Part 5: Deployment, limitations, and what’s next
When does a project become a real project
As long as the calculation only runs locally, it’s just an experiment. The moment you can open it at a public URL, switch languages, go through the workflow, and download a report, it starts to become a real product.
That’s the case with this app. The computational logic matters, but it’s just as important that it’s deployed as a publicly accessible service.
What brings operational value
Today, the project stands on several practical building blocks:
- containerized deployment in Docker,
- separate Czech and English versions,
- persistent storage for local state and reports,
- HTML export of results,
- clear separation of UI, model, and reporting layer.
These are exactly the elements that determine whether the app can be further developed without rewriting it from scratch.
Transparency about limitations is part of quality
For a tool like this, it’s important not only what it can do, but also what it can’t do yet or only handles approximately.
With the current implementation, it’s good to be open about, for example:
- the output is indicative, not a certified PENB,
- the reference year in the MVP is an approximated typical year, not a full TMY dataset,
- result quality depends on the scope and consistency of input data,
- some parts of the result presentation still have room for further development.
This is not a weakness in communication. It’s its professionalization.
What I would develop in the next iteration
If the project were to continue to the next version, I believe these directions would make the most sense:
- more precise handling of the reference year and climate scenarios,
- expanding the interpretation of results with further recommendations,
- deeper work with visualizations and calibration explanations,
- more robust handling of a wider range of input situations.
These steps would not only advance the technical side of the model. They would also increase user trust in the output and the ability to use the tool in real decision-making.
Key takeaways from the whole series
The PENB approximation project clearly shows that a quality data application doesn’t arise from a single clever idea. It emerges from the interplay of several disciplines:
- choosing the right problem,
- a reasonable model,
- a quality data workflow,
- a usable interface,
- and deployment that allows the result to be truly used.
This combination, in my view, is more interesting than the mere fact that the application returns an energy class.