Impact of Artificial Intelligence on the Planning and Operation of Distributed Energy Systems in Smart Grids
Energies,
Journal Year:
2024,
Volume and Issue:
17(17), P. 4501 - 4501
Published: Sept. 8, 2024
This
review
paper
thoroughly
explores
the
impact
of
artificial
intelligence
on
planning
and
operation
distributed
energy
systems
in
smart
grids.
With
rapid
advancement
techniques
such
as
machine
learning,
optimization,
cognitive
computing,
new
opportunities
are
emerging
to
enhance
efficiency
reliability
electrical
From
demand
generation
prediction
flow
optimization
load
management,
is
playing
a
pivotal
role
transformation
infrastructure.
delves
deeply
into
latest
advancements
specific
applications
within
context
systems,
including
coordination
resources,
integration
intermittent
renewable
energies,
enhancement
response.
Furthermore,
it
discusses
technical,
economic,
regulatory
challenges
associated
with
implementation
intelligence-based
solutions,
well
ethical
considerations
related
automation
autonomous
decision-making
sector.
comprehensive
analysis
provides
detailed
insight
how
reshaping
grids
highlights
future
research
development
areas
that
crucial
for
achieving
more
efficient,
sustainable,
resilient
system.
Language: Английский
Guiding Urban Decision-Making: A Study on Recommender Systems in Smart Cities
Electronics,
Journal Year:
2024,
Volume and Issue:
13(11), P. 2151 - 2151
Published: May 31, 2024
In
recent
years,
the
research
community
has
increasingly
embraced
topics
related
to
smart
cities,
recognizing
their
potential
enhance
residents’
quality
of
life
and
create
sustainable,
efficient
urban
environments
through
integration
diverse
systems
services.
Concurrently,
recommender
have
demonstrated
continued
improvement
in
accuracy,
delivering
more
precise
recommendations
for
items
or
content
aiding
users
decision-making
processes.
This
paper
explores
utilization
context
cities
by
analyzing
a
dataset
comprised
papers
indexed
ISI
Web
Science
database.
Through
bibliometric
analysis,
key
themes,
trends,
prominent
authors
institutions,
preferred
journals,
collaboration
networks
among
were
extracted.
The
findings
revealed
an
average
annual
scientific
production
growth
25.85%.
Additionally,
n-gram
analysis
across
keywords,
abstracts,
titles,
keywords
plus,
along
with
review
selected
papers,
enriched
analysis.
insights
gained
from
these
efforts
offer
valuable
perspectives,
contribute
identifying
pertinent
issues,
provide
guidance
on
trends
this
evolving
field.
importance
lies
ability
living
providing
personalized
recommendations,
optimizing
resource
utilization,
improving
processes,
ultimately
contributing
sustainable
intelligent
environment.
Language: Английский
The application of collective intelligence in the construction industry: a review of the current state, challenges, and opportunities
Architectural Engineering and Design Management,
Journal Year:
2025,
Volume and Issue:
unknown, P. 1 - 26
Published: March 11, 2025
Language: Английский
Explainable hybrid forecasting model for a 4-node smart grid stability
Energy Reports,
Journal Year:
2025,
Volume and Issue:
13, P. 4948 - 4961
Published: April 24, 2025
Language: Английский
Security and Privacy in Networks and Multimedia
Electronics,
Journal Year:
2024,
Volume and Issue:
13(15), P. 2887 - 2887
Published: July 23, 2024
The
digital
era
has
significantly
transformed
the
dissemination
of
information
and
business
operations,
creating
an
intricate
web
interconnected
systems
[...]
Language: Английский
Radian Scaling and Its Application to Enhance Electricity Load Forecasting in Smart Cities Against Concept Drift
Smart Cities,
Journal Year:
2024,
Volume and Issue:
7(6), P. 3412 - 3436
Published: Nov. 8, 2024
In
a
real-world
implementation,
machine
learning
models
frequently
experience
concept
drift
when
forecasting
the
electricity
load.
This
is
due
to
seasonal
changes
influencing
scale,
mean,
and
median
values
found
in
input
data,
changing
their
distribution.
Several
methods
have
been
proposed
solve
this,
such
as
implementing
automated
model
retraining,
feature
engineering,
ensemble
learning.
The
biggest
drawback,
however,
that
they
are
too
complex
for
simple
implementation
existing
projects.
Since
drifted
data
follow
same
pattern
training
dataset
terms
of
having
different
values,
radian
scaling
was
new
way
scale
without
relying
on
these
values.
It
works
by
converting
difference
between
two
sequential
into
compute,
removing
bounding,
allowing
forecast
beyond
scale.
experiment,
not
only
does
constrained
gated
recurrent
unit
with
shorter
average
epochs,
but
it
also
lowers
root
mean
square
error
from
158.63
43.375,
outperforming
best
normalization
method
72.657%.
Language: Английский
Electricity Load Forecasting using Hybrid Datasets with Linear Interpolation and Synthetic Data
Karma P. Dorji,
No information about this author
Sorawut Jittanon,
No information about this author
Prapita Thanarak
No information about this author
et al.
Engineering Technology & Applied Science Research,
Journal Year:
2024,
Volume and Issue:
14(6), P. 17931 - 17938
Published: Dec. 2, 2024
Electricity
load
forecasting
is
an
important
aspect
of
power
system
management.
Improving
accuracy
ensures
reliable
electricity
supply,
grid
operations,
and
cost
savings.
Often,
collected
data
consist
Missing
Values
(MVs),
anomalies,
outliers,
or
other
inconsistencies
caused
by
failures,
metering
errors,
collection
hardware
network
unexpected
events.
This
study
uses
real-world
to
investigate
the
possibility
using
synthetically
generated
as
alternative
filling
in
MVs.
Three
datasets
were
created
from
original
one
based
on
different
imputation
methods.
The
methods
employed
linear
interpolation,
synthetic
data,
a
proposed
hybrid
method
interpolation
data.
performance
three
was
compared
deep
learning,
machine
statistical
models
verified
improvements.
findings
demonstrate
that
dataset
outperformed
models.
Language: Английский