# LuminLab: An AI-Powered Building Retrofit and Energy Modelling Platform

arXiv: https://arxiv.org/abs/2404.16057 · HTML: https://mlsanity.com/paper/2404.16057/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 10 references that are in the map.
- First posted on arXiv 14 Apr 2024.

## Builds on (most cited first, 3 of 3)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption](https://mlsanity.com/paper/2106.15147/) (2021)

## Built on by (most cited first, 1 of 1)

- [Can AI Make Energy Retrofit Decisions? An Evaluation of Large Language Models](https://mlsanity.com/paper/2509.06307/) (2025)

## Abstract (arXiv)

This paper describes the technical and conceptual development of the LuminLab platform, an online tool that integrates a purpose-fit human-centric AI chatbot and predictive energy model into a streamlined front-end that can rapidly produce and discuss building retrofit plans in natural language. The platform provides users with the ability to engage with a range of possible retrofit pathways tailored to their individual budget and building needs on-demand. Given the complicated and costly nature of building retrofit projects, which rely on a variety of stakeholder groups with differing goals and incentives, we feel that AI-powered tools such as this have the potential to pragmatically de-silo knowledge, improve communication, and empower individual homeowners to undertake incremental retrofit projects that might not happen otherwise.