Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: Dec. 28, 2024
The
growing
global
demand
for
water
and
energy
has
created
an
urgent
necessity
precise
forecasting
management
of
these
resources,
especially
in
urban
regions
where
population
growth
economic
development
are
intensifying
consumption.
Shenzhen,
a
rapidly
expanding
megacity
China,
exemplifies
this
trend,
with
its
requirements
anticipated
to
rise
further
the
upcoming
years.
This
research
proposes
innovative
Convolutional
Neural
Network
(CNN)
technique
consumption
considering
intricate
interactions
among
climate,
socio-economic,
demographic
elements.
proposed
approach
integrates
CNN
model
Enhanced
Gorilla
Troops
Optimization
(EGTO)
algorithm
demonstrate
superior
performance
compared
other
leading
methods
terms
accuracy
reliability.
results
show
strong
correlation
between
simulated
observed
data,
coefficient
0.87
0.91
consumption,
indicating
high
level
agreement
real-world
data.
Also,
it
is
indicated
that
new
can
accurately
forecast
achieving
mean
absolute
error
(MAE)
0.63
root
square
(RMSE)
0.58,
respectively.
indicates
suggested
promote
policymakers
stakeholders
making
well-informed
decisions
by
delivering
predictions
usage.
This,
turn,
facilitate
better
resource
distribution,
minimize
waste,
greater
sustainability.
study
emphasizes
incorporating
climate
change
socio-economic
factors
into
process
showcases
method's
potential
aid
decision-making
domain.
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: Nov. 9, 2024
The
increasing
pressure
on
resources
and
the
persistent
failure
to
address
global
malnutrition
are
evident
challenges.
A
significant
contributing
factor
is
decline
in
quality
of
production
resources,
particularly
water.
As
a
result,
many
countries
their
experts
have
prioritized
need
balance
resource
consumption.
To
research
gap
regarding
balanced
optimal
use,
various
methodologies
been
developed
over
time,
culminating
nexus
studies.
This
study
aimed
investigate
what,
why,
how
conducting
water-energy-food
(WEFN)
employed
sequential
mixed-methods
approach,
integrating
content
analysis
with
Analytical
Network
Process
(ANP).
findings
reveal
that
objectives
WEFN
studies
encompass
wide
range
interests,
which
can
be
systematically
categorized
into
seven
principal
domains:
system
sustainability
assessment,
integration
planning
decision-making
processes
related
consumption,
optimization
management
consumption
systems,
development
theoretical
frameworks
for
nexus,
evaluation
impacts
assessment
associated
risks.
Notably,
results
indicate
most
critical
reason
Furthermore,
identified
simulation
as
effective
technique
within
Hierarchy
(AHP)
framework.
In
context
ANP
technique,
statistical
emerged
important
methods.
advocates
using
diagram
facilitate
selection
method
study.
Energy Science & Engineering,
Journal Year:
2023,
Volume and Issue:
12(3), P. 755 - 770
Published: Dec. 27, 2023
Abstract
In
this
paper,
a
multigeneration
cycle
of
electricity,
cooling,
and
Bitcoin
whose
energy
source
is
geothermal,
has
been
subjected
to
energy,
exergy,
economic
analyses.
The
under
consideration
includes
the
steam
(upstream
cycle),
carbon
dioxide
(downstream
liquid–gas
line
absorb
heat
dissipated
by
cycle.
cycle,
condenser
acts
as
evaporator.
Part
electricity
generated
used
generate
Bitcoins.
Energy
exergy
efficiencies
at
baseline
(excluding
production)
are
45.8%
38.1%,
respectively.
if
more
power
spent
on
producing
product,
reduced.
Because
itself
not
valuable
in
terms
exergy.
Considering
average
price
during
years
2015–2022
100%
system
production,
payback
period
2018,
2021,
2022
when
equal
$13,412.4,
$21,398.8,
$47,743.0,
respectively,
less
than
baseline.
Therefore,
production
with
variety
renewable
energies
can
be
considered
solution.
Of
course,
it
should
noted
that
large
changes
affect
issue
benefit.
Frontiers in Environmental Science,
Journal Year:
2024,
Volume and Issue:
11
Published: Jan. 11, 2024
Introduction:
There
is
a
pressing
need
for
holistic
approach
to
optimize
water-energy-food
(WEF)
resources
management
and
address
their
interlinkages
with
other
due
population
growth,
socio-economic
development,
climate
change.
