Innovative machine learning approaches for indoor air temperature forecasting in smart infrastructure
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Янв. 2, 2025
Abstract
Efficient
energy
management
and
maintaining
an
optimal
indoor
climate
in
buildings
are
critical
tasks
today’s
world.
This
paper
presents
innovative
approach
to
surrogate
modeling
for
predicting
air
temperature
(IAT)
buildings,
leveraging
advanced
machine
learning
techniques.
At
the
core
of
this
study
is
application
Long
Short-Term
Memory
(LSTM)
networks
time-series
modeling,
which
significantly
enhances
capture
temporal
dependencies
predictions.
The
proposed
LSTM
with
RWCV
(Rolling
Window
Cross-Validation)
offers
significant
advantages
over
a
usual
tasks,
particularly
due
its
ability
adapt
new
data
trends
through
rolling
window
mechanism.
It
provides
more
robust
generalizable
forecasts
dynamic
environments,
prevents
overfitting
dropout
cross-validation,
improves
model
evaluation
integrity.
In
contrast,
traditional
models
better
suited
static,
non-evolving
datasets
may
not
handle
effectively.
To
rigorously
assess
performance,
comprehensive
framework
developed,
incorporating
metrics
such
as
mean
square
error
(MSE)
coefficient
determination
(R²).
Additionally,
novel
cumulative
analysis
method
introduced
enabling
real-time
monitoring
adjustment
maintain
predictive
accuracy
time.
Test
results
demonstrate
that
losses
on
test
dataset
only
marginally
higher
than
those
training
dataset,
indicating
generalization
capabilities.
Loss
values
range
from
0.0004709
0.02819861,
depending
building
operating
conditions.
A
comparative
reveals
Adaboost
Gradient
Boosting
outperform
linear
regression,
highlighting
their
potential
achieving
energy-efficient
comfortable
buildings.
findings
underscore
efficacy
IAT
prediction
point
towards
further
research
possibilities
expansion
optimization
enhance
conservation.
Язык: Английский
SolarFlux Predictor: A Novel Deep Learning Approach for Photovoltaic Power Forecasting in South Korea
Electronics,
Год журнала:
2024,
Номер
13(11), С. 2071 - 2071
Опубликована: Май 27, 2024
We
present
SolarFlux
Predictor,
a
novel
deep-learning
model
designed
to
revolutionize
photovoltaic
(PV)
power
forecasting
in
South
Korea.
This
uses
self-attention-based
temporal
convolutional
network
(TCN)
process
and
predict
PV
outputs
with
high
precision.
perform
meticulous
data
preprocessing
ensure
accurate
normalization
outlier
rectification,
which
are
vital
for
reliable
analysis.
The
TCN
layers
crucial
capturing
patterns
energy
data;
we
complement
them
the
teacher
forcing
technique
during
training
phase
significantly
enhance
sequence
prediction
accuracy.
By
optimizing
hyperparameters
Optuna,
further
improve
model’s
performance.
Our
incorporates
multi-head
self-attention
mechanisms
focus
on
most
impactful
features,
thereby
improving
In
validations
against
datasets
from
nine
regions
Korea,
outperformed
conventional
methods.
results
indicate
that
is
robust
tool
systems’
management
operational
efficiency
can
contribute
Korea’s
pursuit
of
sustainable
solutions.
Язык: Английский
Exergy focused optimum solar panel tilt angle determination with improved hybrid model: The case of Turkey
Engineering Applications of Artificial Intelligence,
Год журнала:
2025,
Номер
145, С. 110220 - 110220
Опубликована: Фев. 8, 2025
Язык: Английский
Multi-Building Energy Forecasting Through Weather-Integrated Temporal Graph Neural Networks
Samuel Moveh,
Emmanuel Alejandro Merchán-Cruz,
Maher Abuhussain
и другие.
Buildings,
Год журнала:
2025,
Номер
15(5), С. 808 - 808
Опубликована: Март 3, 2025
While
existing
building
energy
prediction
methods
have
advanced
significantly,
they
face
fundamental
challenges
in
simultaneously
modeling
complex
spatial–temporal
relationships
between
buildings
and
integrating
dynamic
weather
patterns,
particularly
dense
urban
environments
where
interactions
significantly
impact
consumption
patterns.
This
study
presents
an
deep
learning
system
combining
temporal
graph
neural
networks
with
data
parameters
to
enhance
accuracy
across
diverse
types
through
innovative
modeling.
approach
integrates
LSTM
layers
convolutional
networks,
trained
using
from
150
commercial
over
three
years.
The
incorporates
spatial
a
weighted
adjacency
matrix
considering
proximity
operational
similarities,
while
are
integrated
via
specialized
network
component.
Performance
evaluation
examined
normal
operations,
gaps,
seasonal
variations.
results
demonstrated
3.2%
mean
absolute
percentage
error
(MAPE)
for
15
min
predictions
4.2%
MAPE
24
h
forecasts.
showed
robust
recovery,
maintaining
95.8%
effectiveness
even
30%
missing
values.
Seasonal
analysis
revealed
consistent
performance
conditions
(MAPE:
3.1–3.4%).
achieved
33.3%
better
compared
conventional
methods,
75%
efficiency
four
GPUs.
