(SSI)
product
provided
by
geostationary
satellite
FY4A.
After
experiments,
SSIM
been
improved
0.86.
Compared
with
other
existing
models,
our
obvious
advantages
great
prospects
short-term
radiation.
Energies,
Journal Year:
2024,
Volume and Issue:
17(24), P. 6222 - 6222
Published: Dec. 10, 2024
Solar
radiation
forecasting
is
the
basis
of
building
a
robust
solar
power
system.
Most
ground-based
methods
are
unable
to
consider
impact
cloud
changes
on
future
radiation.
To
alleviate
this
limitation,
study
develops
hybrid
network
which
relies
convolutional
neural
extract
motion
patterns
from
time
series
satellite
observations
and
long
short-term
memory
establish
relationship
between
information,
as
well
antecedent
measurements.
We
carefully
select
optimal
scales
spatial
temporal
correlations
design
test
experiments
at
ten
stations
check
model
performance
in
various
climate
zones.
The
results
demonstrate
that
accuracy
considerably
improved,
particularly
cloudy
conditions,
compared
with
purely
models.
maximum
magnitude
improvements
reaches
up
50
W/m2
(15%)
terms
(relative)
root
mean
squared
error
(RMSE)
for
1
h
ahead
forecasts.
achieves
superior
forecasts
correlation
coefficients
varying
0.96
0.85
6
ahead.
Forecast
errors
related
regimes,
amount
leads
relative
RMSE
difference
about
50%
an
additional
5%
variability.
This
ascertains
multi-source
data
fusion
contributes
better
simulation
impacts
combination
different
deep
learning
techniques
enables
more
reliable
In
addition,
multi-step
low
latency
make
advance
planning
management
energy
possible
practical
applications.
Solar
radiation
is
an
essential
meteorological
parameter
for
building
energy
efficiency
analysis,
and
the
quality
of
data
directly
affects
analysis
results.
This
paper
investigates
estimation
hourly
solar
based
on
generation
typical
year(TMY)
using
various
real
parameters
limited
data.
The
focus
this
to
use
two
types
neural
network
algorithms
improve
accuracy
applicability,
solve
problem
acquisition
in
non-radiation
areas.
First,
select
city
station
three
methods
generate
TMY.
Then,
models,
BP
Neural
Network
(BP)，Convolutional
(CNN)
are
used
estimate
verify
Finally,
by
constructing
a
photovoltaic-integrated
office
model,
model
verified
consumption
simulation
photovoltaic
(PV)
power
simulation.
results
show
that
can
well
data,
which
provides
new
idea
study
areas
where
missing.
