South
Africa
is
in
the
throes
of
a
worse
electrical
energy
crisis.
The
reason
to
this
crisis
due
Africa's
state
owned
electricity
utility,
which
facing
relatively
close
hurricane
an
increasing
supply
shortage,
compounded
by
Eskom's
declining
generation
more
unsustainable
ageing
power
stations,
leading
spiking
bills.
These
factors
are
causing
havoc
on
economy.
With
country
recovering
from
COVID-induced
economic
downturn
during
last
Three
years.
harsh
truth
situation
disturbing,
with
roughly
weekly
outages.
key
questions
must
be
addressed:
What
has
been
done
remedy
situation?
how
can
government
speed
up
reform
sector?
From
back
rounds,
paper
aimed
at
investigating
role
Artificial
Intelligence
addressing
findings
demonstrate
that
intelligence
ability
enhance
efficiency,
reliability,
and
transparency.
Sensors,
Journal Year:
2025,
Volume and Issue:
25(1), P. 206 - 206
Published: Jan. 2, 2025
This
paper
presents
a
systematic
review
that
explores
the
latest
advancements
in
predictive
maintenance
methods
and
cybersecurity
for
solar
panel
systems,
shedding
light
on
advantages
challenges
of
most
recent
developments
techniques
plants.
Numerous
important
research
studies,
reviews,
empirical
studies
published
between
2018
2023
are
examined.
These
technologies
help
detecting
defects,
degradation,
anomalies
panels
by
facilitating
early
intervention
reducing
probability
inverter
failures.
The
analysis
also
emphasizes
how
challenging
it
is
to
adopt
renewable
energy
industry.
Achieving
balance
model
complexity
accuracy,
dealing
with
system
unpredictability,
adjusting
shifting
environmental
conditions
among
challenges.
It
highlights
Internet
Things
(IoT),
machine
learning
(ML),
deep
(DL),
which
all
incorporated
into
maintenance.
By
enabling
real-time
monitoring,
data
analysis,
anomaly
identification,
these
improve
accuracy
effectiveness
procedures.
Energies,
Journal Year:
2022,
Volume and Issue:
15(19), P. 7217 - 7217
Published: Oct. 1, 2022
After
the
massive
integration
of
distributed
energy
resources,
storage
systems
and
charging
stations
electric
vehicles,
it
has
become
very
difficult
to
implement
an
efficient
grid
management
system
regarding
unmanageable
behavior
power
flow
within
grid,
which
can
cause
many
critical
problems
in
different
stages,
typically
substations,
such
as
failures,
blackouts,
transformer
explosions.
However,
current
digital
transition
toward
Energy
4.0
Smart
Grids
allows
smart
solutions
substations
by
integrating
sensors
implementing
new
control
monitoring
techniques.
This
paper
is
proposing
a
hybrid
artificial
intelligence
multilayer
for
transformers,
diagnostic
algorithms,
Health
Index,
life-loss
estimation
approaches.
gathering
datasets,
this
presents
exhaustive
algorithm
comparative
study
select
best
fit
models.
developed
architecture
prognostic
(PHM)
health
interaction
between
evolutionary
support
vector
machine,
random
forest,
k-nearest
neighbor,
linear
regression-based
models
connected
online
transformer;
these
interactions
are
calculating
important
key
performance
indicators
related
alarms
that
gives
decisions
on
load
management,
factor
control,
maintenance
schedule
planning.
Applied Sciences,
Journal Year:
2025,
Volume and Issue:
15(2), P. 980 - 980
Published: Jan. 20, 2025
In
contemporary
business
environments,
manufacturing
companies
must
continuously
enhance
their
performance
to
ensure
competitiveness.
Material
feeding
systems
are
of
pivotal
importance
in
the
optimization
productivity,
with
attendant
improvements
quality,
reduction
costs,
and
minimization
delivery
times.
This
study
investigates
selection
material
methods,
including
Kanban,
line-storage,
call-out,
kitting
systems,
within
a
company.
The
research
employs
six
machine
learning
(ML)
algorithms—logistic
regression
(LR),
decision
trees
(DT),
random
forest
(RF),
support
vector
machines
(SVM),
K-nearest
neighbors
(K-NN),
artificial
neural
networks
(ANN)—to
develop
multi-class
classification
model
for
system
selection.
