Optimizing Sustainable Power Generation with Triplet Deep Borehole Heat Exchangers: A Machine Learning Approach
A. A. Magaji,
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Bin Dou,
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AL-Wesabi Ibrahim
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et al.
Research Square (Research Square),
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 1, 2025
Abstract
Geothermal
energy,
a
renewable
and
sustainable
resource,
has
significant
potential
for
meeting
global
energy
demands;
most
of
the
study
on
production
generation
relies
numerical
simulation.
However,
computational
intensity
physics-based
simulations
geothermal
poses
challenges.
This
explores
integration
machine
learning
models
with
simulation
to
forecast
long-term
electricity
from
triplet
deep
borehole
heat
exchanger
system.
A
large
dataset
generated
through
COMSOL
Multiphysics
served
as
input
three
models:
Decision
Tree,
XGBoost,
Random
Forest.
The
Forest
model
outperformed
others,
achieving
lowest
error
metrics
Root
Mean
Square
Percentage
Error
(RMSPE)
0.104,
Absolute
(MAPE)
0.0539,
highest
R²
value
0.9996.
These
indicate
that
RF
provides
exceptional
prediction
accuracy
generalization
capabilities.
combined
approach
significantly
reduced
time
required,
enabling
forecasting
an
additional
15
years
power
using
Forest,
which
makes
it
easier
faster
than
waiting
almost
21
hours
before
simulating
25
years.
results
confirm
viability
optimizing
forecasting,
ensuring
sustainability
operational
efficiency
in
generation.
Language: Английский
Optimization of staggered peak intermittent pumping operation scheduling of pumping unit well clusters under wind, solar and energy storage microgrid with improved adaptive GAPSO hybrid algorithm
Geoenergy Science and Engineering,
Journal Year:
2025,
Volume and Issue:
unknown, P. 213897 - 213897
Published: April 1, 2025
Language: Английский
A computational fluid dynamics (CFD) study on extraction of geothermal energy in Bangladesh
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Journal Year:
2025,
Volume and Issue:
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Published: April 1, 2025
Language: Английский
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Journal Year:
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Volume and Issue:
unknown, P. 100814 - 100814
Published: Nov. 1, 2024
Language: Английский