Applied Sciences,
Journal Year:
2024,
Volume and Issue:
14(18), P. 8288 - 8288
Published: Sept. 14, 2024
The
rapid
development
of
modern
information
technology
(IT),
power
supply,
communication
and
traffic
systems
so
on
is
resulting
in
progress
the
area
distributed
energy-efficient
(if
possible,
powered
by
renewable
energy
sources)
smart
grid
components
securely
connected
to
entire
city
management
systems.
This
enables
a
wide
range
applications
such
as
management,
system
health
forecasting
cybersecurity
based
huge
volumes
data
that
automate
improve
performance
grid,
but
also
require
analysis,
inference
prediction
using
artificial
intelligence.
Data
strategies,
sharing
consumers,
institutions,
organisations
industries,
can
be
supported
edge
clouds,
thus
protecting
privacy
improving
performance.
article
presents
develops
authors’
own
concept
this
area,
which
planned
for
research
coming
years.
paper
aims
develop
initially
test
conceptual
framework
takes
into
account
aspects
discussed
above,
emphasising
practical
use
cases
Social
Internet
Things
(SIoT)
intelligence
(AI)
everyday
lives
sustainable
(SSC)
residents.
We
present
an
approach
consisting
seven
algorithms
integration
large
sets
machine
learning
processing
applied
optimisation
context
cities.
Journal of Machine and Computing,
Journal Year:
2024,
Volume and Issue:
unknown, P. 40 - 48
Published: Jan. 5, 2024
The
21st
century
witnesses
a
pivotal
global
shift
towards
Renewable
Energy
Sources
(RES)
to
combat
climate
change.
Nations
are
adopting
wind,
solar,
hydro,
and
other
sustainable
energy
forms.
However,
primary
concern
is
the
inconsistent
nature
of
these
sources.
Daily
fluctuations,
seasonal
changes,
weather
conditions
sometimes
make
renewables
like
sun
wind
unreliable.
key
managing
this
unpredictability
efficient
Storage
Systems
(ESS),
ensuring
saved
during
peak
periods
used
low
production
times.
existing
ESSs
not
flawless.
conversion
storage
inefficiencies
emerge
due
temperature
charge
rates,
voltage
fluctuations.
These
challenges
diminish
quality
stored
energy,
resulting
in
potential
waste.
There
unique
chance
address
using
vast
data
from
renewable
systems.
This
research
explores
Machine
Learning
(ML),
particularly
Neural
Networks
(NN),
improve
REES
efficiencies.
Analyzing
Palm
Springs
farms,
study
employs
an
Entropy-Based
Recursive
Feature
Elimination
(ERFE)
coupled
with
Feed-Forward
(FFNN).
ERFE
utilizes
entropy
prioritize
essential
features,
reducing
redundant
computational
demands.
tailored
FFNN
then
predicts
aiming
enhance
maximize
usability
generated
(RE).
Applied Sciences,
Journal Year:
2024,
Volume and Issue:
14(18), P. 8288 - 8288
Published: Sept. 14, 2024
The
rapid
development
of
modern
information
technology
(IT),
power
supply,
communication
and
traffic
systems
so
on
is
resulting
in
progress
the
area
distributed
energy-efficient
(if
possible,
powered
by
renewable
energy
sources)
smart
grid
components
securely
connected
to
entire
city
management
systems.
This
enables
a
wide
range
applications
such
as
management,
system
health
forecasting
cybersecurity
based
huge
volumes
data
that
automate
improve
performance
grid,
but
also
require
analysis,
inference
prediction
using
artificial
intelligence.
Data
strategies,
sharing
consumers,
institutions,
organisations
industries,
can
be
supported
edge
clouds,
thus
protecting
privacy
improving
performance.
article
presents
develops
authors’
own
concept
this
area,
which
planned
for
research
coming
years.
paper
aims
develop
initially
test
conceptual
framework
takes
into
account
aspects
discussed
above,
emphasising
practical
use
cases
Social
Internet
Things
(SIoT)
intelligence
(AI)
everyday
lives
sustainable
(SSC)
residents.
We
present
an
approach
consisting
seven
algorithms
integration
large
sets
machine
learning
processing
applied
optimisation
context
cities.
