Machine learning-based energy management and power forecasting in grid-connected microgrids with multiple distributed energy sources
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Авг. 19, 2024
The
growing
integration
of
renewable
energy
sources
into
grid-connected
microgrids
has
created
new
challenges
in
power
generation
forecasting
and
management.
This
paper
explores
the
use
advanced
machine
learning
algorithms,
specifically
Support
Vector
Regression
(SVR),
to
enhance
efficiency
reliability
these
systems.
proposed
SVR
algorithm
leverages
comprehensive
historical
production
data,
detailed
weather
patterns,
dynamic
grid
conditions
accurately
forecast
generation.
Our
model
demonstrated
significantly
lower
error
metrics
compared
traditional
linear
regression
models,
achieving
a
Mean
Squared
Error
2.002
for
solar
PV
3.059
wind
forecasting.
Absolute
was
reduced
0.547
0.825
scenarios,
Root
(RMSE)
1.415
1.749
power,
showcasing
model's
superior
accuracy.
Enhanced
predictive
accuracy
directly
contributes
optimized
resource
allocation,
enabling
more
precise
control
schedules
reducing
reliance
on
external
sources.
application
our
resulted
an
8.4%
reduction
overall
operating
costs,
highlighting
its
effectiveness
improving
management
efficiency.
Furthermore,
system's
ability
predict
fluctuations
output
allowed
adaptive
real-time
management,
stress
enhancing
system
stability.
approach
led
10%
improvement
balance
between
supply
demand,
15%
peak
load
12%
increase
utilization
enhances
stability
by
better
balancing
mitigating
variability
intermittency
These
advancements
promote
sustainable
microgrid,
contributing
cleaner,
resilient,
efficient
infrastructure.
findings
this
research
provide
valuable
insights
development
intelligent
systems
capable
adapting
changing
conditions,
paving
way
future
innovations
Additionally,
work
underscores
potential
revolutionize
practices
providing
accurate,
reliable,
cost-effective
solutions
integrating
existing
infrastructures.
Язык: Английский
Hybrid Renewable Energy Systems—A Review of Optimization Approaches and Future Challenges
Applied Sciences,
Год журнала:
2025,
Номер
15(4), С. 1744 - 1744
Опубликована: Фев. 8, 2025
The
growing
need
for
sustainable
energy
solutions
has
propelled
the
development
of
Hybrid
Renewable
Energy
Systems
(HRESs),
which
integrate
diverse
renewable
sources
like
solar,
wind,
biomass,
geothermal,
hydropower
and
tidal.
This
review
paper
focuses
on
balancing
economic,
environmental,
social
technical
criteria
to
enhance
system
performance
resilience.
Using
comprehensive
methodologies,
examines
state-of-the-art
algorithms
such
as
Multi-Objective
Particle
Swarm
Optimization
(MOPSO)
Non-Dominated
Sorting
Genetic
Algorithm
II
(NSGA-II),
alongside
Crow
Search
(CSA),
Grey
Wolf
Optimizer
(GWO),
Levy
Flight-Salp
(LF-SSA),
Mixed-Integer
Linear
Programming
(MILP)
tools
HOMER
Pro
3.12–3.16
MATLAB
9.1–9.13,
have
been
instrumental
in
optimizing
HRESs.
Key
findings
highlight
role
advanced,
multi-energy
storage
technologies
stabilizing
HRESs
addressing
intermittency
sources.
Moreover,
integration
metaheuristic
with
machine
learning
enabled
dynamic
adaptability
predictive
optimization,
paving
way
real-time
management.
HRES
configurations
cost-effectiveness,
environmental
sustainability,
operational
reliability
while
also
emphasizing
transformative
potential
emerging
quantum
computing
are
underscored.
provides
critical
insights
into
evolving
landscape
offering
actionable
recommendations
future
research
practical
applications
achieving
global
sustainability
goals.
Язык: Английский
A Hybrid Demand-Side Policy for Balanced Economic Emission in Microgrid Systems
iScience,
Год журнала:
2025,
Номер
28(3), С. 112121 - 112121
Опубликована: Фев. 27, 2025
Язык: Английский
Improved TLBO Algorithm for Optimal Energy Management in a Hybrid Microgrid with Support Vector Machine-based Forecasting of Uncertain Parameters
Results in Engineering,
Год журнала:
2024,
Номер
unknown, С. 102992 - 102992
Опубликована: Сен. 1, 2024
Язык: Английский
Optimal day-ahead scheduling of microgrid equipped with electric vehicle and distributed energy resources: SFO-CSGNN approach
Journal of Energy Storage,
Год журнала:
2024,
Номер
102, С. 113933 - 113933
Опубликована: Окт. 5, 2024
Язык: Английский
Kubernetes and IoT-based next-generation scalable energy management framework for residential clusters
Journal of Building Engineering,
Год журнала:
2025,
Номер
unknown, С. 112292 - 112292
Опубликована: Март 1, 2025
Язык: Английский
An Efficient and Resilient Energy Management Strategy for Hybrid Microgrids Inspired by the Honey Badger's Behavior
Results in Engineering,
Год журнала:
2024,
Номер
unknown, С. 103161 - 103161
Опубликована: Окт. 1, 2024
Язык: Английский
Smart Grid Stability Prediction Using Adaptive Aquila Optimizer and Ensemble Stacked BiLSTM
Results in Engineering,
Год журнала:
2024,
Номер
unknown, С. 103261 - 103261
Опубликована: Окт. 1, 2024
Язык: Английский
Eco-Friendly Scheduling Model for Construction Projects Utilizing Genetic Algorithms
Sustainability,
Год журнала:
2024,
Номер
16(24), С. 11164 - 11164
Опубликована: Дек. 19, 2024
An
assessment
of
construction
activities
related
to
pollution
needs
be
conducted
during
the
planning
a
given
project.
Such
an
is
essential
ensure
that
resulting
does
not
surpass
environmental
threshold
limits.
This
research
provides
optimized
pollution-based
scheduling
model
in
projects
by
applying
Genetic
Algorithms
(GAs).
The
suggested
approach
figures
out
produced
gasses,
noise,
and
dust
for
each
activity
Then,
whole
project’s
duration
minimized
optimizing
project
schedule
using
GAs
while
keeping
different
pollutants
under
In
developed
model,
pollutant
handled
as
dummy
resource
incorporated
into
projects.
When
emitted
allowable
limits,
per
regulations,
re-schedule
tasks
so
levels
are
reduced
redistributed.
proposed
framework
presented
being
practically
applicable
through
actual
case
study.
results
show
GA
improves
leveling
process
more
efficiently
than
standard
technique
Microsoft
Project,
producing
fewer
histogram
moments
X
Y
axes
with
9.4%
2.2%,
respectively.
Sensitivity
analysis
reveals
best
solutions
this
study
obtained
when
population
size,
offspring
generation,
crossover
rate,
mutation
rate
equal
100,
50,
0.95,
0.05,
can
aid
reducing
projects’
impact
stages,
which
benefits
decision-makers
planners.
Язык: Английский