Grey-Box Modelling of District Heating Networks Using Modified LPV Models
Energies,
Год журнала:
2025,
Номер
18(7), С. 1626 - 1626
Опубликована: Март 24, 2025
The
International
Energy
Agency
(IEA)
2023
report
highlights
that
global
energy
losses
have
persisted
over
the
years,
with
32%
of
supply
lost
in
2022
alone.
To
mitigate
this,
this
research
adopts
optimisation
to
enhance
efficiency
district
heating
networks
(DHNs),
a
key
technology.
Given
dynamic
nature
DHNs
and
challenges
predicting
disturbances,
real-time
(DRTO)
approach
is
proposed.
However,
does
not
implement
DRTO;
instead,
it
develops
fast
grey-box
linear
parameter
varying
(LPV)
model
for
future
integration
into
DRTO
algorithm.
A
high-fidelity
physical
replicating
theoretical
time
delays
pipes
serves
as
reference
validation.
For
single
pipe,
achieved
91.5%
fit
an
R2
value
0.993
operated
5
times
faster
than
model.
At
DHN
scale,
captured
98.64%
model’s
dynamics,
corresponding
0.9997,
while
operating
52
faster.
Low-fidelity
models
(LFPMs)
were
also
developed
validated,
proving
be
more
precise
models.
This
recommends
performing
both
determine
which
better
identifies
local
minima.
Язык: Английский
From data scarcity to diagnostic precision: A novel data augmentation and fault diagnosis framework for district heating substations
Engineering Applications of Artificial Intelligence,
Год журнала:
2025,
Номер
151, С. 110662 - 110662
Опубликована: Апрель 4, 2025
Язык: Английский
Power Grid Renovation: A Comprehensive Review of Technical Challenges and Innovations for Medium Voltage Cable Replacement
Smart Cities,
Год журнала:
2024,
Номер
7(6), С. 3727 - 3763
Опубликована: Дек. 3, 2024
The
rapid
growth
of
electrical
energy
demands
raises
the
need
for
modernization
distribution
grids.
Medium-voltage
(MV)
aged
cables
are
infrastructures
facing
significant
challenges
that
can
compromise
security
supply
and
reduce
reliability
power
To
address
challenges,
there
is
a
growing
interest
in
optimizing
cable
replacement
management
strategies.
This
comprehensive
review
focuses
on
technical
innovations
associated
with
MV
replacement,
highlighting
defect
detection,
lifetime
estimation,
assessment,
Various
methods
detecting
monitoring
defects
discussing
their
advantages
limitations
surveyed.
Moreover,
different
models
techniques
estimating
remaining
useful
life
explored,
emphasizing
importance
accurate
predictions
assessing
schedules.
Furthermore,
emerging
technologies
enhance
strategies
also
highlighted.
provides
insights
recommendations
future
research
development,
paving
way
sustainable
evolution
Язык: Английский
Hierarchical reconciliation of convolutional gated recurrent units for unified forecasting of branched and aggregated district heating loads
Energy,
Год журнала:
2024,
Номер
unknown, С. 134097 - 134097
Опубликована: Дек. 1, 2024
Язык: Английский
Data-Driven Reliability Analysis of District Heating Systems for Asset Management Applications: A Review
Sustainable Cities and Society,
Год журнала:
2024,
Номер
118, С. 106052 - 106052
Опубликована: Дек. 8, 2024
Язык: Английский
Modelling the Prioritisation of Technical Objects Using the EPN Indicator
Energies,
Год журнала:
2024,
Номер
17(23), С. 6170 - 6170
Опубликована: Дек. 7, 2024
The
objective
of
this
article
is
to
analyse
and
evaluate
the
effectiveness
predictive
maintenance
for
machines
performing
key
functions
within
a
production
structure.
This
presents
methodology
determining
Equipment
Priority
Number
(EPN),
calculated
based
on
parameters
such
as
energy
consumption,
criticality
in
value
stream,
their
impact
continuity
supply
chain.
experimental
implementation
system
monitoring
operational
parameters—including
current
vibrations,
torque
moments—enabled
prediction
potential
failures
planning
actions,
which
contributed
improving
stability
reducing
risk
unplanned
downtime.
obtained
results
confirm
proposed
demonstrate
that
supported
by
EPN
indicator
enables
accurate
prioritisation
activities
an
actual
system.
findings
also
show
implementing
algorithm
allows
more
precise
highly
customised
environments.
Furthermore,
analysis
collected
data
suggests
further
optimisation
through
integration
data-driven
diagnostics
artificial
intelligence
methods,
could
enhance
efficiency
competitiveness
study’s
conclusions
provide
foundation
advancing
methods
industrial
production.
Язык: Английский