用视觉语言模型实现低成本建筑能效评估预判。
Multimodal Modular Chain of Thoughts in Energy Performance Certificate Assessment
- 分阶段推理，通过结构化提示传递属性信息
- 在81个英国家庭数据上误差低于基线方法
- 适合数据少的能效评估场景，可快速部署
在缺乏可扩展能效证书（EPC）评估的地区，准确评估建筑能效仍具挑战。本文提出一种低成本框架，利用视觉语言模型从有限视觉信息中实现自动化EPC预评估。所提出的多模态模块化思维链（MMCoT）架构将EPC估算分解为中间推理阶段，并通过结构化提示显式传播推断属性。在英国81个住宅物业的多模态数据集上实验表明，MMCoT在EPC估算上显著优于仅指令提示的方法。基于准确率、召回率、平均绝对误差和混淆矩阵的分析显示，该方法有效捕捉了EPC评级的序数结构，多数错误发生在相邻等级之间。结果表明，模块化提示推理为数据稀缺环境下低成本EPC预评估提供了有前景的方向。
原文摘要 · Abstract (English)
Accurate evaluation of building energy performance remains challenging in regions where scalable Energy Performance Certificate (EPC) assessments are unavailable. This paper presents a cost-efficient framework that leverages Vision-Language models for automated EPC pre-assessment from limited visual information. The proposed Multimodal Modular Chain of Thoughts (MMCoT) architecture decomposes EPC estimation into intermediate reasoning stages and explicitly propagates inferred attributes across tasks using structured prompting. Experiments on a multimodal dataset of 81 residential properties in the United Kingdom show that MMCoT achieves statistically significant improvements over instruction-only prompting for EPC estimation. Analysis based on accuracy, recall, mean absolute error, and confusion matrices indicate that the proposed approach captures the ordinal structure of EPC ratings, with most errors occurring between adjacent classes. These results suggest that modular prompt-based reasoning offers a promising direction for low-cost EPC pre-assessment in data-scarce settings.
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