Advances in computer and electrical engineering book series,
Год журнала:
2024,
Номер
unknown, С. 383 - 414
Опубликована: Окт. 23, 2024
This
chapter
explores
how
AI
and
smart
technologies
could
altogether
be
integrated
to
bring
revolutionary
change
in
the
fields
of
disaster
forecasting
management.
It
will
try
analyze
through
advanced
algorithms
IoT
sensors
these
can
potentially
advance
a
disaster-related
prediction
along
with
accuracy
timeliness.
Important
applications
real-time
data
collection,
predictive
modeling,
automated
alerts
collectively
enhance
response
strategies
as
well
resource
allocation.
chapter's
discussion
promise
merged
technologies—improved
predictiveness,
faster
times,
better
risk
assessment—perhaps
weighs
potential
liabilities
limitations
such
applications,
including
privacy
issues
infrastructures
sturdy
enough
host
system.
draws
on
case
studies
continuing
research
into
use
AI-driven
systems
disasters
present
insights
about
they
are
changing
practices
management
outline
future
directions
for
emerging
field.
Advances in chemical and materials engineering book series,
Год журнала:
2024,
Номер
unknown, С. 293 - 324
Опубликована: Сен. 13, 2024
This
chapter
explores
the
connection
between
circular
construction
principles
and
energy-efficient
design
strategies
to
examine
impact
on
environmental
stewardship
resource
optimization.
It
also
discusses
economic
viability,
regulatory
frameworks,
case
studies
of
design.
Sustainable
architecture
trends,
including
smart
technologies,
economy
principles,
public
policy,
are
explored.
The
interdisciplinary
collaboration
innovation
transforming
architectural
practices
illustrated
meet
current
needs
promote
societal
well-being.
A
comprehensive
sustainable
approach,
focusing
strategies,
is
empathized
with
tackle
issues
create
resilient
communities.
Advances in computer and electrical engineering book series,
Год журнала:
2025,
Номер
unknown, С. 119 - 144
Опубликована: Фев. 7, 2025
Body-focused
wireless
sensor
networks
have
surfaced
as
the
leading-edge
technology
in
healthcare,
wearable
electronics,
and
human-computer
interaction
–
a
critical
dimension
for
continuous
health
monitoring
remote
diagnostics.
BF-WSNs
face
challenges
advancing
due
to
nodes'
lack
of
battery
life,
which
is
crucial
long-term,
uninterrupted
operation.
This
chapter
explores
higher-order
energy
harvesting
management
strategies
within
with
an
emphasis
on
sustained
sources
like
thermal,
kinetic,
photovoltaic
energy.
The
discusses
low-power
circuit
design,
duty
cycling,
data
transmission
optimization,
energy-aware
protocols
BF-WSNs.
It
efficient
frameworks
extend
operational
lifetimes
reduce
dependency
highlights
harvesting's
potential
developing
self-sufficient
networks.
IGI Global eBooks,
Год журнала:
2025,
Номер
unknown, С. 167 - 184
Опубликована: Апрель 11, 2025
Abstract
The
exponential
rise
in
big
data
has
resulted
higher
energy
requirements
processing
frameworks,
which
present
a
major
environmental
and
practical
concern.
As
the
amount
of
being
generated
grows,
cost
effective
efficient
become
critical.
This
paper
reviews
different
techniques
that
improve
efficiency
from
hardware
level
optimization,
software
adaptation
optimization.
Proposed
implemented
low
power
processors
aware
storage;
scheduling;
compression;
reduction
strategies
such
as
edge
computing
have
been
found
to
be
management
processing.
Other
new
paths
include
artificial
intelligence
based
green
centers.
goal
this
survey
is
give
an
overview
existing
situation,
show
examples
implementation
energy-efficient
BD
point
out
possible
directions
for
their
further
development.
Advances in computational intelligence and robotics book series,
Год журнала:
2025,
Номер
unknown, С. 27 - 52
Опубликована: Фев. 28, 2025
This
chapter
delves
into
how
ML
and
QC
combine
in
the
development
of
theory
quantum
systems.
With
an
increase
system
complexity,
traditional
approaches
to
analysis
suffer
from
extremely
vast
computational
limitations.
Incorporation
algorithms
along
with
frameworks
computation
allows
for
novel
solutions
classification,
optimization,
noise
mitigation.
We
present
key
techniques;
both
supervised
unsupervised
learning,
their
synthesis
algorithms,
such
as
Quantum
Approximate
Optimization
Algorithm
(QAOA),
Variational
Eigensolver,
among
others.
The
latter
will
also
focus
on
application
real-world
activities
like
chemistry,
cryptography,
material
science,
synergy
increases
efficiency
better
accuracy.
work
gives
a
comprehensive
roadmap
harnessing
revolutionize
systems
solve
previously
intractable
problems
by
addressing
current
challenges
outlining
future
directions.
Advances in civil and industrial engineering book series,
Год журнала:
2025,
Номер
unknown, С. 67 - 92
Опубликована: Фев. 14, 2025
AI
integration
in
smart
grids
enhances
efficiency,
reliability,
and
sustainability
through
machine
learning
deep
techniques.
Smart
utilize
these
technologies
for
precise
demand
forecasting,
real-time
grid
optimization,
fault
detection.
advancements
enhance
energy
distribution
minimize
transmission
losses,
facilitate
renewable
predictive
analytics
adaptive
control
systems.
Advanced
AI-powered
models
enable
management
of
DER
dynamic
pricing
demand-response
management,
improving
the
robustness
grids.
