Deep learning for sensible cooling and heating loads associated with solar panels of residential buildings in arid climate
Smart and Sustainable Built Environment,
Journal Year:
2025,
Volume and Issue:
unknown
Published: March 11, 2025
Purpose
This
study
aims
to
develop
accurate
prediction
models
for
heating
and
cooling
demands
in
buildings
equipped
with
solar
panels.
By
integrating
renewable
energy
technologies,
the
goal
is
design
nearly
energy-neutral
that
significantly
reduce
consumption
enhance
overall
efficiency.
Design/methodology/approach
The
research
utilizes
deep
learning
address
variables
building
design,
an
area
previous
studies
have
not
fully
explored.
A
dataset
from
arid
climate
regions
was
used
train
test
two
predict
output.
evaluation
focused
on
how
well
predicted
needs,
as
amount
of
panels
would
need
generate
order
meet
these
demands.
approach
represents
advancement
over
methodologies
by
techniques
context
climates,
where
efficiency
a
critical
concern.
Findings
developed
this
were
highly
predicting
both
requirements
output
suggests
can
effectively
support
energy-efficient
buildings,
ensuring
provide
enough
cover
building’s
needs.
Originality/value
introduces
novel
method
panel
performance
characteristics
consumption,
moving
beyond
traditional
reliance
environmental
factors.
It
optimizes
management
systems
enhancing
accuracy
applicability.
use
optimization
ensures
precise
flexible
predictions,
providing
holistic
solution
design.
findings
useful
insights
architects
builders
looking
create
zero-energy
advancing
field
green
technologies.
Language: Английский
Analysis of Grid-Scale Photovoltaic Plants Incorporating Battery Storage with Daily Constant Setpoints
Energies,
Journal Year:
2024,
Volume and Issue:
17(23), P. 6117 - 6117
Published: Dec. 5, 2024
A
global
energy
transition
is
crucial
to
combat
climate
change,
involving
a
shift
from
fossil
fuels
renewable
sources
and
low-emission
technologies.
Solar
photovoltaic
technology
has
grown
exponentially
in
the
last
decade,
establishing
itself
as
cost-effective
sustainable
option
for
electricity
generation.
However,
its
large-scale
integration
faces
challenges
due
intermittency
lack
of
dispatchability.
This
study
evaluates,
an
perspective,
case
hybrid
(PV)
plants
with
battery
storage
systems.
It
addresses
aspect
little
explored
literature:
sizing
maintain
steady
constant
24
h
power
supply,
which
usually
avoided
high
cost.
Although
current
economic
feasibility
limited,
rapidly
falling
price
lithium
batteries
suggests
that
this
solution
could
be
viable
near
future.
Using
Matlab
simulations,
system’s
ability
deliver
production
assessed.
Energy
indicators
are
used
identify
optimal
system
size
under
different
scenarios
setpoints.
The
results
determine
supply
covers
all
or
large
part
daily
PV
generation,
achieving
reliable
production.
In
addition,
impact
using
setpoints
at
time
horizons
approach
potential
redefine
perception
solar
PV,
making
it
dispatchable
source,
improving
into
grid,
supporting
more
resilient
Language: Английский