Harnessing Renewable Energy with Machine Learning: A Comparative Study of Renewable Energy Approaches in the USA and Sub-Saharan Africa
Опубликована: Янв. 10, 2025
The
integration
of
machine
learning
(ML)
in
renewable
energy
systems
has
emerged
as
a
pivotal
strategy
for
enhancing
efficiency,
forecasting
demand,
and
improving
the
stability
power
grids.
This
study
presents
comparative
analysis
adoption
application
ML
between
United
States
sub-Saharan
Africa
(SSA).
made
significant
advancements
utilizing
technologies,
leveraging
them
optimizing
grid
operations,
consumption
forecasting,
waste
management.
Conversely,
Africa,
despite
its
vast
potential,
faces
substantial
barriers
such
inadequate
infrastructure,
limited
data
availability,
insufficient
technological
capacity,
hindering
widespread
energy.
Through
critical
review
existing
literature,
this
identifies
technological,
economic,
policy-related
challenges
that
both
regions
face
integrating
into
systems.
While
benefits
from
strong
infrastructure
investment
research
development,
SSA
is
still
early
stages
adopting
ML,
with
considerable
room
growth.
findings
suggest
while
USA
been
successful
applying
to
improve
efficiency
integrate
resources,
Africa’s
by
structural
constraints,
lack
skilled
personnel,
financial
challenges.
paper
offers
policy
recommendations
African
countries
foster
greater
energy,
including
investing
educational
cross-border
collaborations.
Additionally,
can
play
key
role
supporting
nations
through
technology
transfer,
joint
ventures,
strategic
investments
overcome
sector.
In
conclusion,
transformative
opportunity
regions.
Addressing
infrastructural
States,
will
be
crucial
achieving
sustainable
efficient
global
underscores
importance
international
cooperation
tailored
frameworks
advancing
applications
developed
developing
Язык: Английский
Big Data and Machine Learning for Hybrid Power System—Power Quality
Green energy and technology,
Год журнала:
2025,
Номер
unknown, С. 159 - 171
Опубликована: Янв. 1, 2025
Язык: Английский
Adaptive Control and Market Integration: Optimizing Distributed Power Resources for a Sustainable Grid
Energies,
Год журнала:
2025,
Номер
18(7), С. 1658 - 1658
Опубликована: Март 26, 2025
Distributed
power
resources
(DPRs)
offer
a
transformative
opportunity
to
improve
the
efficiency,
sustainability,
and
reliability
of
modern
infrastructures
through
their
integration.
This
work
presents
novel
method
based
on
mix
renewable
energy
sources,
storage
technologies,
conventional
generators
for
optimization
DPR
operations
under
dynamic
market
settings.
Maximizing
economic
gains
is
major
objective
while
preserving
system
resilience
stability.
To
handle
complexity
interactions,
we
strong,
hierarchical
control
architecture
encompassing
main,
secondary,
tertiary
levels.
System
performance
improved
using
advanced
strategies
together
with
real-time
market-responsive
changes
predictive
algorithms.
The
efficacy
proposed
methodology
validated
detailed
simulation
small
island
grid
mixed-integer
linear
programming
(MILP)
particle
swarm
(PSO),
which
demonstrates
significant
operational
improvements.
Results
indicate
cost
reductions
approximately
54.7%,
were
achieved
by
effectively
prioritizing
sources
optimizing
usage.
research
contributes
both
theoretically
practically
accelerating
transition
toward
sustainable,
resilient,
economically
viable
systems.
Язык: Английский