融合文本与空间数据，精准预测建筑能效并辅助改造决策。
Gated Multimodal Learning for Interpretable Property Energy Performance Prediction and Retrofit Scenario Analysis
- 用门控机制融合表格、文本和地理信息，自动学习各模态重要性。
- 在伦敦威斯敏斯特区预测能效分和环境影响分，平均误差仅4.39分。
- 可解释性分析揭示关键影响因素，适合城市级建筑节能规划使用。
实现韧性可持续城市需规模化减碳住宅建筑，其占英国约20%温室气体排放及欧盟25%能源相关排放。能源性能证书（EPC）支撑监管与改造规划，但依赖现场勘查限制了城市尺度评估。本研究提出一种门控多模态模型，通过整合EPC表格变量、评估员文本描述及地理信息系统（GIS）衍生的空间特征（如占地面积、高度、朝向等），预测标准评估程序（SAP）能效分与环境影响（EI）分。样本级门控学习属性特定模态权重，辅助分类头稳定训练。在伦敦威斯敏斯特区案例中，模型对SAP和EI的预测均方误差分别为4.03和4.76，决定系数达0.757和0.748，平均误差4.39。消融实验表明，全模态融合优于单模态与双模态基线。可解释性分析显示：门控权重强调评估文本；SHAP指出主要燃料、建筑形式与建造年代为关键因素；文本遮蔽突出屋顶与墙体；空间归因受高度与面积主导，且对形状敏感。该框架进一步应用于墙体、屋顶保温及窗户升级的改造情景，预测能效提升、年能耗成本下降与碳排放减少。整体提供可扩展的建筑级证据，支持改造筛选、干预优先级设定与净零住房转型。
原文摘要 · Abstract (English)
Achieving resilient and sustainable cities requires scalable approaches to decarbonising residential buildings, which account for about 20% of UK greenhouse gas emissions and 25% of energy-related emissions in the European Union. Energy Performance Certificates (EPCs) support regulation and retrofit planning, but their reliance on on-site inspections limits timely city-scale assessment. This study introduces a gated multimodal model to predict Standard Assessment Procedure (SAP) energy efficiency and Environmental Impact (EI) scores by integrating EPC tabular variables, assessor-written free text, and Geographic Information System (GIS)-derived spatial features describing footprint geometry, height, area, and orientation. Sample-wise gating learns property-specific modality weights, while an auxiliary band classification head stabilises training. In a Westminster, London case study, the model predicts SAP and EI scores with MAEs of 4.03 and 4.76 points and R2 values of 0.757 and 0.748, respectively, achieving a mean MAE of 4.39. Ablation results show that full multimodal fusion outperforms unimodal and bimodal baselines for both score prediction and band-level classification. Interpretability analyses provide decision-relevant evidence: gating weights indicate strong reliance on assessor text; SHAP highlights main fuel, built form, and construction age band; text occlusion prioritises roof and wall fields; and spatial attribution is dominated by height and footprint area, with sensitivity to footprint shape. The validated framework is further applied to retrofit scenarios for wall insulation, roof insulation, and window glazing upgrades, indicating projected improvements in SAP, EI, annual energy cost, and equivalent CO2 emissions. Overall, the framework provides scalable property-level evidence for retrofit screening, intervention prioritisation, and net-zero housing transitions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。