However,
the
structural
spatial
extent
of
WEF
system
boundaries
cause
exponential
growth
in
computational
complexity,
making
exploratory
data
analysis
crucial
obtain
insight
into
system’s
characteristics
focus
on
critical
components.
Methods:
This
study
conducts
multiscale
investigation
nexus
within
Canadian
prairie
provinces
(Alberta,
Saskatchewan,
Manitoba),
utilizing
causal-correlational
multispatial
Convergence
Cross
Mapping
(mCCM)
method.
Initially,
we
employed
regression
establish
equations,
along
coefficients
determination
(R
2
),
identify
patterns
among
pairs
sectors,
gross
domestic
product
(GDP),
greenhouse
gas
(GHG)
emissions.
Subsequently,
conducted
causal
between
correlated
using
mCCM
method
explore
cause-and-effect
relationships
sector
provinces;
both
individually
as
single
unit
over
period
1990-2020.
Results
discussion:
show
that
energy
water
are
most
influential
sectors
GHG
emissions
GDP
prairies
whole.
Energy
has
stronger
influence
compared
food
while
strongest
Alberta,
do
so
Saskatchewan
Manitoba,
respectively.
The
trade-offs
improving
security
strongly
depend
scale
under
investigation,
highlighting
careful
deliberations
around
boundary
judgment
decision-making.
provides
better
understanding
WEF-GDP-GHG
existing
interrelationships
aforementioned
helping
build
more
efficient
models
further
simulation
scenario
analysis.
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: Oct. 18, 2024
This
study
introduces
an
advanced
mathematical
methodology
for
predicting
energy
generation
and
consumption
based
on
temperature
variations
in
regions
with
diverse
climatic
conditions
increasing
demands.
Using
a
comprehensive
dataset
of
monthly
production,
consumption,
readings
spanning
ten
years
(2010-2020),
we
applied
polynomial,
sinusoidal,
hybrid
modeling
techniques
to
capture
the
non-linear
cyclical
relationships
between
metrics.
The
model,
which
combines
sinusoidal
polynomial
functions,
achieved
accuracy
79.15%
estimating
using
as
predictor
variable.
model
effectively
captures
seasonal
patterns,
demonstrating
significant
improvement
over
conventional
models.
In
contrast,
while
yielding
partial
(R²
=
0.65),
highlights
need
more
fully
temperature-dependent
nature
production.
results
indicate
that
significantly
affect
higher
temperatures
driving
increased
demand
cooling,
lower
production
efficiency,
particularly
systems
like
hydropower.
These
findings
underscore
necessity
integrating
sophisticated
models
into
planning
ensure
resilience
amidst
climate
variability.
offers
critical
insights
policymakers
optimize
distribution
response
changing
conditions.
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: Dec. 28, 2024
The
growing
global
demand
for
water
and
energy
has
created
an
urgent
necessity
precise
forecasting
management
of
these
resources,
especially
in
urban
regions
where
population
growth
economic
development
are
intensifying
consumption.
Shenzhen,
a
rapidly
expanding
megacity
China,
exemplifies
this
trend,
with
its
requirements
anticipated
to
rise
further
the
upcoming
years.
This
research
proposes
innovative
Convolutional
Neural
Network
(CNN)
technique
consumption
considering
intricate
interactions
among
climate,
socio-economic,
demographic
elements.
proposed
approach
integrates
CNN
model
Enhanced
Gorilla
Troops
Optimization
(EGTO)
algorithm
demonstrate
superior
performance
compared
other
leading
methods
terms
accuracy
reliability.
results
show
strong
correlation
between
simulated
observed
data,
coefficient
0.87
0.91
consumption,
indicating
high
level
agreement
real-world
data.
Also,
it
is
indicated
that
new
can
accurately
forecast
achieving
mean
absolute
error
(MAE)
0.63
root
square
(RMSE)
0.58,
respectively.
indicates
suggested
promote
policymakers
stakeholders
making
well-informed
decisions
by
delivering
predictions
usage.
This,
turn,
facilitate
better
resource
distribution,
minimize
waste,
greater
sustainability.
study
emphasizes
incorporating
climate
change
socio-economic
factors
into
process
showcases
method's
potential
aid
decision-making
domain.
