A practical research project exploring AI-assisted UI development workflows. The focus is on methods for generating, iterating and evaluating UI components with AI tools, with a structured feedback loop that converts user observations into tracked issues. The issues form a prioritised backlog from which developers decide how to handle each item.
The UI under test is a building property configurator for the EnerPlanET platform. It prepares building inputs for the ignis and BUEM energy simulation services.
The configurator publishes as @thd-spatial-ai/building-configurator, a React
component that calls the EnerPlanET backend over a transport the host supplies.
See Installing the component.
npm install @thd-spatial-ai/building-configuratornpm install
npm run devnpm run dev serves the demo application: a map, a live Loenen fixture and the
configurator itself, against a local EnerPlanET backend. npm run build:lib
builds the package.
- Configure building geometry, envelope elements, and thermal parameters
- Set up roof and photovoltaic (PV) system properties
- Visualise the building energy envelope and surface composition
- View simulated heating and cooling load profiles
- Step-by-step configuration workflow with live building snapshot
See CONTRIBUTING.md for how to report bugs, request features, and submit pull requests.
Please read our Code of Conduct before participating.
This project is itself an experiment in AI-assisted development. The concept, design decisions, and development methodology are the author's own. AI tools were used for implementation: the initial UI prototype was generated with Figma Make, and the majority of the code was written using Claude Code (Anthropic Claude Sonnet 4.6). The feedback pipeline, which captures user observations and converts them into GitHub issues, is part of the methodology being developed and tested here. All AI-generated output was reviewed, directed, and adapted by the author throughout.
This project is being developed in the context of the research project RENvolveIT (https://projekte.ffg.at/projekt/5127011). This research was funded by CETPartnership, the Clean Energy Transition Partnership under the 2023 joint call for research proposals, co-funded by the European Commission (GA N°101069750) and with the funding organizations detailed on https://cetpartnership.eu/funding-agencies-and-call-modules.
