Energies,
Год журнала:
2024,
Номер
17(18), С. 4641 - 4641
Опубликована: Сен. 17, 2024
In
order
to
reduce
the
contribution
of
building
sector
global
greenhouse
gas
emissions
and
climate
change,
it
is
important
improve
performance
through
retrofits
from
perspective
carbon
emission
reductions.
Data-driven
methods
are
now
widely
used
in
retrofit
research.
To
better
apply
data-driven
techniques
low-carbon
retrofits,
a
understanding
needed
connections
interactions
optimization
objectives
parameters,
as
well
tools.
This
paper
provides
bibliometric
analysis
selected
45
studies,
summarizes
current
research
hotspots
field,
discusses
gaps
be
filled,
proposes
potential
directions
for
future
work.
The
results
show
that
(1)
building-performance
(BPO)
process
established
physical
simulation
combines
site,
variables,
carbon-related
objectives,
generated
datasets
either
directly
processed
using
multi-objective
(MOO)
algorithms
or
trained
surrogate
model
iteratively
optimized
MOO
methods.
When
sufficient
amount
data
available,
can
develop
mathematical
models
use
(2)
benefits
maximized
by
holistically
taking
environmental,
economic,
social
factors
into
account;
perspectives
emissions,
costs,
thermal
comfort,
more,
adopted
strategies
include
improving
envelopes,
regulating
HVAC
systems,
utilizing
renewable
energy.
(3)
based
on
methods,
such
machine
learning,
automatic
iterative
calculations
screen
out
optimal
solutions
with
computer
assistance
high
efficiency
while
ensuring
accuracy.
(4)
Only
2.2%
6.7%
literature
focus
impacts
human
behavior
change
respectively.
future,
necessary
give
further
consideration
user
behaviors
long-term
process,
addition
accuracy
exploring
generalization
migration
capabilities
models.
Applied Sciences,
Год журнала:
2024,
Номер
14(13), С. 5501 - 5501
Опубликована: Июнь 25, 2024
Local
governments
face
critical
challenges
in
the
era
of
digital
transformation,
balancing
responsibility
safeguarding
resident
information
and
administrative
documents
while
maintaining
data
integrity
public
trust.
These
responsibilities
become
even
more
as
they
transition
into
smart
cities
adopting
advanced
technological
innovations
to
revolutionize
governance,
enhance
service
delivery,
foster
sustainable
resilient
urban
environments.
Technological
advancements
like
Internet-of-Things
devices
artificial
intelligence-driven
approaches
can
provide
better
services
residents,
but
also
expose
local
cyberthreats.
There
has
been,
nonetheless,
very
little
study
on
cybersecurity
issues
from
government
perspective,
multifaceted
nature
settings
is
scattered
fragmented,
highlighting
need
for
a
conceptual
understanding
adequate
action.
Against
this
backdrop,
aims
identify
key
components
governmental
context
through
systematic
literature
review.
This
review
further
extends
development
framework
providing
comprehensive
government’s
landscape.
makes
significant
contribution
academic
professional
domains
policies
within
context,
offering
valuable
insights
decision-makers,
practitioners,
academics.
helps
vulnerabilities,
enabling
stakeholders
recognize
shortcomings
their
implement
effective
countermeasures
safeguard
confidential
documents.
Thus,
findings
inform
policy
cybersecurity-aware
prepared.
Smart Cities,
Год журнала:
2024,
Номер
7(4), С. 1576 - 1625
Опубликована: Июнь 28, 2024
In
an
era
marked
by
rapid
technological
progress,
the
pivotal
role
of
Artificial
Intelligence
(AI)
is
increasingly
evident
across
various
sectors,
including
local
governments.
These
governmental
bodies
are
progressively
leveraging
AI
technologies
to
enhance
service
delivery
their
communities,
ranging
from
simple
task
automation
more
complex
engineering
endeavours.
