Catalyzing Informed Residential Energy Retrofit Decisions via Domain-Specific LLM

TL;DR

This study introduces a domain-specific large language model that aids homeowners in making informed residential energy retrofit decisions using natural language descriptions, validated against physics-based benchmarks.

Contribution

A novel physics-grounded LLM fine-tuned with LoRA on a large corpus enables non-experts to identify optimal retrofit options with high accuracy and robustness.

Findings

Achieves 98.9% top-3 hit rate for CO2 reduction.

Achieves 93.3% top-3 hit rate for shortest payback period.

Maintains performance with only 60% of input information.

Abstract

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.