Catalyzing Informed Residential Energy Retrofit Decisions via Domain-Specific LLM

ArXi:2602.20181v2 Announce Type: replace-cross 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.