As
governments
adopt
AI,
it
imperative
understand
functions,
implications,
and
consequences
these
advanced
technologies.
Despite
growing
importance
this
domain,
a
significant
gap
persists
within
scholarly
discourse.
This
study
aims
bridge
void
exploring
applications
context
government
provision.
Through
inquiry,
seeks
generate
best
practice
lessons
for
smart
city
initiatives.
By
conducting
comprehensive
review
grey
literature,
we
analysed
262
real-world
implementations
170
worldwide.
The
findings
underscore
several
key
points:
(a)
there
has
been
consistent
upward
trajectory
in
adoption
over
last
decade;
(b)
China,
US,
UK
at
forefront
adoption;
(c)
among
technologies,
natural
language
processing
robotic
process
emerge
as
most
prevalent
ones;
(d)
primarily
deploy
28
distinct
services;
(e)
information
management,
back-office
work,
transportation
traffic
management
leading
domains
terms
adoption.
enriches
existing
body
knowledge
providing
overview
current
sphere
governance.
It
offers
valuable
insights
policymakers
decision-makers
considering
adoption,
expansion,
or
refinement
urban
Additionally,
highlights
using
guide
successful
integration
optimisation
future
projects,
ensuring
they
meet
evolving
needs
communities.
Energies,
Год журнала:
2024,
Номер
17(17), С. 4501 - 4501
Опубликована: Сен. 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.
Journal of Geovisualization and Spatial Analysis,
Год журнала:
2024,
Номер
8(2)
Опубликована: Июнь 26, 2024
Abstract
Artificial
intelligence
(AI)
has
increasingly
been
integrated
into
various
domains,
significantly
impacting
geospatial
applications.
Machine
learning
(ML)
and
computer
vision
(CV)
are
critical
in
urban
decision-making.
However,
AI
implementation
faces
unique
challenges.
Academic
literature
on
responsible
largely
focuses
general
principles,
with
limited
emphasis
the
domain.
This
important
gap
scholarly
work
could
hinder
effective
integration
Our
study
employs
a
multi-method
approach,
including
systematic
academic
review,
word
frequency
analysis
insights
from
grey
literature,
to
examine
potential
challenges
propose
strategies
for
(GeoAI)
integration.
We
identify
range
of
practices
relevant
complexities
using
planning
its
implementation.
The
review
provides
comprehensive
actionable
framework
adoption
domain,
offering
roadmap
researchers
practitioners.
It
highlights
ways
optimise
benefits
while
minimising
negative
consequences,
contributing
sustainability
equity.
Energies,
Год журнала:
2024,
Номер
17(23), С. 5965 - 5965
Опубликована: Ноя. 27, 2024
With
accelerating
climate
change
and
rising
global
energy
consumption,
the
application
of
artificial
intelligence
(AI)
machine
learning
(ML)
has
emerged
as
a
crucial
tool
for
enhancing
efficiency
mitigating
impacts
change.
However,
their
implementation
dual
character:
on
one
hand,
AI
facilitates
sustainable
solutions,
including
optimization,
renewable
integration
carbon
reduction;
other
training
operation
large
language
models
(LLMs)
entail
significant
potentially
undermining
neutrality
efforts.
Key
findings
include
an
analysis
237
scientific
publications
from
2010
to
2024,
which
highlights
advancements
obstacles
adoption
across
sectors,
such
construction,
transportation,
industry,
households.
The
review
showed
that
interest
in
use
ML
grown
significantly:
over
60%
documents
have
been
published
last
two
years,
with
topics
construction
forecasting
attracting
most
interest.
Most
articles
are
by
researchers
China,
India,
UK
USA,
(28–33
articles).
This
is
more
than
twice
number
around
rest
world;
58%
research
concentrated
three
areas:
engineering,
computer
science
energy.
In
conclusion,
also
identifies
areas
further
aimed
at
minimizing
negative
maximizing
its
contribution
development,
development
energy-efficient
architectures
new
methods
management.
