Multimodal Modular Chain of Thoughts in Energy Performance Certificate Assessment

Zhen Peng; Peter J. Bentley

physics.soc-phFeb 20, 2026

Multimodal Modular Chain of Thoughts in Energy Performance Certificate Assessment

Authors: Zhen Peng, Peter J. Bentley

Organizations: Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, UK · Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK

Abstract

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.

Multimodal CoT Reasoning

Multimodal chain-of-thought reasoning studies how AI models combine visual, audio, and textual evidence through intermediate reasoning to solve complex tasks.

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Gated Multimodal Learning for Interpretable Property Energy Performance Prediction and Retrofit Scenario Analysis

Yunfei Bai, Aaron Tesfa Tsion, Raul Rosales, Barbara Shollock+1

Department of Engineering, King’s College London London, UK

Multimodal Model Interpretability Multimodal Fusion Multimodal Learning

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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.

Abstract

Multimodal CoT Reasoning

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