• Computer Science > Computers and Society [Submitted on 19 Feb 2026] Title:Closing the Expertise Gap in Residential Building Energy Retrofits: A Domain-Specific LLM for Informed Decision-Making View PDFAbstract: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.

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  • Computer Science > Computers and Society [Submitted on 19 Feb 2026] Title:Closing the Expertise Gap in Residential Building Energy Retrofits: A Domain-Specific LLM for Informed Decision-Making View PDFAbstract: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-tun

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