Proactive
maintenance
cybersecurity
are
also
advanced
high-scale
data
anomalous
malicious
patterns.
This
chapter
discusses
AI/ML
applications
grids,
challenges
practice,
future
perspectives
like
edge
computing
decentralized
intelligence.
The
synergy
hence,
offers
transformative
opportunities
that
could
meet
surging
rising
demands
with
economic
viability.
Advances in civil and industrial engineering book series,
Год журнала:
2025,
Номер
unknown, С. 309 - 332
Опубликована: Фев. 14, 2025
Artificial
intelligence
is
transforming
the
energy
industry
as
it
improves
efficiency
of
power
generation,
enhances
consumption
patterns,
and
makes
possible
shift
towards
renewable
sources
energy.
It
under
aegis
climate
change
that
AI
will
prove
to
be
an
innovation
source
for
reducing
greenhouse
gas
emissions
well
managing
systems.
This
chapter
focuses
on
applications
in
forecasting,
smart
grid
management,
demand-side
optimization
while
considering
issue
carbon
footprint
reduction.
With
machine
learning
models
enhance
predictions
wind
solar
energies,
AI-based
grids
have
led
efficient
distribution
without
significant
losses.
Advanced
algorithms
are
also
capable
equipping
consumers
with
actionable
insights
into
sustainable
use
By
integrating
IoT
technology,
systems
can
much
more
adaptive
resilient.
Advances in civil and industrial engineering book series,
Год журнала:
2025,
Номер
unknown, С. 43 - 66
Опубликована: Фев. 14, 2025
Artificial
intelligence
is
transforming
the
energy
industry
as
it
improves
efficiency
of
power
generation,
enhances
consumption
patterns,
and
makes
possible
shift
towards
renewable
sources
energy.
It
under
aegis
climate
change
that
AI
will
prove
to
be
an
innovation
source
for
reducing
greenhouse
gas
emissions
well
managing
systems.
This
chapter
focuses
on
applications
in
forecasting,
smart
grid
management,
demand-side
optimization
while
considering
issue
carbon
footprint
reduction.
With
machine
learning
models
enhance
predictions
wind
solar
energies,
AI-based
grids
have
led
efficient
distribution
without
significant
losses.
Advanced
algorithms
are
also
capable
equipping
consumers
with
actionable
insights
into
sustainable
use
By
integrating
IoT
technology,
systems
can
much
more
adaptive
resilient.
Advances in civil and industrial engineering book series,
Год журнала:
2025,
Номер
unknown, С. 113 - 138
Опубликована: Фев. 14, 2025
Artificial
intelligence
integration
into
power
systems
has
been
the
revolution
that
transformed
how
energy
is
generated,
distributed,
and
consumed.
In
this
regard,
chapter
discusses
AI-driven
methodologies
for
system
design,
optimization,
operation
with
regards
to
their
potential
reduce
carbon
emissions.
Some
of
key
applications
in
regard
include
predictive
maintenance,
smart
grid
management,
demand
forecasting,
all
which
work
towards
improving
reliability
minimizing
waste
energy.
Advanced
AI
models,
including
machine
learning
deep
learning,
allow
real-time
decision-making,
optimization
renewable
integration,
dynamic
load
balancing.
They
support
installation
distributed
resources,
solar
wind,
promotes
shift
cleaner
systems.
The
advances
can
spur
transformative
reductions
greenhouse
gas
emissions
while
paving
way
resilient,
intelligent,
sustainable
by
addressing
challenges
such
as
stability
scalability.
Advances in civil and industrial engineering book series,
Год журнала:
2025,
Номер
unknown, С. 21 - 42
Опубликована: Фев. 14, 2025
This
chapter
deals
with
the
transformative
role
of
artificial
intelligence
in
power
generation,
conservation,
and
consumption
toward
a
sustainable
future.
In
an
era
where
energy
demands
across
globe
are
on
rise,
AI
presents
innovative
solutions
to
optimize
production,
improve
efficiency,
reduce
waste.
helps
enhance
predictive
maintenance
grid
management,
integrate
renewable
sources
more
effectively.
also
assists
conservation.
It's
possible
track
real
time
usage
identify
inefficiencies
even
recommend
adjustments
bring
under
control.
supports
demand
response
strategies,
reducing
peak
loads,
optimizes
behavior,
enabling
cost
savings
for
consumers
businesses
through
machine
learning
data
analytics.
explores
potential
transforming
systems,
focusing
environmentally
friendly
approaches
meet
global
needs.
Advances in computational intelligence and robotics book series,
Год журнала:
2025,
Номер
unknown, С. 287 - 306
Опубликована: Фев. 28, 2025
This
chapter
elaborates
on
how
machine
learning
is
changing
climatic
condition
prediction
and
analysis.
Conventional
techniques
for
modeling
simply
cannot
handle
the
extraordinary
complexity
non-linearity
inherent
in
climate
systems
quite
often.
As
such,
with
advanced
techniques,
such
as
deep
learning,
reinforcement
ensemble
methods,
masked
patterns
can
be
discovered,
accuracy
of
predictions
enhanced,
uncertainties
associated
data
handled.
Applications
temperature
forecasting,
extreme
weather
prediction,
long-term
trend
analysis
are
discussed.
It
also
discusses
integration
satellite
data,
IoT-enabled
sensors,
high-performance
computing
to
enhance
real-time
monitoring
forecasting
capabilities.
explores
potential
enhancing
science
by
enabling
proactive
decision-making,
addressing
scarcity,
interpretability,
ethical
considerations.