Utilizing
dataset
comprising
2221
materials
an
8-fold
cross-validation
technique,
ANN
exhibits
superior
across
all
evaluation
metrics.
Shapley
values
analysis
is
employed
elucidate
influence
input
parameters
process
systems.
provides
comprehensive
framework
selection,
integrating
advanced
ML
models
practical
insights.
makes
significant
contribution
field
by
enhancing
decision-making
processes,
optimizing
resource
utilization,
establishing
foundation
future
studies
on
adaptive
scalable
strategies
dynamic
industrial
environments.
Computers,
Journal Year:
2024,
Volume and Issue:
13(9), P. 235 - 235
Published: Sept. 17, 2024
This
paper
presents
a
comprehensive
and
comparative
study
of
solar
energy
forecasting
in
Morocco,
utilizing
four
machine
learning
algorithms:
Extreme
Gradient
Boosting
(XGBoost),
Machine
(GBM),
recurrent
neural
networks
(RNNs),
artificial
(ANNs).
The
is
conducted
using
smart
metering
device
designed
for
photovoltaic
system
at
an
industrial
site
Benguerir,
Morocco.
collects
usage
data
from
submeter
transmits
it
to
the
cloud
via
ESP-32
card,
enhancing
monitoring,
efficiency,
utilization.
Our
methodology
includes
analysis
resources,
considering
factors
such
as
location,
temperature,
irradiance
levels,
with
PVSYST
simulation
software
version
7.2,
employed
evaluate
performance
under
varying
conditions.
Additionally,
logger
developed
monitor
panel
production,
securely
storing
while
accurately
measuring
key
parameters
transmitting
them
reliable
communication
protocols.
An
intuitive
web
interface
also
created
visualization
analysis.
research
demonstrates
holistic
approach
devices
systems,
contributing
sustainable
utilization,
grid
development,
environmental
conservation
indicates
that
ANNs
are
most
effective
predictive
model
similar
scenarios,
demonstrating
lowest
RMSE
MAE
values,
along
highest
R2
value.
Power
quality
issues
can
arise
in
an
electrical
grid
due
to
various
factors,
and
one
of
the
most
common
is
harmonic
distortion.
Harmonics
are
essentially
sinusoidal
signals
at
frequencies
that
multiples
fundamental
frequency,
they
occur
when
nonlinear
loads
such
as
variable
speed
drives,
electronic
ballasts,
computer
power
supplies
connected
grid.
The
integration
large-scale
photovoltaic
(PV)
systems
into
has
led
increase
distortion,
which
affect
stability
reliability
In
this
paper,
we
use
ETAP
software
analyze
impact
PV
on
distortion
A
model
system
created
ETAP,
a
analysis
performed
determine
content
system.
results
show
generates
significant
levels
To
mitigate
harmonics
generated
by
system,
mitigation
techniques
analyzed.
Passive
filters
were
sized,
implemented
network,
tested
using
well
capacitor
banks
resonance
impact.
The
Artificial
Neural
Network
(ANN),
Extreme
Gradient
Boosting
(XGBOOST),
Support
Vector
Machine
(SVM),
and
Random
Forest
(RF)
machine
learning
(ML)
algorithms
are
used
in
this
research
to
give
a
comparative
study
of
solar
energy
production
forecasting
Morocco.
models
were
developed,
trained,
then
assessed.
In
paper,
two
metrics
employed
evaluate
the
models'
performance:
Mean
absolute
error
(MAE),
root
mean
squared
(RMSE).
performances
show
that
ANN
is
most
reliable
predictive
model
for
similar
scenarios.
This
paper
describes
an
IOT-based
energy
meter
for
photovoltaic
system
(PV),
with
forecasting
production
in
industrial
location
Morocco,
A
typical
PV
installed
Benguerir
city
monitors
values
the
proposed
device
based
on
ESP-32
card
that
receives
consumption
data
from
sub-meter
and
sends
to
cloud.
The
enhances
a
management.
method
includes
analysis
of
solar
resource
available
at
(Morocco),
as
well
analysis,
evaluation,
selection
components
station
using
simulation
software
such
PVSYST
tool,
addition,
development
datalogger
used
monitoring
by
panels,
storage
cloud
display
results
web
interface.
Finally,
use
ANN
algorithm
predict
future
production.
