Energies,
Journal Year:
2023,
Volume and Issue:
16(19), P. 6830 - 6830
Published: Sept. 26, 2023
This
paper
presents
the
concept
of
an
innovative
control
a
central
heating
system
in
multifamily
building
based
on
original
thermodynamic
model,
resulting
architecture
system,
and
originally
designed
manufactured
wireless
temperature
sensors
for
thermal
zones.
The
novelty
this
solution
is
developed
layers
system:
distributed
measurement
correction
analysis,
which
existing
infrastructure
local
HVAC
controller.
approach
allows
effective
use
measured
data
from
zones
finally
sending
value
calculated
settings
to
Moreover,
analytical
layer,
model
was
also
implemented
that
calculates
necessary
amount
energy
subsystem
located
building.
algorithmic
strategy
presented
extends
functionality
significantly
improves
efficiency
existing,
classic,
reference
algorithm
by
implementing
additional
loops.
Additionally,
it
enables
integration
with
demand-side
response
systems.
successfully
tested,
achieving
real
savings
12%.
These
results
are
described
case-study
format.
authors
believe
can
be
used
other
buildings
thus
will
have
positive
impact
maintain
comfort
reduce
CO2
emissions.
Energies,
Journal Year:
2023,
Volume and Issue:
16(20), P. 7124 - 7124
Published: Oct. 17, 2023
The
efficient
control
of
HVAC
devices
in
building
structures
is
mandatory
for
achieving
energy
savings
and
comfort.
To
balance
these
objectives
efficiently,
it
essential
to
incorporate
adequate
advanced
strategies
adapt
varying
environmental
conditions
occupant
preferences.
Model-free
approaches
systems
have
gained
significant
interest
due
their
flexibility
ability
complex,
dynamic
without
relying
on
explicit
mathematical
models.
current
review
presents
the
recent
advancements
control,
with
an
emphasis
reinforcement
learning,
artificial
neural
networks,
fuzzy
logic
hybrid
integration
other
model-free
algorithms.
main
focus
this
study
a
literature
most
notable
research
from
2015
2023,
highlighting
highly
cited
applications
contributions
field.
After
analyzing
concept
each
work
according
its
strategy,
detailed
evaluation
across
different
thematic
areas
conducted.
end,
prevalence
methodologies,
utilization
equipment,
diverse
testbed
features,
such
as
zoning
utilization,
are
further
discussed
considering
entire
body
identify
patterns
trends
field
control.
Last
but
not
least,
based
field,
provides
future
directions
aspects
areas.
International Communications in Heat and Mass Transfer,
Journal Year:
2022,
Volume and Issue:
140, P. 106538 - 106538
Published: Dec. 6, 2022
Desiccant
evaporative
cooling
systems
pave
the
path
towards
energy
and
environmental
sustainability
in
buildings
especially;
however,
direct
coolers
such
configurations
result
high
water
consumption.
The
application
of
modern
computational
intelligence
tools,
including
artificial
meta-heuristic
optimization
algorithms,
can
improve
operational
comprehension
desiccant
while
addressing
minimization
total
footprints
with
maximization
capacity.
contribution/objective
this
research
is
to
address
gaps
understanding
through
deep
learning,
genetic
algorithm,
multicriteria
decision
analysis
applied
a
system
working
under
real
transient
experimental
conditions
building
located
Austria.
Within
methodology,
calibrated,
experimental,
validated
data
monitoring
displaying
desiccant-enhanced
adapted
generate
set
input-output
sets.
includes
ambient
temperature,
humidity,
regeneration
supply
airflow
rate,
return
rate
yielding
capacity
system.
results
learning
algorithm
using
an
neural
network
have
suggested
that
architectures
5-[6]-[6]-1
5-[12]-[12]-1
are
best
accurately
predict
coefficient
determination
0.98856
0.99246,
respectively.
Secondly,
“white-box
model”
used
develop
digital
twin
model
which
helps
replication
earlier
conditions.
optimized
45.17
kg/h
3.32
tons
refrigeration.
These
optimal
values
found
combination
design
variables
temperature
28
°C,
relative
humidity
52.0%,
2.13
kg/s,
flow
2.35
70.0
°C.
It
concluded
data-driven
models
extend
interpretation
participate
its
performance
enhancement.
Energy Reports,
Journal Year:
2024,
Volume and Issue:
11, P. 5682 - 5702
Published: May 27, 2024
Advanced
control
strategies
for
heating,
ventilation,
and
air-conditioning
(HVAC)
systems
aim
to
enhance
both
buildings'
energy
efficiency
occupant
thermal
comfort.
Despite
their
potential,
real-world
demonstration
studies
on
such
advanced
technologies
are
still
lacking.
This
study
presents
a
field
of
real-time
predicted
mean
vote
(PMV)-based
HVAC
strategy
in
typical
residential
building
under
hot
dry
climate
conditions.
introduces
an
comfort-based
controller
(TCC)
the
PMV-based
control.
TCC
continually
assesses
indoor
outdoor
conditions
adjusts
setpoint
temperature
optimize
use
while
ensuring
satisfactory
Machine
learning
models
system
employed
estimate
radiant
(MRT)
point,
which
is
one
variables
used
calculate
PMV
values.
Three
machine
(i.e.,
linear
regression,
regression
trees,
artificial
neural
network)
adopted
this
with
non-stationary
input
values,
including
times,
pre-determined
setpoints,
temperatures.
The
developed
installed
full-scale
experimental
house
Kuwait,
conditions,
assess
house's
comfort
AC
performance.
Results
indicate
that
provides
better
performance
compared
non-TCC
case,
up
60%
improvement
PMV.
proposed
controlled
by
methods
demonstrates
savings
potential
over
20%
meeting
desired
levels
building.
These
findings
expected
be
valuable,
as
they
can
contribute
reducing
cooling
buildings
