Energy Reports,
Journal Year:
2022,
Volume and Issue:
8, P. 2656 - 2671
Published: Feb. 10, 2022
The
difficulty
in
balancing
energy
supply
and
demand
is
increasing
due
to
the
growth
of
diversified
flexible
building
resources,
particularly
rapid
development
intermittent
renewable
being
added
into
power
grid.
accuracy
consumption
prediction
top
priority
for
electricity
market
management
ensure
grid
safety
reduce
financial
risks.
speed
load
are
fundamental
prerequisites
different
objectives
such
as
long-term
planning
short-term
optimization
systems
buildings
past
few
decades
have
seen
impressive
time
series
forecasting
models
focusing
on
domains
objectives.
This
paper
presents
an
in-depth
review
discussion
models.
Three
widely
used
approaches,
namely,
physical
(i.e.,
white
box),
data-driven
black
hybrid
grey
were
classified
introduced.
principles,
advantages,
limitations,
practical
applications
each
model
investigated.
Based
this
review,
research
priorities
future
directions
domain
highlighted.
conclusions
drawn
could
guide
prediction,
therefore
facilitate
efficiency
buildings.
Automation in Construction,
Journal Year:
2022,
Volume and Issue:
141, P. 104440 - 104440
Published: June 24, 2022
This
article
presents
a
state-of-the-art
review
of
the
applications
Artificial
Intelligence
(AI),
Machine
Learning
(ML),
and
Deep
(DL)
in
building
construction
industry
4.0
facets
architectural
design
visualization;
material
optimization;
structural
analysis;
offsite
manufacturing
automation;
management,
progress
monitoring,
safety;
smart
operation,
management
health
monitoring;
durability,
life
cycle
analysis,
circular
economy.
paper
unique
perspective
on
AI/DL/ML
these
domains
for
complete
lifecycle,
from
conceptual
stage,
operational
maintenance
stage
until
end
life.
Furthermore,
data
collection
strategies
using
vision
sensors,
cleaning
methods
(post-processing),
storage
developing
models
are
discussed,
challenges
model
development
to
overcome
elaborated.
Future
trends
possible
research
avenues
also
presented.
Applied Energy,
Journal Year:
2018,
Volume and Issue:
221, P. 386 - 405
Published: April 17, 2018
In
this
paper,
a
novel
modeling
framework
for
forecasting
electricity
prices
is
proposed.
While
many
predictive
models
have
been
already
proposed
to
perform
task,
the
area
of
deep
learning
algorithms
remains
yet
unexplored.
To
fill
scientific
gap,
we
propose
four
different
predicting
and
show
how
they
lead
improvements
in
accuracy.
addition,
also
consider
that,
despite
large
number
methods
prices,
an
extensive
benchmark
still
missing.
tackle
compare
analyze
accuracy
27
common
approaches
price
forecasting.
Based
on
results,
outperform
state-of-the-art
obtain
results
that
are
statistically
significant.
Finally,
using
same
that:
(i)
machine
yield,
general,
better
than
statistical
models;
(ii)
moving
average
terms
do
not
improve
accuracy;
(iii)
hybrid
their
simpler
counterparts.
Journal of Building Engineering,
Journal Year:
2020,
Volume and Issue:
32, P. 101827 - 101827
Published: Sept. 19, 2020
The
construction
industry
is
known
to
be
overwhelmed
with
resource
planning,
risk
management
and
logistic
challenges
which
often
result
in
design
defects,
project
delivery
delays,
cost
overruns
contractual
disputes.
These
have
instigated
research
the
application
of
advanced
machine
learning
algorithms
such
as
deep
help
diagnostic
prescriptive
analysis
causes
preventive
measures.
However,
publicity
created
by
tech
firms
like
Google,
Facebook
Amazon
about
Artificial
Intelligence
applications
unstructured
data
not
end
field.
There
abound
many
learning,
particularly
within
sector
areas
site
planning
management,
health
safety
prediction,
are
yet
explored.
overall
aim
this
article
was
review
existing
studies
that
applied
prevalent
structural
monitoring,
safety,
building
occupancy
modelling
energy
demand
prediction.
To
best
our
knowledge,
there
currently
no
extensive
survey
techniques
industry.
This
would
inspire
future
into
how
apply
image
processing,
computer
vision,
natural
language
processing
numerous
Limitations
black
box
challenge,
ethics
GDPR,
cybersecurity
cost,
can
expected
researchers
practitioners
when
adopting
some
these
were
also
discussed.
Advanced Engineering Informatics,
Journal Year:
2020,
Volume and Issue:
45, P. 101122 - 101122
Published: June 5, 2020
This
paper
presents
a
study
on
the
usage
landscape
of
augmented
reality
(AR)
and
virtual
(VR)
in
architecture,
engineering
construction
sectors,
proposes
research
agenda
to
address
existing
gaps
required
capabilities.
A
series
exploratory
workshops
questionnaires
were
conducted
with
participation
54
experts
from
36
organisations
industry
academia.
Based
data
collected
workshops,
six
AR
VR
use-cases
defined:
stakeholder
engagement,
design
support,
review,
operations
management
training.
Three
main
categories
for
future
have
been
proposed,
i.e.:
(i)
engineering-grade
devices,
which
encompasses
that
enables
robust
devices
can
be
used
practice,
e.g.
rough
complex
conditions
sites;
(ii)
workflow
management;
effectively
manage
processes
by
technologies;
(iii)
new
capabilities;
includes
will
add
features
are
necessary
specific
demands.
provides
essential
information
practitioners
inform
adoption
decisions.
To
researchers,
it
road
map
their
efforts.
is
foundational
formalises
categorises
roadmap
guide
