发表机构
Graz University of Technology（格拉茨技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种考虑故障检测与诊断软件性能和能耗的模型，通过模拟比较多种架构，发现随机森林等高效方法在小建筑中净节省最大，而大型语言模型在大建筑中更具优势。
AI 中文摘要
全球约30%的能源消耗可归因于建筑行业，其中很大一部分能源消耗可以通过修复现有故障来避免。故障检测与诊断（FDD）软件解决了这一问题；然而，其创建和运行也会对环境产生影响。这种影响的程度受诊断架构的影响，因为不同的架构和方法具有不同的能源需求。然而，仅考虑软件本身消耗的能源不足以评估其整体环境影响，因为诊断性能（例如，检测到的故障数量或漏检的故障数量）也对其生态足迹有所贡献。在本文中，我们提出了一种能源消耗模型，该模型直接考虑了FDD性能和诊断软件所消耗的能源。在初步实验中，我们使用从先前文献中收集的性能和能源消耗值，在模拟中比较了几种FDD架构家族，即基于规则的、基于模型的、经典机器学习和基于大型语言模型的。结果表明，考虑FDD的计算能源和准确性可以改变不同方法的相对优势。计算效率高的机器学习方法（如随机森林）在较小的建筑中提供了最大的净节省，而资源密集型方法（如微调的大型语言模型）随着建筑规模的增大而变得有利。我们的研究结果表明，整体能源效率不仅取决于FDD软件的计算需求，还取决于其诊断性能和建筑规模。
英文摘要
Around 30\% of global energy expenditure can be attributed to the building sector, where a large portion of energy-consumption could be avoided by repairing existing faults. Fault detection and diagnosis (FDD) software addresses this issue; however, its creation and operation also have an environmental impact. The magnitude of this impact is influenced by the diagnosis architecture, as different architectures and methods have different energy demands. Yet, simply considering the energy consumed by the software itself is not sufficient to assess its overall environmental impact, since the diagnostic performance, e.g., number of detected faults or number of faults missed, also contributes to its ecological footprint. In this paper, we propose an energy-consumption model that considers FDD performance and energy spend directly by the diagnosis software. In an initial experiment, we compare several FDD architecture families, i.e., rule-based, model-based, classical machine learning, and large-language-model-based, in simulation using performance and energy-consumption values collected from prior literature. The results show that considering the computational energy and accuracy of FDD can change the relative benefit of the different approaches. Computationally efficient machine learning methods, such as random forest, provide the largest net savings on smaller buildings, whereas more resource-intensive approaches, such as fine-tuned large language models, become advantageous as building size increases. Our findings suggest that overall energy efficiency depends not only on the computational demand of the FDD software, but also on its diagnostic performance and the scale of the building.
