Intelligent Decision Technologies,
Год журнала:
2024,
Номер
unknown, С. 1 - 10
Опубликована: Сен. 10, 2024
Air
pollution
has
become
an
international
calamity,
a
problem
for
human
health
and
the
environment.
The
ability
to
predict
air
quality
becomes
crucial
task.
usual
approaches
assessing
are
exhausted
when
extracting
complicated
non-linear
relationships
long-term
dependence
features
embedded
in
data.
Long-
short-term
memory,
recurrent
neural
network
family,
emerged
as
potent
tool
addressing
mentioned
issues,
so
computer-aided
technology
essential
aid
with
high
level
of
prediction
best-in-class
accuracy.
In
this
study,
we
investigated
classic
time-series
analysis
based
on
Improved
Long
memory
(ILSTM)
improve
performance
index
prediction.
predicted
AQI
value
25
days
lies
97.63%
Confidence
interval
zone
highly
adoptable
metrics
such
R-Square,
MSE,
RMSE,
MAE
values.
Research Square (Research Square),
Год журнала:
2024,
Номер
unknown
Опубликована: Июнь 19, 2024
Abstract
Accurate
electricity
spot
price
forecasting
is
significant
for
market
players
to
make
decisions
on
bidding
strategies.
However,
prices
are
extremely
volatile
forecast
due
the
influences
of
various
factures.
This
paper
develops
an
framework
in
combined
with
wavelet
packet
decomposition
(WPD)
algorithm
and
a
hybrid
deep
neural
network.
The
WPD
has
higher
accuracy
it
can
identify
fluctuating
trends
occasional
noise
data.
network
embedded
temporal
convolutional
(TCN)
network,
long
short-term
memory
(LSTM)
new
designed
improving
ability
feature
extraction
via
TCN
model
enhancing
efficiency
forecasting.
Case
studies
UK
confirm
that
proposed
outperforms
alternatives
accuracy.
Comparing
mean
errors
other
techniques,
average
absolute
error
(MAE),
root
square
(RMSE)
percentage
(MAPE)
method
reduced
by
27.3%,
66.9%
22.8%
respectively.
Meanwhile,
case
different
denoising
methods
datasets
demonstrate
prediction
better
analyze
fluctuations
time
series
data
certain
generalization
robustness.
Intelligent Decision Technologies,
Год журнала:
2024,
Номер
unknown, С. 1 - 10
Опубликована: Сен. 10, 2024
Air
pollution
has
become
an
international
calamity,
a
problem
for
human
health
and
the
environment.
The
ability
to
predict
air
quality
becomes
crucial
task.
usual
approaches
assessing
are
exhausted
when
extracting
complicated
non-linear
relationships
long-term
dependence
features
embedded
in
data.
Long-
short-term
memory,
recurrent
neural
network
family,
emerged
as
potent
tool
addressing
mentioned
issues,
so
computer-aided
technology
essential
aid
with
high
level
of
prediction
best-in-class
accuracy.
In
this
study,
we
investigated
classic
time-series
analysis
based
on
Improved
Long
memory
(ILSTM)
improve
performance
index
prediction.
predicted
AQI
value
25
days
lies
97.63%
Confidence
interval
zone
highly
adoptable
metrics
such
R-Square,
MSE,
RMSE,
MAE
values.
