Data validation
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- Dominant language
- Python
- Stars
- 130
- Forks
- 59
- PR merge metrics
- No merged PRs in 30d
Description
The techno-economical parameters of the different technologies are the main drivers of the PyPSA modeling.
As such, they should be easy to validate by energy experts with e.g. experience in building energy projects.
I structured the data from the PNNL storage database so that it would be easy to see which technologies PyPSA may select. This gives the opportunity to limit the data validation effort to a subset of technologies.
The database structure makes it difficult to do this for all the data, considering the different technology classes (storage (electricity, heat, ...) , generation, transport/transmission, ... ). An approach would be to use more meta-data that allows easy visualization of essential parameters across all technologies.
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No files, tests, or entry points are identified. Start by reviewing the PNNL storage database structure and the different technology classes, then define the metadata and visualization approach needed to compare essential parameters and limit validation to selected technologies; done means energy experts can inspect and validate the relevant data across classes.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100