Energies,
Год журнала:
2024,
Номер
17(2), С. 416 - 416
Опубликована: Янв. 15, 2024
The
use
of
renewable
energy
sources
is
becoming
increasingly
widespread
around
the
world
due
to
various
factors,
most
relevant
which
high
environmental
friendliness
these
types
resources.
However,
large-scale
involvement
green
leads
creation
distributed
networks
that
combine
several
different
generation
methods,
each
has
its
own
specific
features,
and
as
a
result,
data
collection
processing
necessary
optimize
operation
such
systems
become
more
relevant.
Development
new
technologies
for
optimal
RES
one
main
tasks
modern
research
in
field
energy,
where
an
important
place
assigned
based
on
artificial
intelligence,
allowing
researchers
significantly
increase
efficiency
all
within
systems.
This
paper
proposes
consider
methodology
application
approaches
assessment
amount
obtained
from
intelligence
technologies,
used
optimization
control
processes
operating
with
integration
sources.
relevance
work
lies
formation
general
approach
applied
evaluation
solar
wind
technologies.
As
verification
considered
by
authors,
number
models
predicting
power
using
photovoltaic
panels
have
been
implemented,
machine-learning
methods
used.
result
testing
quality
accuracy,
best
results
were
hybrid
forecasting
model,
combines
joint
random
forest
model
at
stage
normalization
input
data,
exponential
smoothing
LSTM
model.
Energy & Fuels,
Год журнала:
2024,
Номер
38(3), С. 1692 - 1712
Опубликована: Янв. 19, 2024
Modern
machine
learning
(ML)
techniques
are
making
inroads
in
every
aspect
of
renewable
energy
for
optimization
and
model
prediction.
The
effective
utilization
ML
the
development
scaling
up
systems
needs
a
high
degree
accountability.
However,
most
approaches
currently
use
termed
black
box
since
their
work
is
difficult
to
comprehend.
Explainable
artificial
intelligence
(XAI)
an
attractive
option
solve
issue
poor
interoperability
black-box
methods.
This
review
investigates
relationship
between
(RE)
XAI.
It
emphasizes
potential
advantages
XAI
improving
performance
efficacy
RE
systems.
realized
that
although
integration
with
has
enormous
alter
how
produced
consumed,
possible
hazards
barriers
remain
be
overcome,
particularly
concerning
transparency,
accountability,
fairness.
Thus,
extensive
research
required
address
societal
ethical
implications
using
create
standardized
data
sets
evaluation
metrics.
In
summary,
this
paper
shows
potential,
perspectives,
opportunities,
challenges
application
system
management
operation
aiming
target
efficient
energy-use
goals
more
sustainable
trustworthy
future.
Heliyon,
Год журнала:
2024,
Номер
10(3), С. e25407 - e25407
Опубликована: Фев. 1, 2024
Integration
of
photovoltaic
(PV)
systems,
desalination
technologies,
and
Artificial
Intelligence
(AI)
combined
with
Machine
Learning
(ML)
has
introduced
a
new
era
remarkable
research
innovation.
This
review
article
thoroughly
examines
the
recent
advancements
in
field,
focusing
on
interplay
between
PV
systems
water
within
framework
AI
ML
applications,
along
it
analyses
current
to
identify
significant
patterns,
obstacles,
prospects
this
interdisciplinary
field.
Furthermore,
incorporation
methods
improving
performance
systems.
includes
raising
their
efficiency,
implementing
predictive
maintenance
strategies,
enabling
real-time
monitoring.
It
also
explores
transformative
influence
intelligent
algorithms
techniques,
specifically
addressing
concerns
pertaining
energy
usage,
scalability,
environmental
sustainability.
provides
thorough
analysis
literature,
identifying
areas
where
is
lacking
suggesting
potential
future
avenues
for
investigation.
These
have
resulted
increased
decreased
expenses,
improved
sustainability
system.
By
utilizing
artificial
intelligence
freshwater
productivity
can
increase
by
10
%
efficiency.
offers
informative
perspectives
researchers,
engineers,
policymakers
involved
renewable
technology.
sheds
light
latest
desalination,
which
are
facilitated
ML.
The
aims
guide
towards
more
sustainable
technologically
advanced
future.
World Journal of Advanced Research and Reviews,
Год журнала:
2024,
Номер
21(1), С. 2487 - 2799
Опубликована: Янв. 29, 2024
The
integration
of
Artificial
Intelligence
(AI)
in
the
renewable
energy
sector
has
emerged
as
a
transformative
force,
enhancing
efficiency
and
sustainability
systems.
This
paper
provides
comprehensive
review
application
AI
two
critical
aspects
relation
to
predictive
maintenance
optimization.
