Applied Energy,
Journal Year:
2024,
Volume and Issue:
372, P. 123773 - 123773
Published: June 26, 2024
This
paper
proposes
a
novel
method
namely
WaveletKernelNet-Convolutional
Block
Attention
Module-BiLSTM
for
intelligent
fault
diagnosis
of
drilling
pumps.
Initially,
the
random
forest
is
applied
to
determine
target
signals
that
can
reflect
characteristics
Accordingly,
Module
Net
constructed
noise
reduction
and
feature
extraction
based
on
signals.
The
Convolutional
embedded
in
WaveletKernelNet-CBAM
adjusts
weight
enhances
representation
channel
spatial
dimension.
Finally,
Bidirectional
Long-Short
Term
Memory
concept
introduced
enhance
ability
model
process
time
series
data.
Upon
constructing
network,
Bayesian
optimization
algorithm
utilized
ascertain
fine-tune
ideal
hyperparameters,
thereby
ensuring
network
reaches
its
optimal
performance
level.
With
hybrid
deep
learning
presented,
an
accurate
real
five-cylinder
pump
carried
out
results
confirmed
applicability
reliability.
Two
sets
comparative
experiments
validated
superiority
proposed
method.
Additionally,
generalizability
verified
through
domain
adaptation
experiments.
contributes
safe
production
oil
gas
sector
by
providing
robust
industrial
equipment.
Sustainable Cities and Society,
Journal Year:
2023,
Volume and Issue:
98, P. 104860 - 104860
Published: Aug. 15, 2023
Accurately
modelling
and
forecasting
electricity
consumption
remains
a
challenging
task
due
to
the
large
number
of
statistical
properties
that
characterize
this
time
series
such
as
seasonality,
trend,
sudden
changes,
slow
decay
autocrrelation
function,
among
many
others.
This
study
contributes
literature
by
using
comparing
four
advanced
econometrics
models,
machine
learning
deep
models1
analyze
forecast
during
COVID-19
pre-lockdown,
lockdown,
releasing-lockdown,
post-lockdown
phases.
Monthly
data
on
Qatar's
total
has
been
used
from
January
2010
December
2021.
The
empirical
findings
demonstrate
both
econometric
models
are
able
capture
most
important
features
characterizing
consumption.
In
particular,
it
is
found
climate
change
based
factors,
e.g
temperature,
rainfall,
mean
sea-level
pressure
wind
speed,
key
determinants
terms
forecasting,
results
indicate
autoregressive
fractionally
integrated
moving
average
three
state
markov
switching
with
exogenous
variables
outperform
all
other
models.
Policy
implications
energy-environmental
recommendations
proposed
discussed.
Buildings,
Journal Year:
2023,
Volume and Issue:
13(10), P. 2658 - 2658
Published: Oct. 22, 2023
The
building
sector
accounts
for
a
significant
proportion
of
global
energy
usage
and
carbon
dioxide
emissions.
It
is
important
to
explore
technological
advances
curtail
support
the
transition
sustainable
future.
This
study
provides
an
overview
emerging
technologies
strategies
that
can
assist
in
achieving
decarbonization.
main
reviewed
include
uncertainty-based
design,
renewable
integration
buildings,
thermal
storage,
heat
pump
technologies,
sharing,
retrofits,
demand
flexibility,
data-driven
modeling,
improved
control,
grid-buildings
integrated
control.
review
results
indicated
these
showed
great
potential
reducing
operating
costs
footprint.
synergy
among
area
should
be
explored.
An
appropriate
combination
help
achieve
grid-responsive
net-zero
which
anticipated
one
best
options
simultaneously
reduce
emissions,
consumption,
costs,
as
well
dynamic
supply
conditions
energy-powered
grids.
However,
unlock
full
collaborative
efforts
between
different
stakeholders
are
needed
facilitate
their
deployment
on
larger
wider
scale.
Case Studies in Thermal Engineering,
Journal Year:
2024,
Volume and Issue:
59, P. 104516 - 104516
Published: May 8, 2024
This
study
proposes
a
hybrid
prediction
model
using
sparrow
search
algorithm
(SSA)
to
optimize
the
convolutional
neural
network
(CNN)
and
support
vector
machine
(SVM),
in
order
perform
accurate
of
secondary
supply
temperature
(Ts2).
The
historical
operation
data
Weifang
residential
building
thermal
station
was
adopted
reasonable
preprocessing
performed
suppress
interference
abnormal
data.
input
variables
were
screened
correlation
analysis
method,
taking
influence
hysteresis
effect
into
consideration.
SSA-CNN-SVM
then
developed
for
prediction.
performance
evaluated
by
root
mean
square
error,
absolute
percentage
error
(MAPE),
value
relative
each
time
step.
results
obtained
demonstrated
that
has
high
accuracy.
MAPE
values
two
heat
exchange
stations
between
2.28%
2.4%.
indoor
significantly
affected
accuracy
Ts2.
After
introduction
temperature,
predicted
reduced
0.35%.
maximum
reduction
1.5%
compared
with
other
models.
Energy,
Journal Year:
2024,
Volume and Issue:
307, P. 132636 - 132636
Published: July 29, 2024
Due
to
the
recent
advancements
in
Internet
of
Things
and
data
science
techniques,
a
wide
range
studies
have
investigated
use
mining
(DM)
machine
learning
(ML)
algorithms
enhance
building
energy
management
(BEM).
However,
different
classes
DM
ML
feature
mechanisms
capabilities,
resulting
their
distinct
roles
performance
BEM.
Appropriate
integration
categories
BEM
is
essential
promote
application
provide
guidance
for
new
topic
areas.
This
study
presents
literature
review
techniques
key
areas
BEM,
including
evaluation,
usage
prediction,
demand
flexibility
optimization.
The
categorizes
into
three
main
categories,
supervised
DM,
unsupervised
reinforcement
(RL).
Unsupervised
are
primarily
used
assessment,
while
mainly
employed
benchmarking
prediction.
RL
has
been
utilized
optimal
control
improve
efficiency,
flexibility,
indoor
thermal
comfort.
strengths,
shortcomings,
these
methods
terms
applications
discussed,
along
with
some
suggestions
future
research
this
field.
