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AI model predicts building energy performance using multimodal data

作者 PulseAugur 编辑部 · [2 个来源] ·

Researchers have developed a gated multimodal model to predict energy performance scores for residential buildings, integrating tabular data, free text descriptions, and GIS spatial features. This approach aims to provide scalable assessments for decarbonizing buildings, which contribute significantly to UK and EU emissions. The model achieved strong predictive accuracy in a London case study and demonstrated that combining multiple data types enhances performance over single-modality approaches. AI

影响 Provides a scalable framework for property-level energy efficiency assessment and retrofit planning, supporting net-zero housing transitions.

排序理由 The cluster contains an academic paper detailing a new multimodal learning model for property energy performance prediction.

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

AI model predicts building energy performance using multimodal data

报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Yunfei Bai, Aaron Tesfa Tsion, Raul Rosales, Barbara Shollock, Wei He ·

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

    arXiv:2605.05088v1 Announce Type: new Abstract: 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. En…

  2. arXiv cs.LG TIER_1 English(EN) · Wei He ·

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

    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 reg…

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