Fractal and Fractional,
Год журнала:
2024,
Номер
8(7), С. 396 - 396
Опубликована: Июль 2, 2024
The
fractional-order
grey
prediction
model
is
widely
recognized
for
its
performance
in
time
series
tasks
with
small
sample
characteristics.
However,
parameter-estimation
method,
namely
the
least
squares
limits
predictive
of
and
requires
to
address
ill-conditioning
system.
To
these
issues,
this
paper
proposes
a
novel
parameter-acquisition
method
treating
structural
parameters
as
hyperparameters,
obtained
through
marine
predators
optimization
algorithm.
experimental
analysis
on
three
datasets
validate
effectiveness
proposed
paper.
Energy and Built Environment,
Год журнала:
2024,
Номер
unknown
Опубликована: Янв. 1, 2024
The
study
meticulously
reviews
international
growth
trends
in
renewable
energy
from
2010
to
2022,
across
various
global
regions.
Utilizing
a
comprehensive
methodology,
the
systematically
analyzes
academic
articles,
policy
documents,
and
industry
reports
offer
holistic
understanding
of
progression
distribution
practices.
It
scrutinizes
principal
drivers
propelling
adoption
resources
identifies
prevalent
challenges
that
impede
their
maximization.
critically
evaluates
existing
policies,
infrastructural
advancements,
technological
innovations,
assessing
effectiveness
diverse
socio-economic
landscapes.
delves
into
environmental
economic
impacts
transitioning
energy,
underlining
intricate
balance
between
sustainable
development
ecological
conservation.
role
as
pivotal
player
climate
change
mitigation
is
explored,
providing
balanced
perspective
its
potential
transform
systems
while
recognizing
complexities
widespread
adoption.
Additionally,
outlines
future
trajectories
for
growth,
offering
invaluable
insights
policymakers,
researchers,
investors.
underscores
necessity
evidence-based
decision-making
navigate
intricacies
capitalize
on
opportunities.
In
essence,
research
encourages
an
active
informed
approach,
guiding
community
towards
more
environmentally
responsible
future.
Sustainability,
Год журнала:
2024,
Номер
16(5), С. 1925 - 1925
Опубликована: Фев. 26, 2024
Buildings
remain
pivotal
in
global
energy
consumption,
necessitating
a
focused
approach
toward
enhancing
their
efficiency
to
alleviate
environmental
impacts.
Precise
prediction
stands
as
linchpin
optimizing
efficiency,
offering
indispensable
foresight
into
future
demands
critical
for
sustainable
environments.
However,
accurately
forecasting
consumption
individual
households
and
commercial
buildings
presents
multifaceted
challenges
due
diverse
patterns.
Leveraging
the
emerging
landscape
of
Internet
Things
(IoT)
smart
homes,
coupled
with
AI-driven
solutions,
promising
avenues
overcoming
these
challenges.
This
study
introduces
pioneering
that
harnesses
hybrid
deep
learning
model
prediction,
strategically
amalgamating
convolutional
neural
networks’
features
long
short-term
memory
(LSTM)
units.
The
granularity
IoT-enabled
meter
data,
enabling
precise
forecasts
both
residential
spaces.
In
comparative
analysis
against
established
models,
proposed
consistently
demonstrates
superior
performance,
notably
exceling
predicting
weekly
average
usage.
study’s
innovation
lies
its
novel
architecture,
showcasing
an
unprecedented
capability
forecast
holds
significant
promise
guiding
tailored
management
strategies,
thereby
fostering
optimized
practices
buildings.
demonstrated
superiority
underscores
potential
serve
cornerstone
driving
utilization,
invaluable
guidance
more
energy-efficient
future.
