Computational Intelligence and Neuroscience,
Journal Year:
2022,
Volume and Issue:
2022, P. 1 - 12
Published: Feb. 18, 2022
Recently,
settlement
planning
and
replanning
process
are
becoming
the
main
problem
in
rapidly
growing
cities.
Unplanned
urban
settlements
quite
common,
especially
low-income
countries.
Building
extraction
on
satellite
images
poses
another
problem.
The
reason
for
is
that
manual
building
very
difficult
takes
a
lot
of
time.
Artificial
intelligence
technology,
which
has
increased
significantly
today,
potential
to
provide
high-resolution
images.
This
study
proposes
differentiation
buildings
by
image
segmentation
with
U-net
architecture.
open-source
Massachusetts
dataset
was
used
as
dataset.
includes
residential
city
Boston.
It
aimed
remove
high-density
In
architecture,
performed
different
encoders
results
compared.
line
work
done,
82.2%
IoU
accuracy
achieved
segmentation.
A
high
result
obtained
an
F1
score
0.9.
successful
90%
accuracy.
demonstrated
automatic
help
artificial
areas.
been
determined
mapping
can
be
antenna
achieved.
Buildings,
Journal Year:
2022,
Volume and Issue:
12(8), P. 1284 - 1284
Published: Aug. 21, 2022
Building
energy
usage
has
been
an
important
issue
in
recent
decades,
and
prediction
models
are
tools
for
analysing
this
problem.
This
study
provides
a
comprehensive
review
of
building
uncertainties
the
models.
First,
paper
introduces
three
types
methods:
white-box
models,
black-box
grey-box
The
principles,
strengths,
shortcomings,
applications
every
model
discussed
systematically.
Second,
analyses
terms
human,
building,
weather
factors.
Finally,
research
gaps
predicting
consumption
summarised
order
to
guide
optimisation
methods.
Advances in Building Energy Research,
Journal Year:
2022,
Volume and Issue:
17(2), P. 125 - 171
Published: Oct. 26, 2022
This
study
proposes
a
novel
Digital
Twin
framework
of
heating,
ventilation,
and
air
conditioning
(HVACDT)
system
to
reduce
energy
consumption
while
increasing
thermal
comfort.
The
is
developed
help
the
facility
managers
better
understand
building
operation
enhance
HVAC
function.
based
on
Building
Information
Modelling
(BIM)
combined
with
newly
created
plug-in
receive
real-time
sensor
data
as
well
comfort
optimization
process
through
Matlab
programming.
In
order
determine
if
suggested
practical,
were
collected
from
Norwegian
office
between
August
2019
October
2021
used
test
framework.
An
artificial
neural
network
(ANN)
in
Simulink
model
multiobjective
genetic
algorithm
(MOGA)
are
then
improve
system.
comprised
distributors,
cooling
units,
heating
pressure
regulators,
valves,
gates,
fans,
among
other
components.
this
context,
several
characteristics,
such
temperatures,
pressure,
airflow,
control,
factors
considered
decision
variables.
objective
functions,
predicted
percentage
dissatisfied
(PPD)
usage
both
calculated.
As
result,
ANN's
variables
function
correlated
well.
Furthermore,
MOGA
presents
different
design
that
can
be
obtain
best
possible
solution
terms
usage.
results
show
average
savings
for
four
days
summer
roughly
13.2%,
10.8%
three
months
(June,
July,
August),
keeping
PPD
under
10%.
Finally,
compared
traditional
approaches,
HVACDT
displays
higher
level
automation
management.
