Closing the Expertise Gap in Residential Building Energy Retrofits: A Domain-Specific LLM for Informed Decision-Making

arXiv:2602.20181v1 Announce Type: new
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 multi-fold reductions for energy use and retrofit cost. The model maintains performance under incomplete input conditions, supporting informed residential decarbonization decisions.