用AI把居民节能改造决策门槛降下来，只需描述房子基本情况就行
Catalyzing Informed Residential Energy Retrofit Decisions via Domain-Specific LLM
- 基于自然语言描述，用领域专用大模型自动评估节能改造方案
- 在9类改造中，碳减排最高方案命中率98.9%，投资回收期最短方案命中率93.3%
- 即使信息只填60%，仍能稳定输出可靠建议，适合普通住户使用
住宅节能改造常因业主缺乏专业知识而停滞，难以获取结构化能源评估信息。本研究构建了一个基于物理模拟和经济计算的领域专用大语言模型（LLM），仅需用户提供的自然语言描述（如建筑年代、面积、位置）即可生成科学决策建议。模型在536,416个美国住宅原型数据上，通过低秩自适应（LoRA）微调训练，评估了包括围护结构升级、暖通空调系统改造和可再生能源安装在内的九类主要改造措施。与物理基准对比显示，该模型在最大二氧化碳减排方案中的前3名命中率达98.9%，在最短贴现投资回收期方案中达93.3%。此外，在输入信息仅部分提供（60%完整）时仍保持性能稳定。该模型显著降低非专业用户的决策门槛，同时保障科学性，为社区和国家层面的规模化节能行动提供了可扩展的智能支持路径。
原文摘要 · Abstract (English)
Residential energy retrofit initiation is often stalled by an expertise gap, where homeowners lack the technical literacy required for structured building energy assessments and are thereby trapped in low-information environments with fragmented sources. To bridge this gap, this study reports a domain-specific large language model (LLM) designed to catalyze informed decision-making based solely on homeowner-accessible, natural-language descriptions, e.g., building age, size, and location. The model is created using the parameter-efficient low-rank adaption (LoRA) fine-tuning approach on a massive corpus grounded in physics-based energy simulations and techno-economic calculations from 536,416 U.S. residential building prototypes. Nine major retrofit categories are evaluated, including envelope upgrades, HVAC systems, and renewable energy installations. Validations against physics-grounded benchmarks show that the LLM consistently identifies high-quality retrofit options, achieving top-3 hit rates of 98.9% for maximum CO2 reduction and 93.3% for the shortest discounted payback year. Moreover, the model exhibits strong robustness under incomplete input conditions, maintaining stable performance even when basic dwelling descriptions are only 60% partially specified. By significantly lowering the information activation energy for non-expert users while maintaining the scientific rigor, this physics-based AI model offers a scalable pathway for parallelized, user-centered decision making, accelerating cumulative energy savings and emission reductions across community and national scales.
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