Sustainable Cities and Society,
Год журнала:
2024,
Номер
113, С. 105671 - 105671
Опубликована: Июль 15, 2024
Numerous
cities
undertakes
substantial
tree
planting
initiatives
for
heatwave
mitigation,
driven
by
model
predictions
indicating
a
positive
mitigation
impact.
However,
emerging
studies
suggest
that
the
transpiration
behavior
of
trees
during
heatwaves
significantly
deviates
from
normal.
This
divergence,
overlooked
in
current
climate
models,
introduces
possibility
inaccuracies
predicting
cooling
heatwaves.
In
this
research,
1)
The
universality
changed
heat
wave
is
revealed:
study
over
700
various
species
indicates
at
least
65
%
sampled
overestimated
conventional
waves.
2)
scheme
within
revised
to
represent
new
pattern.
Comparison
shows
overestimates
peak
hour
efficiency
60
%.
Consequently,
effectiveness
large-scale
tree-planting
as
strategy
may
not
meet
expectations,
emphasizing
need
refinement
Urban Climate,
Год журнала:
2024,
Номер
56, С. 102084 - 102084
Опубликована: Июль 1, 2024
Trees
are
crucial
elements
for
improving
urban
microclimates
by
providing
cooling
through
shading,
evapotranspiration,
and
windbreaks.
To
maximise
their
effects,
it
is
essential
to
strategically
position
the
trees
in
optimal
locations.
However,
research
on
optimising
tree
location
its
impact
limited
owing
computational
challenges
costs.
This
study
introduces
a
novel
method
that
employs
three
optimisation
algorithms—i.e.,
Non-dominated
Sorting
Genetic
Algorithm
II
(NSGA-II),
Particle
Swarm
Optimisation
(PSO),
Ant
Colony
(ACO)—to
identify
locations
environments
enhance
thermal
comfort.
The
methodology
involves
simulating
microclimate
responses
placements
optimised
each
algorithm
assessing
results
based
underscore
efficacy
of
locations,
demonstrating
can
significantly
reduce
Universal
Thermal
Comfort
Index
(UTCI)
areas.
Furthermore,
findings
suggest
clustering
canopies
has
compounding
these
benefits
Notably,
all
algorithms
improved
UTCI.
PSO
demonstrated
rapid
identification
effective
configurations.
ACO
provided
most
substantial
reduction
air
temperature,
highlighting
potential
as
an
tool
cooling.
While
efficient,
NSGA-II
plateaued
earlier,
suggesting
utility
scenarios
where
timely
solutions
crucial.
Buildings,
Год журнала:
2025,
Номер
15(2), С. 210 - 210
Опубликована: Янв. 12, 2025
Rapid
infrastructure
growth
in
developing
countries
has
intensified
environmental
challenges
due
to
cost-prioritizing
practices
over
sustainability.
This
study
evaluates
21
identified
sustainable-driving
tools
improve
the
management
of
throughout
its
life
cycle,
by
interacting
with
20
out
36
key
system
variables
(ISMVs).
Using
a
systems
thinking
approach,
Sustainable
Systems
Dynamic
Model
(SSDM)
is
developed,
comprising
nucleus
representing
interconnected
stages
cycle:
planning
and
design
(S1),
procurement
(S2),
construction
(S3),
operation
maintenance
(S4),
renewal
disposal
(S5).
The
model
incorporates
total
12
balance
(B)
25
reinforcement
(R)
loops,
enabling
visualization
critical
interdependencies
that
influence
sustainability
system.
In
addition,
analysis
shows
between
stages,
demonstrating,
for
example,
how
implementation
such
as
LCA,
BIM,
Circular
Economy
principles
S1,
or
IoT
SHM
S4,
significantly
A
gap
theory
practice
adoption
sustainable
identified,
which
aggravated
lack
knowledge
specific
countries’
context.
Hence,
this
contributes
closure
offering
facilitates
understanding
interactions
systems.
