LuminLab: An AI-Powered Building Retrofit and Energy Modelling Platform
April 14, 2024 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Kevin Credit, Qian Xiao, Jack Lehane, Juan Vazquez, Dan Liu, Leo De Figueiredo
arXiv ID
2404.16057
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.AI
Citations
3
Venue
arXiv.org
Last Checked
4 months ago
Abstract
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.
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