These
findings
demonstrate
the
of
prediction,
providing
valuable
insights
management
systems
planning.
system’s
scalability
make
it
suitable
practical
applications
smart
sustainability.
Язык: Английский
A Novel Capacitive Model of Radiators for Building Dynamic Simulations
Thermo,
Год журнала:
2025,
Номер
5(1), С. 9 - 9
Опубликована: Март 11, 2025
This
study
addresses
the
critical
challenge
of
performing
a
detailed
calculation
energy
savings
in
buildings
by
implementing
suitable
actions
aiming
at
reducing
greenhouse
gas
emissions.
Given
high
consumption
buildings’
space
heating
systems,
optimizing
their
performance
is
crucial
for
overall
primary
demand.
Unfortunately,
calculations
such
are
often
based
on
extremely
simplified
methods,
neglecting
dynamics
emitters
installed
inside
buildings.
These
approximations
may
lead
to
relevant
errors
estimation
possible
savings.
In
this
framework,
present
presents
novel
0-dimensional
capacitive
model
radiator,
most
common
emitter
used
residential
The
final
scope
paper
integrate
within
TRNSYS
18simulation
environment,
1-year
simulation
building-space
system.
radiator
developed
MATLAB
2024b
and
it
carefully
considers
impact
surface
area,
inlet
temperature,
flow
rate
performance.
Moreover,
dynamic
heat
transfer
compared
with
one
returned
built-in
non-capacitive
available
TRNSYS,
showing
that
effect
radiators
leads
an
incorrect
consumption.
During
winter
season,
system
turned
from
8
a.m.
4
p.m.
6
p.m.,
thermal
underestimated
roughly
20%
commonly
model.
Язык: Английский
Leveraging Machine Learning for Predictive Sustainability in Business Operations: A Classification Approach to Optimize Sustainable Resource Management
Studies in big data,
Год журнала:
2025,
Номер
unknown, С. 671 - 682
Опубликована: Янв. 1, 2025
Язык: Английский
A Novel Intelligent Scheme for Building Energy Prediction Based On Machine Learning and Deep Learning Algorithms
Опубликована: Янв. 1, 2024
Язык: Английский
Detection and Early Warning of Duponchelia fovealis Zeller (Lepidoptera: Crambidae) Using an Automatic Monitoring System
AgriEngineering,
Год журнала:
2024,
Номер
6(4), С. 3785 - 3798
Опубликована: Окт. 18, 2024
In
traditional
pest
monitoring,
specimens
are
manually
inspected,
identified,
and
counted.
These
techniques
can
lead
to
poor
data
quality
hinder
effective
management
decisions
due
operational
economic
limitations.
This
study
aimed
develop
an
automatic
detection
early
warning
system
using
the
European
Pepper
Moth,
Duponchelia
fovealis
(Lepidoptera:
Crambidae),
as
a
model.
A
prototype
water
trap
equipped
with
infrared
digital
camera
controlled
microprocessor
served
attraction
capture
device.
Images
captured
by
in
laboratory
were
processed
detect
objects.
Subsequently,
these
objects
labeled,
size
shape
features
extracted.
machine
learning
model
was
then
trained
identify
number
of
insects
present
trap.
The
achieved
99%
accuracy
identifying
target
during
validation
30%
data.
Finally,
deployed
field
for
result
confirmation.
Язык: Английский
Development of a Hybrid Intelligence Algorithm to Estimate the Derivative Weight of Dawakin Tofa Clay for Heat Storage
AUIQ technical engineering science.,
Год журнала:
2024,
Номер
1(2)
Опубликована: Дек. 16, 2024
The
accurate
prediction
of
thermogravimetric
properties
is
critical
for
evaluating
the
suitability
natural
materials
like
Dawakin
Tofa
clay
heat
storage
applications,
but
traditional
linear
models
often
fail
to
capture
complex,
non-linear
relationships
inherent
in
such
datasets.
This
study
develops
a
hybrid
intelligence
framework
integrating
Bilateral
Neural
Network
(BNN),
Kernel
Support
Vector
Machine
(KSVM),
Step-Wise
Linear
Regression
(SWLR),
and
Robust
(RLR)
predict
derivative
weight
based
on
5,030
experimentally
obtained
instances.
Comprehensive
data
preprocessing,
including
normalization,
feature
selection,
dataset
splitting
(80%
training
20%
testing),
ensured
high-quality
inputs
models.
results
demonstrated
that
significantly
outperformed
approaches,
with
BNN
achieving
coefficient
determination
R²
0.999,
Mean
Absolute
Error
(MAE)
0.004377,
Percentage
(MAPE)
9.6%
testing
dataset.
Similarly,
KSVM
achieved
an
MAE
0.012134,
MAPE
26.7%,
indicating
its
robust
predictive
capabilities.
In
contrast,
performed
poorly,
SWLR
RLR
yielding
values
0.03
-0.41,
respectively,
unacceptably
high
612%
53.5%.
findings
underscore
limitations
predicting
complex
behaviors
highlight
transformative
potential
advanced
machine
learning
techniques
KSVM.
Furthermore,
these
align
global
sustainability
efforts,
SDG
7
12,
by
optimizing
use
locally
available,
eco-friendly
energy
storage.
provides
replicable
leveraging
artificial
enhance
material
characterization,
offering
significant
step
toward
developing
sustainable
solutions.
Язык: Английский