Environmental Technology & Innovation,
Journal Year:
2023,
Volume and Issue:
33, P. 103509 - 103509
Published: Dec. 29, 2023
Nowadays
the
pace
of
production
and
consumption
is
reaching
environmentally
unsustainable
levels.
In
this
regard,
great
technological
advances
developed
in
recent
years
are
postulated
as
a
source
opportunities
to
boost
circular
economy
sustainable
development.
This
wide
range
possibilities
offered
by
new
technologies
create
more
reality
has
aroused
curiosity
interest
academic
world,
especially
years.
The
main
objective
research
reveal
challenges
that
arise
when
incorporating
objectives
economy.
Regarding
methodology,
study
been
partially
supported
using
bibliometric
techniques.
results
highlight
transformative
role
technologies,
blockchain
artificial
intelligence,
advancing
economy,
with
particular
emphasis
on
community
technology
integration,
ethical
considerations,
synergies,
business
models,
burgeoning
bioeconomy.
We
conclude
promise
enhanced
resource
efficiency,
optimized
supply
chains,
innovative
improved
product
lifecycle
management,
offering
profound
economic
environmental
benefits
while
fostering
collaborative
innovation.
However,
these
also
represent
address,
such
integrating
advanced
methods,
ensuring
chain
transparency,
overcoming
skill
gap,
avoiding
data
centralization,
adapting
regulatory
frameworks
foster
equitable
growth.
These
some
most
important
areas
for
further
research,
those
related
development
employees'
capabilities
adaptation
frameworks,
they
understudied
gaps.
Sustainable Energy Research,
Journal Year:
2024,
Volume and Issue:
11(1)
Published: July 20, 2024
Abstract
The
global
energy
sector
is
currently
undergoing
a
transformative
shift
mainly
driven
by
the
ongoing
and
increasing
demand
for
clean,
sustainable,
reliable
solutions.
However,
integrating
renewable
sources
(RES),
such
as
wind,
solar,
hydropower,
introduces
major
challenges
due
to
intermittent
variable
nature
of
RES,
affecting
grid
stability
reliability.
Hybrid
storage
systems
(HESS),
which
combine
multiple
devices
(ESDs),
present
promising
solution
leveraging
complementary
strengths
each
technology
involved.
This
comprehensive
review
examines
recent
advancements
in
grid-connected
HESS,
focusing
on
their
components,
design
considerations,
control
strategies,
applications.
It
provides
detailed
analysis
technological
progress
various
ESDs
critical
role
power
conversion,
control,
management,
cooling
optimizing
HESS
performance.
Highlighting
case
studies
some
notable
successful
implementations
across
globe,
we
illustrate
practical
applications
identify
benefits
encountered.
By
addressing
these
challenges,
can
significantly
enhance
efficiency
reliability
supporting
towards
sustainable
resilient
infrastructure.
paper
concludes
identifying
future
research
directions,
highlighting
development
intelligent
systems,
materials,
efficient
recycling
processes
ensure
widespread
adoption
long-term
viability
HESS.
Neural Computing and Applications,
Journal Year:
2024,
Volume and Issue:
36(21), P. 12655 - 12699
Published: May 13, 2024
Abstract
Artificial
neural
networks
(ANN),
machine
learning
(ML),
deep
(DL),
and
ensemble
(EL)
are
four
outstanding
approaches
that
enable
algorithms
to
extract
information
from
data
make
predictions
or
decisions
autonomously
without
the
need
for
direct
instructions.
ANN,
ML,
DL,
EL
models
have
found
extensive
application
in
predicting
geotechnical
geoenvironmental
parameters.
This
research
aims
provide
a
comprehensive
assessment
of
applications
addressing
forecasting
within
field
related
engineering,
including
soil
mechanics,
foundation
rock
environmental
geotechnics,
transportation
geotechnics.
Previous
studies
not
collectively
examined
all
algorithms—ANN,
EL—and
explored
their
advantages
disadvantages
engineering.
categorize
address
this
gap
existing
literature
systematically.
An
dataset
relevant
was
gathered
Web
Science
subjected
an
analysis
based
on
approach,
primary
focus
objectives,
year
publication,
geographical
distribution,
results.
Additionally,
study
included
co-occurrence
keyword
covered
techniques,
systematic
reviews,
review
articles
data,
sourced
Scopus
database
through
Elsevier
Journal,
were
then
visualized
using
VOS
Viewer
further
examination.
The
results
demonstrated
ANN
is
widely
utilized
despite
proven
potential
methods
engineering
due
real-world
laboratory
civil
engineers
often
encounter.
However,
when
it
comes
behavior
scenarios,
techniques
outperform
three
other
methods.
discussed
here
assist
understanding
benefits
geo
area.
enables
practitioners
select
most
suitable
creating
certainty
resilient
ecosystem.
IEEE Access,
Journal Year:
2024,
Volume and Issue:
12, P. 50459 - 50488
Published: Jan. 1, 2024
Wireless
Sensor
Networks
(WSNs)
play
a
critical
role
in
numerous
applications,
and
the
accurate
localization
of
sensor
nodes
is
vital
for
their
effective
operation.
In
recent
years,
optimization
algorithms
have
garnered
significant
attention
as
means
to
enhance
WSN
node
localization.
This
paper
presents
an
in-depth
exploration
necessity
offers
comprehensive
review
used
this
purpose.
The
encompasses
diverse
range
techniques,
including
evolutionary
algorithms,
swarm
intelligence,
metaheuristic
approaches.
Key
factors,
such
accuracy,
scalability,
computational
complexity,
robustness,
are
systematically
evaluated
compared
across
various
algorithms.
Additionally,
sheds
light
on
strengths
limitations
each
approach
discusses
applicability
different
deployment
scenarios.
insights
provided
serve
valuable
resource
researchers
practitioners
seeking
optimize
localization,
thus
promoting
efficient
reliable
operation
WSNs
real-world
applications.
IEEE Access,
Journal Year:
2024,
Volume and Issue:
12, P. 111858 - 111881
Published: Jan. 1, 2024
Short-term
load
forecasting
(STLF)
is
vital
in
effectively
managing
the
reserve
requirement
modern
power
grids.
Subsequently,
it
supports
grid
operator
making
effective
and
economical
decisions
during
balancing
operation.
Therefore,
this
study
comprehensively
reviews
STLF
methods,
including
time
series
analysis,
regression-based
frameworks,
artificial
neural
networks
(ANNs),
hybrid
models
that
employ
different
approaches.
Detailed
mathematical
graphical
analyses
a
comparative
evaluation
of
these
methods
are
provided,
highlighting
their
advantages
disadvantages.
Further,
proposes
CNN-LSTM
model
comprised
Convolutional
(CNN)
for
feature
extraction
high
dimensional
data
Long
short-term
memory
(LSTM)
to
boost
model's
efficiency
temporal
sequence
prediction.
This
assessed
using
comprehensive
dataset
from
Pakistan's
NTDC
national
grid.
The
analysis
revealed
superior
performance
prediction,
achieving
enhanced
accuracy.
For
single-step
forecasting,
yielded
an
RMSE
538.71,
MAE
371.97,
MAPE
2.72.
In
24-hour
achieved
951.94,
656.35,
4.72
on
dataset.
Moreover,
has
outperformed
previous
comparison
AEP
dataset,
demonstrating
its
superiority
enhancing
management
supply
demand
electricity
networks.
