Journal of Asian Energy Studies,
Journal Year:
2024,
Volume and Issue:
8, P. 51 - 67
Published: July 17, 2024
Enhancing
building
energy
efficiency
and
incorporating
renewable
technologies
are
crucial
for
combating
climate
change
promoting
sustainability.
This
study
employed
a
sensitivity
analysis
to
evaluate
the
influence
of
various
parameters
on
costs
consumption.
Standardized
Regression
Coefficients
(SRC)
were
used
measure
each
variable.
The
type
glass,
wall
construction,
window-to-wall
ratios,
shading
elements
identified
as
having
most
significant
impact
electricity
usage.
SRC
values
provided
numerical
representation
strength
direction
relationship
between
these
factors
output
variables.
Furthermore,
quantified
savings
achieved
through
optimization
methods.
data
indicated
an
average
reduction
in
consumption
22%,
with
variations
21%
23%
across
different
floors.
These
results
highlighted
effectiveness
optimizing
variables
applying
energy-efficient
design
principles.
findings
this
investigation
enhance
our
understanding
role
efficiency.
They
can
serve
reference
making
decisions
integrating
into
buildings
designing
them.
strategies
help
reduce
environmental
impact,
promote
sustainable
construction
practices,
achieve
savings.
Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Dec. 16, 2024
The
proposed
work
involves
the
use
of
a
carbon
black
coated
sensor
to
measure
flow
rate
in
circular
pipe
and
monitor
pressure
developed
therein
using
novel
system
with
change
electrical
resistance.
was
made
dip
coating
method
various
proportions
such
as
5
wt%,
10
15
20
wt%.
measurements
were
taken
working
model
data
acquisition
module.
When
liquid
flows
through
pipe,
it
causes
resistance
coating,
which
is
then
detected
by
electrodes
connected
sensor.
actual
water
determined
general
equation
also
noted
simultaneously.
A
mathematical
real
time
between
It
found
that
wt%
has
highest
sensitivity
3.06
compared
other
are
2.24,
1.71,
1.15
respectively.
further
used
calculate
predicted
regression
model.
obtained
R
2
value
0.98,
confirms
strong
correlation
calculated
rate.
Then
prediction
modeling
performed
neural
network,
points
trained
tested.
With
error
less
than
6%,
approach
provides
exponentially
cost-effective
monitoring
