The rapid progression in artificial intelligence has facilitated the
emergence of large language models like ChatGPT, offering potential
applications extending into specialized engineering modeling, especially
physics-based building energy modeling. This paper investigates the innovative
integration of large language models with building energy modeling software,
focusing specifically on the fusion of ChatGPT with EnergyPlus. A literature
review is first conducted to reveal a growing trend of incorporating of large
language models in engineering modeling, albeit limited research on their
application in building energy modeling. We underscore the potential of large
language models in addressing building energy modeling challenges and outline
potential applications including 1) simulation input generation, 2) simulation
output analysis and visualization, 3) conducting error analysis, 4)
co-simulation, 5) simulation knowledge extraction and training, and 6)
simulation optimization. Three case studies reveal the transformative potential
of large language models in automating and optimizing building energy modeling
tasks, underscoring the pivotal role of artificial intelligence in advancing
sustainable building practices and energy efficiency. The case studies
demonstrate that selecting the right large language model techniques is
essential to enhance performance and reduce engineering efforts. Besides direct
use of large language models, three specific techniques were utilized: 1)
prompt engineering, 2) retrieval-augmented generation, and 3) multi-agent large
language models. The findings advocate a multidisciplinary approach in future
artificial intelligence research, with implications extending beyond building
energy modeling to other specialized engineering modeling.

该研究旨在调查大型语言模型与建筑能源模型软件的创新整合，重点关注 ChatGPT 与 EnergyPlus 的融合。文献综述表明，大型语言模型在工程建模中应用日益增长，但在建筑能源模型中的应用研究有限。通过三个案例研究，证明了大型语言模型在自动化和优化建筑能源模型任务方面的潜力，并强调了人工智能在推进可持续建筑实践和能源效率方面的关键作用。研究结果建议未来人工智能研究采用跨学科的方法，并对其他专门工程建模领域有着重要影响。

利用大型语言模型推进建筑能源建模：探索与案例研究

Advancing Building Energy Modeling with Large Language Models:  Exploration and Case Studies

Missing data are frequently observed by practitioners and researchers in the
building energy modeling community. In this regard, advanced data-driven
solutions, such as Deep Learning methods, are typically required to reflect the
non-linear behavior of these anomalies. As an ongoing research question related
to Deep Learning, a model's applicability to limited data settings can be
explored by introducing prior knowledge in the network. This same strategy can
also lead to more interpretable predictions, hence facilitating the field
application of the approach. For that purpose, the aim of this paper is to
propose the use of Physics-informed Denoising Autoencoders (PI-DAE) for missing
data imputation in commercial buildings. In particular, the presented method
enforces physics-inspired soft constraints to the loss function of a Denoising
Autoencoder (DAE). In order to quantify the benefits of the physical component,
an ablation study between different DAE configurations is conducted. First,
three univariate DAEs are optimized separately on indoor air temperature,
heating, and cooling data. Then, two multivariate DAEs are derived from the
previous configurations. Eventually, a building thermal balance equation is
coupled to the last multivariate configuration to obtain PI-DAE. Additionally,
two commonly used benchmarks are employed to support the findings. It is shown
how introducing physical knowledge in a multivariate Denoising Autoencoder can
enhance the inherent model interpretability through the optimized physics-based
coefficients. While no significant improvement is observed in terms of
reconstruction error with the proposed PI-DAE, its enhanced robustness to
varying rates of missing data and the valuable insights derived from the
physics-based coefficients create opportunities for wider applications within
building systems and the built environment.

建筑能源建模中经常观察到的缺失数据需要用先进的数据驱动解决方案来反映这些异常的非线性行为。本文提出了在商业建筑中使用基于物理信息的去噪自编码器 (PI-DAE) 进行缺失数据填补的方法，并通过引入物理知识到网络中实现了更可解释的预测，从而促进了该方法的实际应用。

打开黑匣子：利用建筑物理学洞察实现内在可解释的能源数据填补模型

Opening the Black Box: Towards inherently interpretable energy data  imputation models using building physics insight

The 2030 Challenge is aimed at making all new buildings and major renovations
carbon neutral by 2030. One of the potential solutions to meet this challenge
is through innovative sustainable design strategies. For developing such
strategies it is important to understand how the various building factors
contribute to energy usage of a building, right at design time. The growth of
artificial intelligence (AI) in recent years provides an unprecedented
opportunity to advance sustainable design by learning complex relationships
between building factors from available data. However, rich training datasets
are needed for AI-based solutions to achieve good prediction accuracy.
Unfortunately, obtaining training datasets are time consuming and expensive in
many real-world applications. Motivated by these reasons, we address the
problem of accurately predicting the energy usage of new or unknown building
types, i.e., those building types that do not have any training data. We
propose a novel approach based on zero-shot learning (ZSL) to solve this
problem. Our approach uses side information from building energy modeling
experts to predict the closest building types for a given new/unknown building
type. We then obtain the predicted energy usage for the k-closest building
types using the models learned during training and combine the predicted values
using a weighted averaging function. We evaluated our approach on a dataset
containing five building types generated using BuildSimHub, a popular platform
for building energy modeling. Our approach achieved better average accuracy
than a regression model (based on XGBoost) trained on the entire dataset of
known building types.

利用零样本学习方法，本文提出了一种基于建筑能源模型的预测建筑能耗的方法，该方法使用侧面信息来预测最接近给定未知建筑类型的建筑类型，并在训练中学习建筑模型，并使用加权平均函数组合所预测的建筑能耗值，其平均精度比基于 XGBoost 的回归模型训练的知名建筑类型数据集更优。