Accurate occupancy information helps to improve building energy efficiency
and occupant comfort. Occupancy detection methods based on CO2 sensors have
received attention due to their low cost and low intrusiveness. In naturally
ventilated buildings, the accuracy of CO2-based occupancy detection is
generally low in related studies due to the complex ventilation behavior and
the difficulty in measuring the actual air exchange through windows. In this
study, we present two novel features for occupancy detection based on the
spatial distribution of the CO2 concentration. After a quantitative analysis
with Support Vector Machine (SVM) as classifier, it was found that the accuracy
of occupancy state detection in naturally ventilated rooms could be improved by
up to 14.8 percentage points compared to the baseline, reaching 83.2 % (F1
score 0.84) without any ventilation information. With ventilation information,
the accuracy reached 87.6 % (F1 score 0.89). The performance of occupancy
quantity detection was significantly improved by up to 25.3 percentage points
versus baseline, reaching 56 %, with root mean square error (RMSE) of 11.44
occupants, using only CO2-related features. Additional ventilation information
further enhanced the performance to 61.8 % (RMSE 9.02 occupants). By
incorporating spatial features, the model using only CO2-related features
revealed similar performance as the model containing additional ventilation
information, resulting in a better low-cost occupancy detection method for
naturally ventilated buildings.

通过在二氧化碳浓度的空间分布上进行定量分析，本研究提出了两种新颖的用于基于 CO2 浓度的房间占有检测的特征，通过支持向量机作为分类器，将房间占有状态检测的准确率提高 14.8 个百分点，达到 83.2%（F1 得分 0.84）。同时，房间占有数量检测的性能也得到了显著提升，与基线相比，提高了 25.3 个百分点，达到 56%（均方根误差 11.44 人），进一步加入通风信息后，性能提高到 61.8%（均方根误差 9.02 人）。通过将空间特征纳入模型中，仅使用 CO2 相关特征的模型展现了与包含额外通风信息的模型类似的性能，从而为自然通风建筑提供了更好的低成本占有检测方法。

自然通风校舍中二氧化碳的空间特征用于占用检测

Spatial features of CO2 for occupancy detection in a naturally  ventilated school building

In recent years, the rapid advancement and impressive capabilities of Large
Language Models (LLMs) have been evident across various domains. This paper
explores the application, implications, and potential of LLMs in building
energy efficiency and decarbonization studies. The wide-ranging capabilities of
LLMs are examined in the context of the building energy field, including
intelligent control systems, code generation, data infrastructure, knowledge
extraction, and education. Despite the promising potential of LLMs, challenges
including complex and expensive computation, data privacy, security and
copyright, complexity in fine-tuned LLMs, and self-consistency are discussed.
The paper concludes with a call for future research focused on the enhancement
of LLMs for domain-specific tasks, multi-modal LLMs, and collaborative research
between AI and energy experts.

近年来，大型语言模型（LLMs）在各个领域取得了快速进展和令人印象深刻的能力。本文探讨了 LLMs 在建筑能效和碳减排研究中的应用、影响和潜力。文章考察了 LLMs 在建筑能源领域的广泛能力，包括智能控制系统、代码生成、数据基础设施、知识提取和教育。尽管 LLMs 具有很大的潜力，但仍存在复杂和昂贵的计算、数据隐私、安全和版权、精细调整 LLMs 的复杂性以及自一致性等挑战。文章最后呼吁未来的研究集中在增强 LLMs 的领域特定任务、多模式 LLMs 和人工智能与能源专家之间的合作研究上。

应用大型语言模型于建筑能效和碳减排研究的机遇与挑战：一项探索性概述

Opportunities and Challenges of Applying Large Language Models in  Building Energy Efficiency and Decarbonization Studies: An Exploratory  Overview

Current methods to determine the energy efficiency of buildings require
on-site visits of certified energy auditors which makes the process slow,
costly, and geographically incomplete. To accelerate the identification of
promising retrofit targets on a large scale, we propose to estimate building
energy efficiency from widely available and remotely sensed data sources only,
namely street view, aerial view, footprint, and satellite-borne land surface
temperature (LST) data. After collecting data for almost 40,000 buildings in
the United Kingdom, we combine these data sources by training multiple
end-to-end deep learning models with the objective to classify buildings as
energy efficient (EU rating A-D) or inefficient (EU rating E-G). After
evaluating the trained models quantitatively as well as qualitatively, we
extend our analysis by studying the predictive power of each data source in an
ablation study. We find that the end-to-end deep learning model trained on all
four data sources achieves a macro-averaged F1 score of 64.64% and outperforms
the k-NN and SVM-based baseline models by 14.13 to 12.02 percentage points,
respectively. Thus, this work shows the potential and complementary nature of
remotely sensed data in predicting energy efficiency and opens up new
opportunities for future work to integrate additional data sources.

通过对广泛可用的遥感数据进行多源融合和深度学习建模，可以较快地并且准确地评估建筑的能源效率，本研究通过对英国近 40,000 座建筑的数据分析和建模，证明了通过街景、航拍图像、建筑平面和卫星表面温度等信息，深度学习建模的方法可以取得令人满意的预测结果，并为未来的进一步工作提供了可能性。