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[2602.20181] Closing the Expertise Gap in Residential Building Energy Retrofits: A Domain-Specific LLM for Informed Decision-Making
Summary
This article presents a domain-specific large language model (LLM) designed to assist homeowners in making informed decisions about residential energy retrofits, addressing the expertise gap in energy assessments.
Why It Matters
As residential energy efficiency becomes increasingly critical for reducing carbon emissions, this research highlights a novel approach to empower homeowners with accessible tools for making informed retrofit decisions. The model's high accuracy in recommendations could significantly enhance the decarbonization efforts in the housing sector.
Key Takeaways
- The LLM provides optimal retrofit recommendations based on basic dwelling characteristics.
- It demonstrates high accuracy in identifying optimal retrofits for CO2 reduction and cost-effectiveness.
- The model can operate effectively even with incomplete input data, enhancing its usability for homeowners.
Computer Science > Computers and Society arXiv:2602.20181 (cs) [Submitted on 19 Feb 2026] Title:Closing the Expertise Gap in Residential Building Energy Retrofits: A Domain-Specific LLM for Informed Decision-Making Authors:Lei Shu, Armin Yeganeh, Sinem Mollaoglu, Jiayu Zhou, Dong Zhao View a PDF of the paper titled Closing the Expertise Gap in Residential Building Energy Retrofits: A Domain-Specific LLM for Informed Decision-Making, by Lei Shu and 4 other authors View PDF Abstract:Residential energy retrofit decision-making is constrained by an expertise gap, as homeowners lack the technical literacy required for energy assessments. To address this challenge, this study develops a domain-specific large language model (LLM) that provides optimal retrofit recommendations using homeowner-accessible descriptions of basic dwelling characteristics. The model is fine-tuned on physics-based energy simulations and techno-economic calculations derived from 536,416 U.S. residential building prototypes across nine major retrofit categories. Using Low-Rank Adaptation (LoRA), the LLM maps dwelling characteristics to optimal retrofit selections and associated performance outcomes. Evaluation against physics-grounded baselines shows that the model identifies the optimal retrofit for CO2 reduction within its top three recommendations in 98.9% of cases and the shortest discounted payback period in 93.3% of cases. Fine-tuning yields an order-of-magnitude reduction in CO2 prediction error and...