Predictive
maintenance,
enabled
by
AI,
revolutionized
landscape
predicting
preventing
equipment
failures
before
they
occur.
Utilizing
machine
learning
algorithms,
analyzes
vast
amounts
data
from
sensors
historical
performance
identify
patterns
indicative
potential
faults.
proactive
approach
not
only
minimizes
downtime
but
also
extends
lifespan
infrastructure,
resulting
substantial
cost
savings
improved
reliability.
Furthermore,
plays
pivotal
role
optimizing
output
sources.
Through
advanced
analytics
real-time
monitoring,
algorithms
can
adapt
changing
environmental
conditions,
production
resource
allocation.
ensures
maximum
yield
sources,
making
them
more
competitive
with
traditional
delves
into
specific
techniques
such
deep
learning,
neural
networks,
employed
for
optimization
various
systems
like
solar,
wind,
hydropower.
Challenges
opportunities
associated
implementing
are
discussed,
including
security,
interoperability,
need
standardized
frameworks.
synthesis
technologies
addresses
operational
challenges
contributes
global
transition
towards
sustainable
clean
solutions.
serves
valuable
researchers,
practitioners,
policymakers
seeking
insights
evolving
applications
sector.
As
technology
continues
advance,
synergies
between
poised
shape
future
paradigm.
Engineering Science & Technology Journal,
Год журнала:
2024,
Номер
5(4), С. 1243 - 1256
Опубликована: Апрель 10, 2024
Artificial
intelligence
(AI)
is
revolutionizing
the
field
of
energy
efficiency
optimization
by
enabling
advanced
analysis
and
control
systems.
This
review
provides
a
concise
overview
role
AI
in
enhancing
efficiency.
technologies,
such
as
machine
learning
neural
networks,
are
being
increasingly
applied
to
optimize
consumption
various
sectors,
including
buildings,
transportation,
industrial
processes.
These
technologies
analyze
vast
amounts
data
identify
patterns
trends,
more
precise
systems
prediction
demand.
One
key
advantages
its
ability
adapt
learn
from
data,
leading
continuous
improvement
energy-saving
strategies.
algorithms
can
based
on
factors
weather
conditions,
occupancy
patterns,
prices,
resulting
significant
cost
savings
environmental
benefits.
Furthermore,
enables
integration
renewable
sources
into
existing
predicting
generation
optimizing
use.
helps
reduce
reliance
fossil
fuels
mitigates
greenhouse
gas
emissions,
contributing
sustainable
future.
However,
implementation
not
without
challenges.
include
privacy
concerns,
need
for
specialized
skills
develop
deploy
solutions,
complexity
integrating
infrastructure.
Addressing
these
challenges
will
be
crucial
realizing
full
potential
optimization.
In
conclusion,
holds
great
promise
intelligent
By
leveraging
organizations
achieve
savings,
costs,
contribute
resilient
future.
Keywords:
Role,
AI,
Energy,
Efficiency,
Optimization.
IEEE Access,
Год журнала:
2023,
Номер
11, С. 59558 - 59574
Опубликована: Янв. 1, 2023
In
smart
power
grids,
meters
(SMs)
are
deployed
at
the
end
side
of
customers
to
report
fine-grained
consumption
readings
periodically
utility
for
energy
management
and
load
monitoring.
However,
electricity
theft
cyber-attacks
can
be
launched
by
fraudulent
through
compromising
their
SMs
false
pay
less
usage.
These
attacks
harmfully
affect
sector
since
they
cause
substantial
financial
loss
degrade
grid
performance
because
used
management.
Supervised
machine
learning
approaches
have
been
in
literature
detect
attacks,
but
best
our
knowledge,
use
reinforcement
(RL)
has
not
investigated
yet.
RL
better
than
existing
it
adapt
more
efficiently
with
dynamic
nature
patterns
due
its
capability
learn
exploration
exploitation
mechanisms
deciding
optimal
actions.
this
article,
a
deep
(DRL)
approach
is
proposed
as
promising
solution
problem.
The
samples
real
dataset
employed
an
environment
rewards
given
based
on
detection
errors
made
during
training.
particular,
presented
four
different
scenarios.
First,
global
model
constructed
using
Q
network
(DQN)
double
(DDQN)
architectures
neural
networks.
Second,
detector
build
customized
new
achieve
high
accuracy
while
preventing
zero-day
attacks.
Third,
changing
pattern
taken
into
consideration
third
scenario.
Fourth,
challenges
defending
against
newly
addressed
fourth
Extensive
experiments
conducted,
results
demonstrate
that
DRL
boost
cyberattacks,
patterns,
changes
customers,
cyber-attacks.
