Large language models for building energy applications: Opportunities and challenges
Building Simulation,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 17, 2025
Язык: Английский
Exploring automated energy optimization with unstructured building data: A multi-agent based framework leveraging large language models
Energy and Buildings,
Год журнала:
2024,
Номер
322, С. 114691 - 114691
Опубликована: Авг. 23, 2024
Язык: Английский
Domain-specific large language models for fault diagnosis of heating, ventilation, and air conditioning systems by labeled-data-supervised fine-tuning
Applied Energy,
Год журнала:
2024,
Номер
377, С. 124378 - 124378
Опубликована: Сен. 5, 2024
Язык: Английский
Defining and Generating Operation and Maintenance Management Requirements in Digital Twin Applications Using the DT-GPT Framework
Journal of Building Engineering,
Год журнала:
2025,
Номер
unknown, С. 112356 - 112356
Опубликована: Март 1, 2025
Язык: Английский
Building a construction law knowledge repository to enhance general-purpose large language model performance on domain question-answering: a case of China
Engineering Construction & Architectural Management,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 30, 2025
Purpose
Achieving
smart
question-answering
(QA)
for
construction
laws
(CLs)
holds
significant
promise
in
aiding
domain
professionals
with
legal
inquiries.
Existing
studies
of
law
(CLQA)
rely
on
learning-based
models,
which
require
extensive
training
data
and
are
limited
to
a
narrow
QA
scope.
Meanwhile,
general-purpose
large
language
models
(GPLLMs)
possess
great
potential
CLQA
but
fall
short
domain-specific
knowledge.
This
study
aims
propose
data-driven
expertise-based
approach
develop
knowledge
repository
(CLKR)
validate
its
effectiveness
enhancing
the
performance
GPLLMs.
Design/methodology/approach
methodology
includes
(1)
recognizing
702
candidate
CL
documents
from
374,992
official
judgments,
(2)
building
CLKR
387
filtered
covering
eight
areas,
(3)
integrating
seven
representative
GPLLMs
(4)
constructing
2,140-question
dataset
Professional
Construction
Engineer
Qualification
Examinations
(PCEQEs)
during
2014–2023
compare
between
pairs
without
CLKR.
Findings
The
significantly
enhances
GPLLMs,
yielding
an
impressive
average
accuracy
increase
21.1%,
individual
improvements
ranging
9.9
44.9%.
Furthermore,
boosts
single-answer
questions
by
14.9%
multiple-answer
38.3%.
Additionally,
enhancements
across
8
areas
14.5
28.2%.
Originality/value
proposes
developing
external
base
empower
expanding
scope
while
bypassing
complex
traditional
models.
Moreover,
this
confirms
augmenting
GPLLM
offers
reusable
test
as
benchmark.
Язык: Английский
A review of LLMs and their applications in the architecture, engineering and construction industry
Artificial Intelligence Review,
Год журнала:
2025,
Номер
58(8)
Опубликована: Май 16, 2025
Abstract
During
the
past
decade,
there
has
been
rapid
emergence,
continuous
development
and
advancements
in
field
of
Artificial
Intelligence
(AI),
a
broad
adaptation
ofLarge
Language
Models
(LLMs)
wide
variety
application
domains
transforming
streamlining
industry
practices.
However,
construction
yet
to
fully
incorporate
these
technologies,
delaying
their
wide-scale
adaptation.
Only
limited
number
recent
studies
have
explored
opportunities,
capabilities
potential
current
LLM
implementations
domain
Architecture
Engineering
Construction
(AEC)
industry,
leaving
significant
gap
this
research.
This
study
aims
address
provide
an
extensive
review
already
established
state-of-the-art
applications
use
case
scenarios
LLMs
AEC
industry.
Apart
from
that,
by
exploring
key
contributions
limitations
applications,
considering
relative
reviews
on
subject,
it
was
possible
categorize
them,
extract
emerging
challenges
future
directions
propose
actionable
recommendations
for
stakeholders.
also
includes
introduction
important
concepts
focusing
transformer-based
architectures
providing
list
families.
Язык: Английский
Customized large-scale model for human-AI collaborative operation and maintenance management of building energy systems
Applied Energy,
Год журнала:
2025,
Номер
393, С. 126169 - 126169
Опубликована: Май 20, 2025
Язык: Английский
Deep generative models in energy system applications: Review, challenges, and future directions
Applied Energy,
Год журнала:
2024,
Номер
380, С. 125059 - 125059
Опубликована: Дек. 13, 2024
Язык: Английский
Self-attention variational autoencoder-based method for incomplete model parameter imputation of digital twin building energy systems
Energy and Buildings,
Год журнала:
2024,
Номер
unknown, С. 115162 - 115162
Опубликована: Дек. 1, 2024
Язык: Английский
A Comprehensive Survey of Retrieval-Augmented Large Language Models for Decision Making in Agriculture: Unsolved Problems and Research Opportunities
Journal of Artificial Intelligence and Soft Computing Research,
Год журнала:
2024,
Номер
15(2), С. 115 - 146
Опубликована: Дек. 1, 2024
Abstract
The
breakthrough
in
developing
large
language
models
(LLMs)
over
the
past
few
years
has
led
to
their
widespread
implementation
various
areas
of
industry,
business,
and
agriculture.
aim
this
article
is
critically
analyse
generalise
known
results
research
directions
on
approaches
development
utilisation
LLMs,
with
a
particular
focus
functional
characteristics
when
integrated
into
decision
support
systems
(DSSs)
for
agricultural
monitoring.
subject
integration
LLMs
DSSs
agrotechnical
main
scientific
applied
are
as
follows:
world
experience
using
improve
processes
been
analysed;
critical
analysis
carried
out,
application
architectures
have
identified;
necessity
focusing
retrieval-augmented
generation
(RAG)
an
approach
solving
one
limitations
which
limited
knowledge
base
training
data,
established;
prospects
agriculture
analysed
highlight
trustworthiness,
explainability
bias
reduction
priority
research;
potential
socio-economic
effect
from
RAG
sector
substantiated.
Язык: Английский