Energy Informatics,
Journal Year:
2022,
Volume and Issue:
5(S1)
Published: Sept. 7, 2022
Abstract
Accurate
day-ahead
load
forecasting
is
an
important
task
in
smart
energy
communities,
as
it
enables
improved
management
and
operation
of
flexibilities.
Smart
meter
data
from
individual
households
within
the
communities
can
be
used
to
improve
such
forecasts.
In
this
study,
we
introduce
a
novel
hybrid
bi-directional
LSTM-XGBoost
model
for
community
that
separately
forecasts
general
pattern
peak
loads,
which
are
later
combined
holistic
model.
The
outperforms
traditional
based
on
standard
profiles
well
LSTM-based
Furthermore,
show
accuracy
significantly
by
using
additional
input
features.
Sustainable Cities and Society,
Journal Year:
2022,
Volume and Issue:
85, P. 104059 - 104059
Published: July 19, 2022
Smart
cities
attempt
to
reach
net-zero
emissions
goals
by
reducing
wasted
energy
while
improving
grid
stability
and
meeting
service
demand.
This
is
possible
adopting
next-generation
systems,
which
leverage
artificial
intelligence,
the
Internet
of
things
(IoT),
communication
technologies
collect
analyze
big
data
in
real-time
effectively
run
city
services.
However,
training
machine
learning
algorithms
perform
various
energy-related
tasks
sustainable
smart
a
challenging
science
task.
These
might
not
as
expected,
take
much
time
training,
or
do
have
enough
input
generalize
well.
To
that
end,
transfer
(TL)
has
been
proposed
promising
solution
alleviate
these
issues.
best
authors'
knowledge,
this
paper
presents
first
review
applicability
TL
for
systems
well-defined
taxonomy
existing
frameworks.
Next,
an
in-depth
analysis
carried
out
identify
pros
cons
current
techniques
discuss
unsolved
Moving
on,
two
case
studies
illustrating
use
(i)
prediction
with
mobility
(ii)
load
forecasting
sports
facilities
are
presented.
Lastly,
ends
discussion
future
directions.
Advances in Applied Energy,
Journal Year:
2023,
Volume and Issue:
9, P. 100125 - 100125
Published: Feb. 1, 2023
Since
the
energy
sector
is
dominant
contributor
to
global
greenhouse
gas
emissions,
decarbonization
of
systems
crucial
for
climate
change
mitigation.
Two
major
challenges
are
renewable
transition
planning
and
sustainable
operations.
To
address
challenges,
incorporating
emerging
information
communication
technologies
can
facilitate
both
design
operations
future
smart
with
high
penetrations
decentralized
structures.
The
present
work
provides
a
comprehensive
overview
applicability
in
systems,
including
artificial
intelligence,
quantum
computing,
blockchain,
next-generation
technologies,
metaverse.
Relevant
research
directions
introduced
through
reviewing
existing
literature.
This
review
concludes
discussion
industrial
use
cases
demonstrations
technologies.
Energies,
Journal Year:
2023,
Volume and Issue:
16(3), P. 1404 - 1404
Published: Jan. 31, 2023
The
smart
grid
concept
is
introduced
to
accelerate
the
operational
efficiency
and
enhance
reliability
sustainability
of
power
supply
by
operating
in
self-control
mode
find
resolve
problems
developed
time.
In
grid,
use
digital
technology
facilitates
with
an
enhanced
data
transportation
facility
using
sensors
known
as
meters.
Using
these
meters,
various
functionalities
can
be
enhanced,
such
generation
scheduling,
real-time
pricing,
load
management,
quality
enhancement,
security
analysis
enhancement
system,
fault
prediction,
frequency
voltage
monitoring,
forecasting,
etc.
From
bulk
generated
a
architecture,
precise
predicted
before
time
support
energy
market.
This
supports
operation
maintain
balance
between
demand
generation,
thus
preventing
system
imbalance
outages.
study
presents
detailed
review
on
forecasting
category,
calculation
performance
indicators,
analyzing
process
for
conventional
meter
information,
used
conduct
task
its
challenges.
Next,
importance
meter-based
discussed
along
available
approaches.
Additionally,
merits
conducted
over
are
articulated
this
paper.
Applied Energy,
Journal Year:
2022,
Volume and Issue:
326, P. 119915 - 119915
Published: Sept. 15, 2022
With
high
levels
of
intermittent
power
generation
and
dynamic
demand
patterns,
accurate
forecasts
for
residential
loads
have
become
essential.
Smart
meters
can
play
an
important
role
when
making
these
as
they
provide
detailed
load
data.
However,
using
smart
meter
data
forecasting
is
challenging
due
to
privacy
requirements.
This
paper
investigates
how
requirements
be
addressed
through
a
combination
federated
learning
preserving
techniques
such
differential
secure
aggregation.
For
our
analysis,
we
employ
large
set
simulate
different
models
affect
performance
privacy.
Our
simulations
reveal
that
combining
both
accuracy
near-complete
Specifically,
find
combinations
enable
level
information
sharing
while
ensuring
the
processed
models.
Moreover,
identify
discuss
challenges
applying
learning,
aggregation
short-term
forecasting.
Journal of Information and Intelligence,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Jan. 1, 2024
The
pace
of
society
development
is
faster
than
ever
before,
and
the
smart
city
paradigm
has
also
emerged,
which
aims
to
enable
citizens
live
in
more
sustainable
cities
that
guarantee
well-being
a
comfortable
living
environment.
This
been
done
by
network
new
technologies
hosted
real
time
track
activities
provide
solutions
for
incoming
requests
or
problems
citizens.
One
most
often
used
methodologies
creating
Internet
Things
(IoT).
Therefore,
IoT-enabled
research
topic,
consists
many
different
domains
such
as
transportation,
healthcare,
agriculture,
recently
attracted
increasing
attention
community.
Further,
advances
artificial
intelligence
(AI)
significantly
contribute
growth
IoT.
In
this
paper,
we
first
present
concept,
background
components
IoT-based
city.
followed
up
literature
review
on
recent
developments
breakthroughs
empowered
AI
techniques
highlight
current
stage,
major
trends
unsolved
challenges
adopting
AI-driven
IoT
establishment
desirable
cities.
Finally,
summarize
paper
with
discussion
future
recommendations
direction
domain.
Energies,
Journal Year:
2022,
Volume and Issue:
15(14), P. 4993 - 4993
Published: July 8, 2022
Amongst
energy-related
CO2
emissions,
electricity
is
the
largest
single
contributor,
and
with
proliferation
of
electric
vehicles
other
developments,
energy
use
expected
to
increase.
Load
forecasting
essential
for
combating
these
issues
as
it
balances
demand
production
contributes
management.
Current
state-of-the-art
solutions
such
recurrent
neural
networks
(RNNs)
sequence-to-sequence
algorithms
(Seq2Seq)
are
highly
accurate,
but
most
studies
examine
them
on
a
data
stream.
On
hand,
in
natural
language
processing
(NLP),
transformer
architecture
has
become
dominant
technique,
outperforming
RNN
Seq2Seq
while
also
allowing
parallelization.
Consequently,
this
paper
proposes
transformer-based
load
by
modifying
NLP
workflow,
adding
N-space
transformation,
designing
novel
technique
handling
contextual
features.
Moreover,
contrast
studies,
we
evaluate
proposed
solution
different
streams
under
various
horizons
input
window
lengths
order
ensure
result
reproducibility.
Results
show
that
approach
successfully
handles
time
series
outperforms
models.
