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,
Journal Year:
2023,
Volume and Issue:
278, P. 127911 - 127911
Published: May 25, 2023
Heat
load
prediction
is
essential
for
energy
efficiency
and
carbon
reduction
in
district
heating
systems.
However,
heat
influenced
by
many
factors,
such
as
building
characteristics,
consumption
behavior,
climate,
making
its
challenging.
Traditional
methods
based
on
physical
models
are
complex
insufficiently
accurate,
whereas
most
data-driven
statistical
ignore
customer
behaviors
their
correlation,
do
not
account
the
temporal
inertia
of
consumption.
This
paper
proposes
a
graph
ambient
intelligence
(GAIN)
method
prediction,
which
classifies
customers
profiles
uses
collaborative
attention
graphs
to
capture
associations
weather
impact
loads.
GAIN
also
incorporates
recursive
autoregressive
model
The
proposed
evaluated
four
metrics
compared
with
fifteen
baseline
methods.
results
show
that
achieves
lowest
daily
forecasting
errors
terms
RMSE,
MAE,
CV-RMSE,
values
6.972,
4.442,
0.191,
respectively.
Besides,
maximum
25%,
29%,
25%
respectively,
other
when
taking
meteorological
factors
into
account.
Applied Energy,
Journal Year:
2024,
Volume and Issue:
372, P. 123801 - 123801
Published: July 3, 2024
Electricity
is
fundamental
to
the
development
of
national
economies
and
societies,
reliant
on
accurate
power
load
forecasting
for
its
stable
supply.
Ultra-short-term
analyzes
historical
data
predict
changes
within
next
hour.
This
crucial
achieving
efficient
dispatching,
improving
emergency
management,
ensuring
operation
system.
However,
with
increasingly
widespread
application
renewable
energy,
inherent
intermittency
exacerbates
complexity
randomness
loads,
posing
a
challenge
models
accurately
capture
features.
In
addressing
this
challenge,
study
presents
novel
method
feature
extraction
from
time
series
data,
aimed
at
enhancing
accuracy
forecasting.
By
analyzing
trend,
periodicities,
randomness,
it
simplifies
complex
into
several
features,
significantly
reducing
noise-induced
errors
identification
understanding
Moreover,
applies
five
prevalent
deep
learning
models.
Experimental
results
show
that
using
reduces
mean
absolute
percentage
error
by
an
average
54.6905%,
42.6654%,
51.3868%
datasets
three
different
substations
in
China.
These
not
only
affirm
method's
efficacy
but
also
provide
new
technical
foundations
reliable
functioning
future
systems.
