Renewable and Sustainable Energy Reviews,
Journal Year:
2024,
Volume and Issue:
193, P. 114283 - 114283
Published: Jan. 9, 2024
Because
of
their
low
computational
costs,
surrogate
models
(SMs),
also
known
as
meta-models,
have
attracted
attention
simplified
approximations
detailed
simulations.
Besides
conventional
statistical
approaches,
machine-learning
techniques,
such
neural
networks
(NNs),
been
used
to
develop
models.
However,
based
on
NNs
are
currently
not
developed
in
a
consistent
manner.
The
development
process
the
is
adequately
described
most
studies.
There
may
be
some
doubt
regarding
abilities
due
lack
documented
validation.
In
order
address
these
issues,
this
paper
presents
protocol
for
systematic
NN-based
and
how
procedure
should
reported
justified.
covers
model
sample
generation,
data
processing,
SM
training
validation,
report
implementation,
justify
modeling
choices.
critically
review
quality
SMs
prediction
building
energy
consumption.
Sixty-eight
papers
reviewed,
details
summarized.
developing
procedures
were
evaluated
using
criteria
proposed
protocol.
results
show
that
selection
number
neurons
best-implemented
step
with
justification,
followed
by
determination
architecture,
mostly
justified
discussion
way.
While
greater
focus
given
dataset
especially
input
variables
selection,
considering
independence
check
clear
validation
test
data.
Also,
preprocessing
strongly
recommended.
Energies,
Journal Year:
2022,
Volume and Issue:
15(2), P. 486 - 486
Published: Jan. 11, 2022
A
synthetic
review
of
the
application
multi-objective
optimization
models
to
design
climate-responsive
buildings
and
neighbourhoods
is
carried
out.
The
focused
on
software
utilized
during
both
simulation
stages,
as
well
objective
functions
variables.
hereby
work
aims
at
identifying
knowledge
gaps
future
trends
in
research
field
automation
buildings.
Around
140
scientific
journal
articles,
published
between
2014
2021,
were
selected
from
Scopus
Web
Science
databases.
three-step
selection
process
was
applied
refine
search
terms
discard
works
investigating
mechanical,
structural,
seismic
topics.
Meta-analysis
results
highlighted
that
are
widely
exploited
for
(i)
enhancing
building’s
energy
efficiency,
(ii)
improving
thermal
(iii)
visual
comfort,
minimizing
(iv)
life-cycle
costs,
(v)
emissions.
Reviewed
workflows
demonstrated
be
suitable
exploring
different
alternatives
building
envelope,
systems
layout,
occupancy
patterns.
Nonetheless,
there
still
some
aspects
need
further
enhanced
fully
enable
their
potential
such
ability
operate
multiple
temporal
spatial
scales
possibility
strategies
based
sector
coupling
improve
a
efficiency.
Renewable and Sustainable Energy Reviews,
Journal Year:
2024,
Volume and Issue:
193, P. 114283 - 114283
Published: Jan. 9, 2024
Because
of
their
low
computational
costs,
surrogate
models
(SMs),
also
known
as
meta-models,
have
attracted
attention
simplified
approximations
detailed
simulations.
Besides
conventional
statistical
approaches,
machine-learning
techniques,
such
neural
networks
(NNs),
been
used
to
develop
models.
However,
based
on
NNs
are
currently
not
developed
in
a
consistent
manner.
The
development
process
the
is
adequately
described
most
studies.
There
may
be
some
doubt
regarding
abilities
due
lack
documented
validation.
In
order
address
these
issues,
this
paper
presents
protocol
for
systematic
NN-based
and
how
procedure
should
reported
justified.
covers
model
sample
generation,
data
processing,
SM
training
validation,
report
implementation,
justify
modeling
choices.
critically
review
quality
SMs
prediction
building
energy
consumption.
Sixty-eight
papers
reviewed,
details
summarized.
developing
procedures
were
evaluated
using
criteria
proposed
protocol.
results
show
that
selection
number
neurons
best-implemented
step
with
justification,
followed
by
determination
architecture,
mostly
justified
discussion
way.
While
greater
focus
given
dataset
especially
input
variables
selection,
considering
independence
check
clear
validation
test
data.
Also,
preprocessing
strongly
recommended.
