Solar,
Journal Year:
2025,
Volume and Issue:
5(1), P. 7 - 7
Published: March 6, 2025
The
rapid
acceptance
of
solar
photovoltaic
(PV)
energy
across
various
countries
has
created
a
pressing
need
for
more
coordinated
approaches
to
the
sustainable
monitoring
and
maintenance
these
widely
distributed
installations.
To
address
this
challenge,
several
digitization
architectures
have
been
proposed,
with
one
most
recently
applied
being
digital
twin
(DT)
system
architecture.
DTs
proven
effective
in
predictive
maintenance,
prototyping,
efficient
manufacturing,
reliable
monitoring.
However,
while
DT
concept
is
well
established
fields
like
wind
conversion
monitoring,
its
scope
implementation
PV
remains
quite
limited.
Additionally,
recent
increased
adoption
autonomous
platforms,
particularly
robotics,
expanded
management
revealed
gaps
real-time
needs.
platforms
can
be
redesigned
ease
such
applications
enable
integration
into
broader
network.
This
work
provides
system-level
overview
current
trends,
challenges,
future
opportunities
within
renewable
systems,
focusing
on
systems.
It
also
highlights
how
advances
artificial
intelligence
(AI),
internet-of-Things
(IoT),
systems
leveraged
create
digitally
connected
infrastructure
that
supports
supply
maintenance.
Abstract
Extreme
events
such
as
heat
waves
and
cold
spells,
droughts,
heavy
rain,
storms
are
particularly
challenging
to
predict
accurately
due
their
rarity
chaotic
nature,
because
of
model
limitations.
However,
recent
studies
have
shown
that
there
might
be
systemic
predictability
is
not
being
leveraged,
whose
exploitation
could
meet
the
need
for
reliable
predictions
aggregated
extreme
weather
measures
on
timescales
from
weeks
decades
ahead.
Recently,
numerous
been
devoted
use
artificial
intelligence
(AI)
study
make
climate
predictions.
AI
techniques
great
potential
improve
prediction
uncover
links
large‐scale
local
drivers.
Machine
deep
learning
explored
enhance
prediction,
while
causal
discovery
explainable
tested
our
understanding
processes
underlying
predictability.
Hybrid
combining
AI,
which
can
reveal
unknown
spatiotemporal
connections
data,
with
models
provide
theoretical
foundation
interpretability
physical
world,
improving
skills
extremes
climate‐relevant
possible.
challenges
persist
in
various
aspects,
including
data
curation,
uncertainty,
generalizability,
reproducibility
methods,
workflows.
This
review
aims
at
overviewing
achievements
subseasonal
decadal
timescale.
A
few
best
practices
identified
increase
trust
these
novel
techniques,
future
perspectives
envisaged
further
scientific
development.
article
categorized
under:
Climate
Models
Modeling
>
Knowledge
Generation
The
Social
Status
Change
Science
Decision
Making
Solar,
Journal Year:
2025,
Volume and Issue:
5(1), P. 7 - 7
Published: March 6, 2025
The
rapid
acceptance
of
solar
photovoltaic
(PV)
energy
across
various
countries
has
created
a
pressing
need
for
more
coordinated
approaches
to
the
sustainable
monitoring
and
maintenance
these
widely
distributed
installations.
To
address
this
challenge,
several
digitization
architectures
have
been
proposed,
with
one
most
recently
applied
being
digital
twin
(DT)
system
architecture.
DTs
proven
effective
in
predictive
maintenance,
prototyping,
efficient
manufacturing,
reliable
monitoring.
However,
while
DT
concept
is
well
established
fields
like
wind
conversion
monitoring,
its
scope
implementation
PV
remains
quite
limited.
Additionally,
recent
increased
adoption
autonomous
platforms,
particularly
robotics,
expanded
management
revealed
gaps
real-time
needs.
platforms
can
be
redesigned
ease
such
applications
enable
integration
into
broader
network.
This
work
provides
system-level
overview
current
trends,
challenges,
future
opportunities
within
renewable
systems,
focusing
on
systems.
It
also
highlights
how
advances
artificial
intelligence
(AI),
internet-of-Things
(IoT),
systems
leveraged
create
digitally
connected
infrastructure
that
supports
supply
maintenance.
