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AI researchers develop physics-informed transformer for universal building thermal models

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a physics-informed transformer architecture designed to create a universal thermal model for residential buildings. This model embeds domain knowledge and uses Rotary Position Embedding attention to capture temporal dependencies, aiming for generalization across diverse buildings and climates without specific calibration. Evaluated on the CityLearn dataset, the model demonstrated strong prediction accuracy and zero-shot transferability, outperforming existing baselines and foundation models. AI

IMPACT This research could lead to more efficient building energy management systems by enabling generalized thermal modeling.

RANK_REASON This is an academic paper presenting a new model architecture and evaluation results on a specific dataset. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI researchers develop physics-informed transformer for universal building thermal models

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This is an academic paper presenting a new model architecture and evaluation results on a specific dataset. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ting-Yu Dai, Kingsley Nweye, Dev Niyogi, Zoltan Nagy ·

    Toward a foundational thermal model for residential buildings

    arXiv:2605.01364v1 Announce Type: new Abstract: The building energy community lacks a foundational thermal model, i.e., a single pretrained model capable of generalizing across diverse buildings, climates, and control strategies without building-specific calibration. Achieving th…

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