Global wind energy resources decline under climate change
Energy,
Год журнала:
2023,
Номер
288, С. 129765 - 129765
Опубликована: Ноя. 28, 2023
Wind
energy
is
poised
to
play
a
major
role
in
the
transition.
The
objective
of
this
work
investigate
effects
climate
change
on
global
wind
resources.
For
purpose,
multi-model
ensemble
constructed
with
selected
Global
Climate
Models.
considered
through
most
recent
scenarios,
Shared
Socioeconomic
Pathways.
We
find
significant
decline
resources
by
2100
relative
current
levels.
particularly
evident
mid-latitudes
Northern
Hemisphere
–
heavily
populated
regions
where
it
matters
especially,
given
need
for
renewable
production
increase
substantially
decarbonise
supply.
Exceptions
do
exist,
but
tropical
and
polar
regions,
far
less
populated.
Depending
climate-change
scenario
region,
changes
may
exceed
30
%
average
power
density
values.
Additionally,
we
uncover
variability
regardless
scenario,
which
be
expected
affect
its
integration
into
electricity
networks.
Recognising
these
important
planning
transition
and,
more
specifically,
contribution
energy.
Язык: Английский
A new perspective of wind speed forecasting: Multi-objective and model selection-based ensemble interval-valued wind speed forecasting system
Energy Conversion and Management,
Год журнала:
2023,
Номер
299, С. 117868 - 117868
Опубликована: Ноя. 16, 2023
Язык: Английский
Future global offshore wind energy under climate change and advanced wind turbine technology
Energy Conversion and Management,
Год журнала:
2024,
Номер
321, С. 119075 - 119075
Опубликована: Сен. 21, 2024
Язык: Английский
Machine learning and statistical approaches for wind speed estimation at partially sampled and unsampled locations; review and open questions
Energy Conversion and Management,
Год журнала:
2025,
Номер
327, С. 119555 - 119555
Опубликована: Янв. 27, 2025
Язык: Английский
Introducing new morphometric parameters to improve urban canopy air flow modeling: A CFD to machine-learning study in real urban environments
Urban Climate,
Год журнала:
2024,
Номер
58, С. 102173 - 102173
Опубликована: Окт. 28, 2024
Язык: Английский
LSTM and Transformer-based framework for bias correction of ERA5 hourly wind speeds
Energy,
Год журнала:
2025,
Номер
unknown, С. 136498 - 136498
Опубликована: Май 1, 2025
Язык: Английский
Assessing the Performance of Regional Climate Model Wind Speeds Over Canada
International Journal of Climatology,
Год журнала:
2025,
Номер
unknown
Опубликована: Май 21, 2025
ABSTRACT
Human‐induced
climate
change
is
reshaping
wind
patterns
across
Canada,
posing
significant
challenges
for
sectors
such
as
energy
and
infrastructure
planning.
This
study
assesses
the
capability
of
regional
models
(RCMs)
in
simulating
near‐surface
speed
(WS)
Canada
by
analysing
outputs
from
various
RCM
ensembles,
which
downscale
CMIP5
global
model
(GCM)
output,
including
NA‐CORDEX
multi‐model
ensemble
(at
0.22°
resolution)
CanRCM4
single‐model
large
0.44°
resolution).
These
are
compared
against
observational
data,
two
reanalysis
data
sets
(ERA5
AgERA5),
GCM
ensembles
CMIP6.
The
evaluation
examines
models'
ability
to
replicate
historical
WS
distributions,
biases
mean
extreme
WS,
trends
temporal
variability.
findings
reveal
that,
despite
higher
spatial
resolution
RCMs,
their
added
value
over
limited,
raising
concerns
about
reliability
RCM‐derived
projections
services
without
further
bias
adjustment
or
statistical
downscaling.
inability
both
RCMs
GCMs
accurately
simulate
diminishes
confidence
future
projections,
potentially
leading
inadequate
risk
assessments
insufficient
preparation
impacts
on
vital
like
infrastructure.
Язык: Английский
Future changes of global Annual and Seasonal Wind-Energy Production in CMIP6 projections considering air density variation
Energy,
Год журнала:
2024,
Номер
307, С. 132706 - 132706
Опубликована: Авг. 6, 2024
This
study
investigates
the
effects
of
climate
change
on
future
global
wind-energy
production,
one
main
pillars
decarbonization
strategies,
including
two
generally
disregarded
aspects:
sub-daily
variability
and
variable
air
density.
Estimation
production
by
turbines
remains
almost
unexplored
for
last
generation
scenarios,
that
is,
Shared
Socioeconomic
Pathways
(SSPs),
as
previous
evaluations
have
mostly
focused
Wind
Power
Density
(WPD).
A
complete
view
changes
in
resources
was
presented,
wind,
density,
WPD
Annual/Seasonal
Energy
Production
(AEP/SEP)
statistically
significant
four
different
SSPs
large
Multi-model
Ensembles.
Air
density
decreases
1%–4%
all
SSPs,
over
seasons
everywhere
modulating
wind
negatively.
Changes
AEP/SEP
were
comparable,
with
wider
areas
affected
stronger
expected
former.
In
most
optimistic
SSP
they
range
from
5%
to
25%,
45%
or
higher
pessimistic.
locations,
specially
oceans,
energy
is
decrease
remain
unaltered;
however,
increased
are
Arctic,
Southern
Ocean,
other
narrower
areas,
such
Bay
Guinea
southern
Brazil.
Язык: Английский
Mapping future offshore wind resources in the South China Sea under climate change by regional climate modeling
Renewable and Sustainable Energy Reviews,
Год журнала:
2023,
Номер
188, С. 113865 - 113865
Опубликована: Окт. 12, 2023
Язык: Английский
Approximation in scour depth around spur dikes using novel hybrid ensemble data-driven model
Water Science & Technology,
Год журнала:
2024,
Номер
89(4), С. 962 - 975
Опубликована: Янв. 29, 2024
Abstract
The
scouring
process
near
spur
dikes
poses
a
threat
to
riverbank
stability,
making
it
crucial
for
river
engineering
accurately
calculate
the
maximum
scour
depth.
However,
determining
depth
has
been
challenging
due
intricacy
of
phenomena
surrounding
these
structures.
This
research
introduces
reliable
ensemble
data-driven
model
by
hybridizing
random
tree
(RT)
using
additive
regression
(AR),
bagging
(B),
and
subspace
(RSS)
predicting
depths
around
dikes.
A
database
154
experimental
observations
was
collected
from
literature,
with
103
51
used
training
testing
subsets,
respectively.
dimensionless
analysis
performed
on
dataset,
selecting
four
variables
as
input
(v/vs,
y/l,
l/d50,
Fd50)
ds/l
response
variables.
performance
comparison
demonstrates
that
B_AR_RT
better
coefficient
determination
(R2)
0.9693,
root
mean
square
error
(RMSE)
0.1305,
Nash–Sutcliffe
efficiency
(NSE)
0.9692.
Finally,
best
hybrid
done
previous
studies,
sensitivity
is
determine
most
influential
parameter
Язык: Английский