Building Services Engineering Research and Technology,
Год журнала:
2024,
Номер
45(6), С. 775 - 794
Опубликована: Авг. 18, 2024
The
regulation
strategy
of
a
district
heating
system
is
adjusted
based
on
accurate
heat
load
prediction,
which
not
only
effectively
reduces
energy
consumption
but
also
improves
efficiency
and
user
comfort.
In
order
to
improve
the
accuracy
forecasting,
forecasting
model
considering
two-dimensional
change
time
series
introduced
in
this
paper.
Firstly,
original
data
denoised
by
SVMD
decomposition,
several
stationary
regular
modal
components
are
obtained.
Then,
three
strategies
were
used
enhance
BWO
algorithm,
IBWO-TimesNet
prediction
was
established
extract
hidden
information
from
perspective.
Finally,
performance
evaluated
detail
through
case
analysis.
results
show
that
MAE,
RMSE
R2
SVMD-IBWO-TimesNet
0.647,
1.190
99.1%,
respectively.
Compared
with
other
mainstream
models,
has
higher
accuracy.
addition,
even
if
training
samples
reduced,
can
still
predict
strong
generalization
ability.
Therefore,
verified,
provides
reference
for
control
load.
Practical
application
Heat
vital
task
particularly
relation
its
impact
management
building
efficiency.
contribution
paper
provide
advanced
algorithms
This
insight
derived
modelling
will
assist
professionals
pursuit
more
needs
buildings,
thereby
optimizing
design
operation
systems.
practical
technology
could
save
costs,
reduce
carbon
emissions,
comfort
sustainability
buildings.
Alexandria Engineering Journal,
Год журнала:
2023,
Номер
86, С. 690 - 703
Опубликована: Дек. 28, 2023
Membrane
desalination
(MD)
is
an
efficient
process
for
desalinating
saltwater,
combining
the
uniqueness
of
both
thermal
and
separation
distillation
configurations.
In
this
context,
optimization
strategies
sizing
methodologies
are
developed
from
balance
system's
energy
demand.
Therefore,
robust
prediction
modeling
thermodynamic
behavior
freshwater
production
crucial
optimal
design
MD
systems.
This
study
presents
a
new
advanced
machine-learning
model
to
obtain
permeate
flux
tubular
direct
contact
membrane
unit.
The
was
established
by
optimizing
long-short-term
memory
(LSTM)
election-based
algorithm
(EBOA).
inputs
were
temperatures
feed
flow,
rate
salinity
flow.
optimized
compared
with
other
LSTM
models
sine–cosine
(SCA),
artificial
ecosystem
optimizer
(AEO),
grey
wolf
(GWO).
All
trained,
tested,
evaluated
using
different
accuracy
measures.
LSTM-EBOA
outperformed
in
predicting
based
on
had
highest
coefficient
determination
0.998
0.988
lowest
root
mean
square
error
1.272
4.180
training
test,
respectively.
It
can
be
recommended
that
paper
provide
useful
pathway
parameters
selection
performance
systems
makes
optimally
designed
rates
without
costly
experiments.
Abstract
The
short‐term
load
prediction
is
the
critical
operation
in
peak
demand
administration
and
power
generation
scheduling
of
buildings
that
integrated
smart
solar
microgrid
(SSM).
Many
research
studies
have
proved
hybrid
deep
learning
strategies
achieve
more
accuracy
feasibility
practical
applications
than
individual
algorithms.
Moreover,
many
SSM
on
rooftop
with
battery
management
system
(BMS)
to
enhance
energy
efficiency
management.
However,
traditional
methodologies
only
processed
weather
parameters
information
for
prediction,
ignoring
collected
data
from
BMS
by
advanced
metering
infrastructures
(AMI),
which
probably
improved
accuracy.
In
this
research,
accumulated
building
are
before
methodology
implementation.
Considering
diversities
BMS,
an
adaptive
convolution
neural
network
long
memory
(CNN‐LSTM)
proposed
hourly
electrical
prediction.
CNN
could
extract
large‐scale
input
feature,
while
LSTM
better
accurate
forecasts.
Pearson
correlation
matrix
calculated
feature
selection
scheme
different
units.
hyperparameter
tuning
utilized
obtaining
optimized
CNN‐LSTM
algorithm.
K‐fold
cross‐validation
employed
algorithm
verification,
includes
LSTM,
GRU,
CNN,
Bi‐LSTM
methodologies.
results
prove
achieved
outperformed
improvements,
20.57%,
29.63%,
19.06%
MSE,
MAE,
MAPE,
21.24%,
22.02%,
3.82%
validating
respectively.
combined
superior
predicting
accuracies,
proving
adaptability
ability
integrating
into
(EMS)
building's
SSM.
Sustainability,
Год журнала:
2024,
Номер
16(17), С. 7805 - 7805
Опубликована: Сен. 7, 2024
Building
energy
consumption
prediction
models
are
powerful
tools
for
optimizing
management.
Among
various
methods,
artificial
neural
networks
(ANNs)
have
become
increasingly
popular.
This
paper
reviews
studies
since
2015
on
using
ANNs
to
predict
building
use
and
demand,
focusing
the
characteristics
of
different
ANN
structures
their
applications
across
phases—design,
operation,
retrofitting.
It
also
provides
guidance
selecting
most
appropriate
each
phase.
Finally,
this
explores
future
developments
in
ANN-based
predictions,
including
improving
data
processing
techniques
greater
accuracy,
refining
parameterization
better
capture
features,
algorithms
faster
computation,
integrating
with
other
machine
learning
such
as
ensemble
hybrid
models,
enhance
predictive
performance.
Sustainability,
Год журнала:
2024,
Номер
16(6), С. 2522 - 2522
Опубликована: Март 19, 2024
Accurately
predicting
the
cold
load
of
industrial
buildings
is
a
crucial
step
in
establishing
an
energy
consumption
management
system
for
constructions,
which
plays
significant
role
advancing
sustainable
development.
However,
due
to
diverse
influencing
factors
and
complex
nonlinear
patterns
exhibited
by
data
buildings,
these
loads
poses
challenges.
This
study
proposes
hybrid
prediction
approach
combining
Improved
Snake
Optimization
Algorithm
(ISOA),
Variational
Mode
Decomposition
(VMD),
random
forest
(RF),
BiLSTM-attention.
Initially,
ISOA
optimizes
parameters
VMD
method,
obtaining
best
decomposition
results
data.
Subsequently,
RF
employed
predict
components
with
higher
frequencies,
while
BiLSTM-attention
utilized
lower
frequencies.
The
final
are
obtained
predictions.
proposed
method
validated
using
actual
from
building,
experimental
demonstrate
its
excellent
predictive
performance,
making
it
more
suitable
constructions
compared
traditional
methods.
By
enhancing
accuracy
not
only
improves
efficiency
but
also
promotes
reduction
carbon
emissions,
thus
contributing
development
sector.
