Efficient reliability-based concurrent topology optimization method under PID-driven sequential decoupling framework
Thin-Walled Structures,
Год журнала:
2024,
Номер
203, С. 112117 - 112117
Опубликована: Июнь 29, 2024
Язык: Английский
Machine Learning Applications in Building Energy Systems: Review and Prospects
Buildings,
Год журнала:
2025,
Номер
15(4), С. 648 - 648
Опубликована: Фев. 19, 2025
Building
energy
systems
(BESs)
are
essential
for
modern
infrastructure
but
face
significant
challenges
in
equipment
diagnosis,
consumption
prediction,
and
operational
control.
The
complexity
of
BESs,
coupled
with
the
increasing
integration
renewable
sources,
presents
difficulties
fault
detection,
accurate
forecasting,
dynamic
system
optimisation.
Traditional
control
strategies
struggle
low
efficiency,
slow
response
times,
limited
adaptability,
making
it
difficult
to
ensure
reliable
operation
optimal
management.
To
address
these
issues,
researchers
have
increasingly
turned
machine
learning
(ML)
techniques,
which
offer
promising
solutions
improving
scheduling,
real-time
BESs.
This
review
provides
a
comprehensive
analysis
ML
techniques
applied
According
results
literature
review,
supervised
methods,
such
as
support
vector
machines
random
forest,
demonstrate
high
classification
accuracy
detection
require
extensive
labelled
datasets.
Unsupervised
approaches,
including
principal
component
clustering
algorithms,
robust
identification
capabilities
without
data
may
complex
nonlinear
patterns.
Deep
particularly
convolutional
neural
networks
long
short-term
memory
models,
exhibit
superior
forecasting
Reinforcement
further
enhances
management
by
dynamically
adjusting
parameters
maximise
efficiency
cost
savings.
Despite
advancements,
remain
terms
availability,
computational
costs,
model
interpretability.
Future
research
should
focus
on
hybrid
integrating
explainable
AI
enhancing
adaptability
evolving
demands.
also
highlights
transformative
potential
BESs
outlines
future
directions
sustainable
intelligent
building
Язык: Английский
Multi-Objective Optimization of Thin-Walled Composite Axisymmetric Structures Using Neural Surrogate Models and Genetic Algorithms
Materials,
Год журнала:
2023,
Номер
16(20), С. 6794 - 6794
Опубликована: Окт. 20, 2023
Composite
shells
find
diverse
applications
across
industries
due
to
their
high
strength-to-weight
ratio
and
tailored
properties.
Optimizing
parameters
such
as
matrix-reinforcement
orientation
of
the
reinforcement
is
crucial
for
achieving
desired
performance
metrics.
Stochastic
optimization,
specifically
genetic
algorithms,
offer
solutions,
yet
computational
intensity
hinders
widespread
use.
Surrogate
models,
employing
neural
networks,
emerge
efficient
alternatives
by
approximating
objective
functions
bypassing
costly
computations.
This
study
investigates
surrogate
models
in
multi-objective
optimization
composite
shells.
It
incorporates
deep
networks
approximate
relationships
between
input
key
metrics,
enabling
exploration
design
possibilities.
Incorporating
mode
shape
identification
enhances
accuracy,
especially
multi-criteria
optimization.
Employing
network
ensembles
strengthens
reliability
mitigating
model
weaknesses.
Efficiency
analysis
assesses
required
computations,
managing
trade-off
cost
accuracy.
Considering
complex
comparing
against
Monte
Carlo
approach
further
demonstrates
methodology’s
efficacy.
work
showcases
successful
integration
employed
identification,
enhancing
engineering
applications.
The
approach’s
efficiency
handling
intricate
designs
accuracy
has
broad
implications
methodologies.
Язык: Английский