Energies,
Journal Year:
2022,
Volume and Issue:
15(23), P. 9114 - 9114
Published: Dec. 1, 2022
This
paper
reviews
the
current
techniques
used
in
energy
management
systems
to
optimize
schedules
into
microgrids,
accounting
for
uncertainties
various
time
frames
(day-ahead
and
real-time
operations).
The
affecting
applications,
including
residential,
commercial,
virtual
power
plants,
electric
mobility,
multi-carrier
are
main
subjects
of
this
article.
We
outline
most
recent
modeling
approaches
describe
associated
with
microgrid
such
as
prediction
errors,
load
consumption,
degradation,
state
health.
discussed
article
probabilistic,
possibilistic,
information
gap
theory,
deterministic.
Then,
presents
compares
optimization
techniques,
considering
their
problem
formulations,
stochastic,
robust,
fuzzy
optimization,
model
predictive
control,
multiparametric
programming,
machine
learning
techniques.
depend
on
used,
data
available,
specific
application,
platform,
time.
hope
guide
researchers
identify
best
technique
scheduling,
uncertainty
application.
Finally,
challenging
issues
enhance
operations,
despite
by
new
trends,
discussed.
Energy and AI,
Journal Year:
2022,
Volume and Issue:
10, P. 100195 - 100195
Published: Aug. 5, 2022
The
vigorous
expansion
of
renewable
energy
as
a
substitute
for
fossil
is
the
predominant
route
action
to
achieve
worldwide
carbon
neutrality.
However,
clean
supplies
in
multi-energy
building
districts
are
still
at
preliminary
stages
paradigm
transitions.
In
particular,
technologies
and
methodologies
large-scale
integrations
not
sufficiently
sophisticated,
terms
intelligent
control
management.
Artificial
(AI)
techniques
powered
systems
can
learn
from
bio-inspired
lessons
provide
power
with
intelligence.
there
few
in-depth
dissections
deliberations
on
roles
AI
decarbonisation
systems.
This
study
summarizes
commonly
used
AI-related
approaches
discusses
their
functional
advantages
when
being
applied
various
sectors,
well
contribution
optimizing
operational
modalities
improving
overall
effectiveness.
also
presents
practical
applications
integration
systems,
analyzes
effectiveness
through
theoretical
explanations
diverse
case
studies.
addition,
this
introduces
limitations
challenges
associated
neutrality
transition
using
relevant
techniques,
proposes
further
promising
research
perspectives
recommendations.
comprehensive
review
ignites
advanced
provides
valuable
informational
instructions
guidelines
different
stakeholders
(e.g.,
engineers,
designers
scientists)
transition.
Energies,
Journal Year:
2023,
Volume and Issue:
16(3), P. 1434 - 1434
Published: Feb. 1, 2023
An
effective
energy
oversight
represents
a
major
concern
throughout
the
world,
and
problem
has
become
even
more
stringent
recently.
The
prediction
of
load
consumption
depends
on
various
factors
such
as
temperature,
plugged
load,
etc.
machine
learning
deep
(DL)
approaches
developed
in
last
decade
provide
very
high
level
accuracy
for
types
applications,
including
time-series
forecasting.
Accordingly,
number
models
this
task
is
continuously
growing.
current
study
does
not
only
overview
most
recent
relevant
DL
supply
demand,
but
it
also
emphasizes
fact
that
many
methods
use
parameter
tuning
enhancing
results.
To
fill
abovementioned
gap,
research
conducted
purpose
manuscript,
canonical
straightforward
long
short-term
memory
(LSTM)
model
electricity
tuned
multivariate
One
open
dataset
from
Europe
used
benchmark,
performance
LSTM
one-step-ahead
evaluated.
Reported
results
can
be
benchmark
hybrid
LSTM-optimization
forecasting
power
systems.
work
highlights
leads
to
better
when
using
metaheuristics
all
cases:
while
grid
search
achieves
coefficient
determination
(R2)
0.9136,
metaheuristic
led
worst
result
still
notably
with
corresponding
score
0.9515.
Energy and AI,
Journal Year:
2024,
Volume and Issue:
16, P. 100358 - 100358
Published: March 12, 2024
Electric
Load
Forecasting
(ELF)
is
the
central
instrument
for
planning
and
controlling
demand
response
programs,
electricity
trading,
consumption
optimization.
Due
to
increasing
automation
of
these
processes,
meaningful
transparent
forecasts
become
more
important.
Still,
at
same
time,
complexity
used
machine
learning
models
architectures
increases.
Because
there
an
interest
in
interpretable
explainable
load
forecasting
methods,
this
work
conducts
a
literature
review
present
already
applied
approaches
regarding
explainability
interpretability
using
Machine
Learning.
Based
on
extensive
research
covering
eight
publication
portals,
recurring
modeling
approaches,
trends,
techniques
are
identified
clustered
by
properties
achieve
forecasts.
The
results
show
increase
use
probabilistic
models,
methods
time
series
decomposition
fuzzy
logic
addition
classically
models.
Dominant
Feature
Importance
Attention
mechanisms.
discussion
shows
that
lot
knowledge
from
related
field
still
needs
be
adapted
problems
ELF.
Compared
other
applications
such
as
clustering,
currently
relatively
few
results,
but
with
trend.
